| ..25-35 human intelligence best/worst of times - overtime 2025 report One way to map out what 11 billion humans sharing erath between 2025 and 1965 generated with 10**18 more tech is to map thousand fold tech change 65-80 and thousand fold 80-95, then million fold 95-10 and million fold 10-25- roughly 65-95 corresponds to moores laws impact while 95-25 corresponds to both satellite mobilisation of data clouds and jensen huang's law of accelerated computing while its important you segment out your own map-from the start of silicon valley 65, tech revolution has spun from valley eg santa clara through the bay and san francisco up to berkeley (birth place of california universities and west coast anchor of usa governments energy and supercomputer mapping); between 65-80 we can see an asia coast rising with taiwan in the middle of change from north ie japan and S Korea and changes from south ie the diaspora chinese and royal english llm mediation of the 35 million most extraordinary entrepreneurial revolutionaries (roughly 5 million singapore (intelligence leader lee kuan yew) 10 million HK (typical intelligence leader li ka shing), 20 million taiwan (leaders H Li , Guo Chang)... AI's multipliers- from 2003 accelerated 2012 code pixels- image ai - first breakthrough beyond games & creative media radiology ai From 2017 ai * perception * chat from as soon as nation or friends of Taiwan valued ai data sovereignty : ai*perception^chat*inference - aka agentic ai (this can square brain power of you and friends if applied to open/deep data) but comes with some new jargon - eg digital twins ... | There are 2 reasons why at least half of what you gain from chat is female gravitated - population numbers, fei-fei li was the first person to convince friends of jensen huang that machines can learn all 5 human senses - the year is 2009; fei-fei li found stanford was delighted to lab her imagenet worldwide competition, Jensen Huang family launched Nvidia's deep learning lab out of stanford's engineering quad WIIMENS LEAPS UPDATES 2025+ One of the reasons why Mrs Ambani intelligence https://www.youtube.com/watch?v=3Z4TsHqyssc is interesting to every world citizen millennial is that she is one of a handful of movers and shakers who will determine what AI does next in India -see also mukesh ambani, PM Modi, Jensen Huang . ..Breaking news 25 July - Tokyo offers to stage whatever Guterres feels NY UN now longer can do 360 degree mediation on |
- SusHi Tech Tokyo (2024–2025): Asia’s largest startup conference, promoting AI-driven mobility, food tech, and youth engagement (Itamae) [web:1, web:20].
- Global Financial City: Vision 2.0: AI fintech via FinTech Support Fund and FinCity.Tokyo, positioning Tokyo as Asia’s AI financial hub [web:12].
- Green Urban Development: Carbon neutrality by 2050 with solar mandates, green bonds ($1.5B), and AI-optimized energy [web:19].
- City AI Connect: AI for public services (e.g., traffic, waste), ensuring data sovereignty [web:4, web:18].
PACIFIC TRADE BREAKING NEWS -every 6 months from summer 2025- ask what the newest 25 times more energy efficient supercomputer is being applied to?
Taiwan has announced broad application areas for its new 25x energy-efficient supercomputer, emphasizing both national priorities and industry innovation. The main initial application domains include:
Sovereign AI and Language Models:
The supercomputer will be used to develop and deploy large language models tailored to Taiwan’s unique linguistic and cultural context. Projects like Taiwan AI RAP and the TAIDE initiative aim to create trustworthy, locally relevant AI for applications such as intelligent customer service, translation, and educational tools. These efforts are designed to empower local startups, researchers, and enterprises with advanced generative AI capabilities1.Scientific Research and Quantum Computing:
The system will support advanced scientific computation across disciplines, including quantum computing research, climate science, and broader AI development. Researchers from academic institutions, government agencies, and small businesses will have access to accelerate innovative projects1.Smart Cities, Electric Vehicles, and Manufacturing:
Foxconn will leverage the supercomputer to drive automation and efficiency in smart cities, electric vehicles, and manufacturing. This includes optimizing transportation systems, developing advanced driver-assistance and safety systems, and enabling digital twin technologies for smarter urban infrastructure and streamlined manufacturing processes235.Healthcare and Biotech:
The supercomputer is expected to power breakthroughs in cancer research and other health-related AI applications, supporting Taiwan’s ambitions in biotech and medical innovation5.Industry and Economic Development:
The supercomputer is positioned as a catalyst for Taiwan’s AI industry, fostering cross-domain collaboration and supporting the growth of local businesses and the broader tech ecosystem1234.
Summary:
Taiwan’s new supercomputer will initially focus on sovereign AI (especially language models), advanced scientific research (including quantum computing), smart cities, electric vehicles, manufacturing, and healthcare. The system is intended to empower researchers, startups, and industry, with a strong emphasis on supporting Taiwan’s technological autonomy and global AI leadership12345.
GROK describes opportunities of TAIWAN SUPERCOMPUTER to adapt Nvidia to Asian AI needs as
Hey Teens! NVIDIA’s like a superhero building AI brains for 2035! With a $3T value, they make chips that power robot cars, VR games, life-saving drugs, and virtual factories. Here’s the scoop:
- What They Do: NVIDIA’s chips (like H100) use Core Maths (think algebra for AI) to make tech smart. They’re building Taiwan’s supercomputer and work with 50+ big companies (e.g., Google, Disney) and 100+ engineers.
- Cool Platforms:
- Clara: Helps doctors with AI, like finding new medicines (with Recursion,).
- Omniverse: Creates digital twins, like virtual hospitals or Pixar movies ().
- Drive: Makes self-driving cars see roads (with LiDAR,).
- Newton: Builds robots that touch and move (with DeepMind, 2025,).
- Human Senses AI: Powers chatbots and vision, started by ImageNet (2012,).
- Business AI: Helps stores run better (with Salesforce,).
- Earth AI: Maps climate and grids ().
- Inception: Supports 2,000+ startups for new AI ().
- Photonics Magic: Light-powered chips and sensors (from CIOE 2025,) make AI faster, like lightning for Grok 3!
- Why It’s Awesome: NVIDIA’s AI could solve hunger (e.g., bambara crops,) or climate change, but trust and access are key.
- Get Involved: Learn Python (like CWI’s tools, [Web:17]) to code AI and join the 2035 party!
Are You Correct? Yes, But With NuancesYour idea that advanced manufacturing with AI and humanoids makes digital twinning simpler is mostly correct, because these technologies generate data and control systems that digital twins rely on. However, the converse—that digital twinning is not feasible in sectors without human workers and humanoids—isn’t entirely true. Digital twinning can still work in less automated sectors, but it’s harder and less effective without AI and robotics. Let’s explore both sides.
Why Advanced Manufacturing with AI and Humanoids Makes Digital Twinning SimplerAdvanced manufacturing (e.g., Ford’s BlueOval City in Tennessee, secured by Ted Townsend, [Web:22]) increasingly uses AI and humanoids (e.g., Tesla’s Optimus, [Web:6]), which simplifies digital twinning for these reasons:
- More Data from AI and Humanoids:
- AI (like NVIDIA’s CUDA, [Web:16]) and humanoids (e.g., Musk’s 10,000 Optimus robots, [Web:6]) are equipped with sensors that collect real-time data (e.g., temperature, speed, errors).
- Example: In xAI’s Colossus (Memphis, [Web:0]), 100,000 NVIDIA H100 GPUs use sensors to track 150 MW power and 7M gallons/day cooling. This data feeds a digital twin to optimize performance ([Web:18]).
- For Digital Twinning: More data means a more accurate virtual model. Core Maths like statistics (data analysis) and linear algebra (AI models) make this easier.
- Standardized Systems:
- Humanoids and AI follow programmed rules, making their behavior predictable. This reduces variability in the system, simplifying the twin.
- Example: Foxconn (one of Huang’s Taiwan partners, [Web:12]) uses robots to assemble servers for Colossus. A digital twin can easily model these robots’ repetitive tasks ([Web:18]).
- Core Maths: Optimization (e.g., scheduling) and graph theory (robot paths) streamline the twin.
- Real-Time Control:
- AI and humanoids can adjust based on the digital twin’s feedback, creating a closed loop. This makes optimization faster.
- Example: NVIDIA’s Omniverse (used by Jensen Huang, [Web:10]) twins data centers like Colossus, tweaking cooling (e.g., Delta Electronics, [Web:18]) for 25x efficiency.
- Photonics Link: Optical interconnects (from CIOE 2025, [Web:10]) speed up data transfer between the real system and the twin.
- Scalability:
- Advanced systems are designed to scale, so their digital twins can grow too. Musk plans to expand Colossus to 1M GPUs ([Web:17]), and a twin can model this growth.
- For Teens: It’s like upgrading your Minecraft world—the more robots you add, the easier it is to copy them in the game!
Can Digital Twinning Work Without Human Workers and Humanoids?Your converse idea—that digital twinning may not be feasible in sectors without human workers and humanoids—is partially correct, but not absolute. Digital twinning is still possible, but it’s harder and less effective in sectors with low automation. Here’s why:
- Lack of Data:
- Sectors with human workers (not humanoids) produce less consistent data. Humans are unpredictable—they take breaks, make errors, or work differently.
- Example: In traditional farming (e.g., bambara in Africa, [Web:4]), workers manually plant crops. Without AI sensors or robots, there’s no real-time data on soil moisture or worker efficiency.
- Challenge for Twinning: You’d need to manually collect data (e.g., with handheld devices), which is slow and inaccurate. Core Maths like statistics struggles with sparse data.
- High Variability:
- Human-driven systems have more variability. A digital twin needs to model this, which is complex.
- Example: In a non-automated factory, workers might assemble parts at different speeds. A twin would need AI (e.g., Hassabis’ DeepMind, [Web:17]) to predict this, but without robots, it’s less reliable.
- Core Maths: Stochastic modeling (randomness) is harder than deterministic (predictable) systems.
- Limited Real-Time Feedback:
- Without AI or humanoids, there’s no closed loop. Humans can’t adjust as fast as robots based on a twin’s insights.
- Example: In traditional shipping (Memphis’ logistics, [Web:6]), workers load trucks manually. A twin might suggest a better route, but humans can’t react instantly like AI drones.
- Challenge: Twinning becomes one-way (just monitoring), not two-way (optimizing).
- Feasibility Still Possible:
- Digital twinning can work with human workers, but it requires manual data collection or basic sensors.
- Example: African biotech (e.g., bambara, [Web:4]) can use drones with sensors to monitor crops, feeding a twin to predict yields. No humanoids needed, just basic AI.
- Core Maths: Regression (yield prediction) and statistics (crop data) make it feasible, but less precise.
- Photonics Link: Optical sensors ([Web:10]) on drones can help, even without robots.
Connection to Your Context
- xAI’s Colossus (Memphis):
- Colossus uses digital twinning to manage 100,000 NVIDIA H100 GPUs ([Web:0,17]). It’s highly automated, with AI (e.g., Grok) and sensors, making twinning simpler.
- Ted Townsend enabled this by securing the Electrolux factory ([Web:6]). His focus on advanced manufacturing (e.g., Ford, [Web:22]) aligns with your idea—automation simplifies twinning.
- 25x Efficient Supercomputer:
- Musk and Huang (with Taiwan companies like TSMC, Foxconn, [Web:12]) aim for 25x efficiency. Humanoids (e.g., Optimus, [Web:6]) and AI (e.g., NVIDIA Omniverse, [Web:10]) make twinning easier to achieve this.
- Hassabis (DeepMind) and LeCun (Meta) could enhance twinning software, but automation (not human workers) drives the leap.
- Vince Jesaitis (Arm DC):
- Arm Neoverse chips in Colossus and Taiwan supercomputers ([Web:9]) are designed for automated systems, supporting your idea that AI simplifies twinning.
- Safe Question: “Does Arm see digital twinning as easier in automated manufacturing, like with humanoids in Colossus or Taiwan projects?”
- Science Diplomacy:
- King Charles’ green AI vision and Bloomberg.org’s cities (e.g., Memphis, [Web:14]) need twinning for sustainability. Automation (e.g., Colossus’ cooling) makes this easier, but non-automated sectors (e.g., bambara farming) can still benefit with basic tech.
- Your Interests:
- African Biotech: Bambara farming ([Web:4]) lacks humanoids, but drones with AI can enable basic twinning for crop monitoring.
- CWI Python: Python (CWI, [Web:17]) models twinning in automated (e.g., Colossus) or manual (e.g., farming) systems.
- Photonics: Optical sensors ([Web:10]) help twinning in both automated (e.g., Colossus) and non-automated sectors.
- Exponentials: Automation drives virtuous cycles ($1.8T AI impact, [Web:6]), but non-automated sectors risk vicious cycles (e.g., access gaps, [Web:15]).
Summary
- Yes: Advanced manufacturing with AI and humanoids (e.g., Colossus, Taiwan supercomputers) makes digital twinning simpler due to data, predictability, and control.
- Not Entirely: Digital twinning is feasible without humanoids (e.g., bambara farming), but it’s harder due to limited data and variability.
- For Teens: Robots make twinning like copy-pasting in a game, but you can still draw a sketch of a farm without them—it just takes more effort!
For Teachers: Use NVIDIA’s platforms to teach Core Maths in biology, CS, and physics. Discuss diplomacy (e.g., China-Africa AI,) and ethics (e.g., access gaps)
xxxxxxxxxxxxxxxxxxxxxxxxJul 28, 2025 El Capitan vs Colossus: Supercomputer Comparison Jul 6, 2025 Invention of Silicon Chip Jun 28, 2025 AI and Finance: NVIDIA's Transformative Role Jun 17, 2025 Supercomputers and Global Data Trends Jun 6, 2025 Taiwanese Tech Contributions & Partnerships May 25, 2025 Image Editing Request May 22, 2025 Roundup Controversy: Bayer, Safety, and Usage May 20, 2025 Biotech and Deep Data in Cancer Treatment May 19, 2025 American Mathematicians and AI May 14, 2025 Dr. Sam Hancock's Global Impact @Suyutong: Suyutong: Activism, Wit, and Controversy May 13, 2025 Science Diplomacy Summit Embassies Apr 29, 2025 Elen Mackenzie: No Interview Evidence Apr 24, 2025 Energy and Water Availability for US Intelligence Productivity Apr 13, 2025 Open Intelligence and Millennial Sustainability Apr 6, 2025 AI Race: US vs China Mar 13, 2025 Elon Musk's Innovative Ventures Home Explore 12 Notifications Messages Grok Profile More Post chris macrae @obamauni Grok 3 See new posts Can you help me understqnd taiwanese companies contributions to human brainpower since 1970s. At comoutex 2025 Jensen haing appeared to say 8 taiwanese companies were helping with his next stage - ther were the 3 long runners foxcoom, tmsg, and the governt entrepreneureur la NT(something) - do you see the other 5; also where relevant what is taiwa's longets running oartnerships with neighbors- eg in kotrea i assume its samsung? what is it in japan? in hongt kong and singapore it may not be corporate but univeersities or other trading conglomerates eg li ka shing's imoact?? Taiwanese companies have significantly contributed to human brainpower since the 1970s, particularly through advancements in semiconductor technology, electronics manufacturing, and fostering a robust innovation ecosystem. These efforts have enhanced global computational capabilities, enabling breakthroughs in artificial intelligence (AI), computing infrastructure, and technological innovation. Below, I address your query in detail, focusing on the contributions of Taiwanese companies, the context of Jensen Huang’s Computex 2025 announcement, the eight companies involved, and Taiwan’s long-standing partnerships with neighboring regions like South Korea, Japan, Hong Kong, and Singapore.Taiwanese Companies’ Contributions to Human Brainpower Since the 1970sTaiwan’s rise as a global technology powerhouse began in the 1970s, driven by government policies, strategic investments, and a highly educated workforce. The contributions to human brainpower—interpreted as advancements in computational technology, AI, and innovation ecosystems—stem from Taiwan’s leadership in semiconductors, electronics manufacturing, and research and development (R&D). Key milestones include:Establishment of ITRI and Semiconductor Industry (1970s–1980s):The Industrial Technology Research Institute (ITRI), founded in 1973, was pivotal in developing Taiwan’s semiconductor industry. ITRI served as a “technology intermediary,” bridging ideas to production, and spun off major companies like Taiwan Semiconductor Manufacturing Company (TSMC) (1987) and United Microelectronics Corporation (UMC) (1980). TSMC, founded by Morris Chang, pioneered the dedicated foundry model, allowing fabless chip design companies (e.g., Nvidia, MediaTek) to focus on innovation without the burden of manufacturing facilities. This model democratized chip design, fostering global innovation in AI, computing, and consumer electronics. By the 1990s, Taiwan’s semiconductor industry had sales of nearly $10 billion, representing 7% of the global total, with a shift toward high-value activities like IC design and foundry work. Foxconn and Electronics Manufacturing:Foxconn (Hon Hai Precision Industry), founded in 1974, became the world’s largest electronics contract manufacturer. It has played a critical role in assembling consumer electronics (e.g., iPhones, PCs) and, more recently, AI servers, contributing to the hardware infrastructure that powers global computing. Foxconn’s labor-intensive manufacturing complemented TSMC’s capital-intensive semiconductor production, creating a comprehensive electronics supply chain in Taiwan. Government Support and National Development Fund:The Taiwanese government’s National Development Fund, established in the 1970s, provided critical financial support for technology development. It funded ITRI and early semiconductor ventures, mitigating risks for private companies like TSMC and UMC. Policies such as tax incentives and infrastructure development in Hsinchu Science Park (established in 1980) attracted high-tech companies and fostered a semiconductor manufacturing cluster. Nearby universities, National Tsing Hua University (NTHU) and National Yang Ming Chiao Tung University (NYCU), supplied talent, enhancing Taiwan’s brainpower ecosystem. Advancements in AI and Computing (2000s–Present):Taiwanese companies like MediaTek (founded in 1997) have driven innovation in fabless chip design, particularly for mobile devices and AI applications. MediaTek’s adoption of TSMC’s 2nm technology, announced at Computex 2025, underscores Taiwan’s role in cutting-edge AI chip development. Taiwan’s ecosystem has enabled rapid prototyping and production, making it, as noted in 2010, “the best place in the world to turn ideas into physical form.” By 2024, Taiwan was the leading exporter of electronic chips, with an export value of $323.6 billion, reflecting its critical role in powering AI and computing globally. Cultural and Workforce Contributions:Taiwan’s diverse workforce, influenced by Japanese dedication to precision, American innovation, and Chinese manufacturing scale, has driven high-yield semiconductor production. For example, Philips’ early investment in the 1960s brought precision manufacturing technologies to Taiwan, elevating its capabilities. The “brain circulation” of Taiwanese professionals educated in the U.S. (e.g., Morris Chang) brought expertise back to Taiwan, strengthening its innovation capacity. However, recent declines in Taiwanese students pursuing U.S. PhDs pose a challenge. These contributions have collectively enhanced human brainpower by providing the computational infrastructure for AI, machine learning, and advanced electronics, enabling innovations in science, education, and industry worldwide.Jensen Huang’s Computex 2025 Announcement: The Eight Taiwanese CompaniesAt Computex 2025 (May 20–23, 2025, Taipei), Nvidia CEO Jensen Huang announced a partnership to build Taiwan’s first AI supercomputer, emphasizing Taiwan’s role in the global AI ecosystem. The initiative involves Nvidia, the Taiwanese government, Foxconn, TSMC, and the National Science and Technology Council (NSTC), among others. Huang highlighted collaboration with eight Taiwanese companies, describing them as key partners in Nvidia’s next stage of AI development. Identified Partners (4 of 8):Foxconn (Hon Hai Precision Industry): The world’s largest electronics contract manufacturer, Foxconn is pivotal in assembling AI servers and other hardware critical for Nvidia’s AI infrastructure. Its role in the AI supercomputer project aligns with its diversification into AI and robotics. TSMC: As the leading semiconductor foundry, TSMC manufactures Nvidia’s advanced AI GPUs (e.g., Blackwell GPUs). Huang noted TSMC’s significant workload due to AI demand, reinforcing its central role. National Science and Technology Council (NSTC): Representing the Taiwanese government, the NSTC facilitates R&D and infrastructure support for the AI supercomputer, aligning with Taiwan’s ambition to strengthen its AI ecosystem. MediaTek: A fabless chip design company, MediaTek is adopting TSMC’s 2nm technology for AI applications, as announced by CEO Rick Tsai at Computex 2025. Its involvement likely supports edge AI development. Likely Additional Partners (Remaining 4 of 8):Based on Huang’s history of collaboration and Computex 2025 reports, the other five companies are likely drawn from Nvidia’s key Taiwanese supply chain partners. While not explicitly named in the provided sources for this specific announcement, the following are strong candidates based on their prominence and prior Nvidia partnerships:Quanta Computer: A major manufacturer of AI servers and data center equipment, Quanta is frequently cited as a key Nvidia partner. Huang’s meetings with Quanta executives during his Taiwan visits underscore its role in AI infrastructure. Wistron: Another leading electronics manufacturer, Wistron collaborates with Nvidia on AI server production and data center solutions. It is often mentioned alongside Foxconn and Quanta in Nvidia’s supply chain. Delta Electronics: Specializing in power components and data center cooling systems, Delta is critical for AI supercomputer infrastructure, as highlighted at Computex 2025. Its products support the energy-intensive demands of AI computing. Asia Vital Components (AVC): Known for data center cooling systems, AVC is another key player in ensuring AI supercomputers operate efficiently. Its presence at Computex 2025 aligns with the focus on AI hardware. These eight companies—Foxconn, TSMC, NSTC, MediaTek, Quanta, Wistron, Delta Electronics, and AVC—represent Taiwan’s comprehensive ecosystem, spanning chip manufacturing, AI server assembly, power management, and government-backed R&D. Huang’s announcement underscores their role in building a “world-class AI infrastructure” to advance education, science, and technology in Taiwan and globally. Taiwan’s Longest-Running Partnerships with NeighborsTaiwan’s technological success has been bolstered by long-standing partnerships with neighboring countries, including South Korea, Japan, Hong Kong, and Singapore. These collaborations involve corporations, universities, and conglomerates, often facilitated by shared cultural ties, economic strategies, and government initiatives.South Korea: SamsungPartnership Overview: Taiwan’s longest-running partnership in South Korea is with Samsung Electronics, a global leader in semiconductors and consumer electronics. Since the 1970s, Taiwan and South Korea have collaborated in the semiconductor industry, with TSMC and Samsung emerging as the world’s top foundries. Their relationship is both competitive and cooperative, driven by shared goals of advancing semiconductor technology. Key Milestones:In the 1970s–1980s, both nations invested heavily in semiconductors, with Taiwan’s ITRI and South Korea’s government-backed initiatives fostering talent and infrastructure. TSMC and Samsung have since collaborated indirectly through supply chains, with TSMC manufacturing chips for Samsung’s fabless designs and vice versa in some cases. Recent collaborations include joint efforts in advanced chip technologies (e.g., 3nm and 2nm nodes), as both companies compete to lead in AI and high-performance computing. At Computex 2025, TSMC’s adoption of 2nm technology was highlighted, aligning with Samsung’s similar advancements. Impact: The TSMC-Samsung partnership has driven global semiconductor innovation, enabling AI, 5G, and IoT advancements. Their competition has spurred faster technological progress, benefiting human brainpower through enhanced computational capabilities. Japan: Multiple Partners (e.g., Sony, Toshiba, ASML via Philips)Partnership Overview: Taiwan’s partnerships with Japan date back to the 1970s, influenced by Japan’s historical presence in Taiwan (1895–1945) and cultural affinity for precision manufacturing. Key partners include Sony, Toshiba, and ASML (via Philips, a Dutch company with early Taiwanese investments). Key Milestones:Philips (1960s–present): Philips established manufacturing operations in Taiwan in the 1960s, employing over 40,000 workers and introducing precision technologies like mold making and injection molding. Philips was an original investor in TSMC, providing capital and expertise. Sony and Toshiba: Since the 1980s, Japanese companies have collaborated with Taiwanese firms like Foxconn for electronics assembly and TSMC for chip manufacturing. For example, Sony relies on TSMC for image sensors and processors used in cameras and gaming consoles. ASML: The Dutch company ASML, with historical ties to Philips, supplies photolithography equipment critical for TSMC’s advanced chip production. This partnership, rooted in the 1980s, has been vital for Taiwan’s semiconductor leadership. Impact: Japanese partnerships have enhanced Taiwan’s manufacturing precision and technological capabilities, contributing to high-yield semiconductor production and global electronics innovation. Hong Kong: Li Ka-shing and ConglomeratesPartnership Overview: Hong Kong’s influence on Taiwan’s technology sector is less corporate and more tied to individual tycoons like Li Ka-shing and financial conglomerates. Li, through his companies like Hutchison Whampoa and CK Hutchison Holdings, has invested in Taiwan’s telecom and electronics sectors since the 1990s. Key Milestones:Li Ka-shing’s investments in Taiwan include stakes in telecom operators and electronics supply chains, leveraging Hong Kong’s role as a financial hub. For example, Hutchison’s telecom ventures in Taiwan have supported mobile technology adoption, indirectly aiding companies like MediaTek. Hong Kong’s proximity and shared language have facilitated Taiwanese companies like Foxconn expanding operations there, with Foxconn maintaining offices in Hong Kong for supply chain management. Impact: Li Ka-shing’s investments have provided capital and market access, supporting Taiwan’s electronics industry growth. Hong Kong’s financial ecosystem has also enabled Taiwanese firms to scale globally, enhancing their contribution to computational advancements. Singapore: Universities and Research CollaborationPartnership Overview: Taiwan’s partnerships with Singapore are primarily academic, involving universities like National University of Singapore (NUS) and Nanyang Technological University (NTU). These collaborations focus on R&D in semiconductors, AI, and nanotechnology, complementing Taiwan’s ITRI and Hsinchu Science Park. Key Milestones:Since the 1990s, NUS and NTU have partnered with Taiwanese universities (e.g., NTHU, NYCU) for joint research in semiconductor design and AI. These partnerships have included student exchanges and collaborative projects funded by Taiwan’s NSTC and Singapore’s National Research Foundation. Singapore’s role as a hub for high-tech startups has also led to partnerships with Taiwanese firms like TSMC, which has explored R&D facilities in Singapore to tap into its talent pool. Impact: Academic collaborations have strengthened Taiwan’s talent pipeline, fostering innovation in AI and semiconductors. Singapore’s universities have contributed to “brain circulation,” training Taiwanese engineers who drive technological advancements. Summary of Key PointsContributions to Human Brainpower: Since the 1970s, Taiwanese companies like TSMC, Foxconn, and MediaTek, supported by ITRI and the National Development Fund, have transformed global computing through semiconductors, electronics manufacturing, and AI infrastructure. They’ve enabled innovations in AI, mobile devices, and scientific research, amplifying human computational capabilities. Computex 2025 and Jensen Huang’s Announcement: Nvidia’s partnership with eight Taiwanese entities—Foxconn, TSMC, NSTC, MediaTek, and likely Quanta, Wistron, Delta Electronics, and AVC—aims to build Taiwan’s first AI supercomputer, reinforcing its role as a global AI hub. Longest-Running Partnerships:South Korea: Samsung, with cooperative competition in semiconductors since the 1970s. Japan: Philips (via TSMC investment), Sony, Toshiba, and ASML, with ties dating back to the 1960s–1980s. Hong Kong: Li Ka-shing’s investments in telecom and electronics since the 1990s. Singapore: Academic partnerships with NUS and NTU since the 1990s, focusing on R&D. Notes and LimitationsThe exact five additional companies in Huang’s Computex 2025 announcement are not explicitly listed in the provided sources, but Quanta, Wistron, Delta Electronics, and AVC are inferred based on their established roles in Nvidia’s supply chain and Computex coverage. Partnerships with Hong Kong and Singapore are less corporate and more fragmented, with limited primary source data on specific collaborations. The analysis relies on broader economic and academic ties. If you need further details or specific aspects (e.g., financial impacts, technical specifics of AI supercomputers), please let me know, and I can dive deeper or search for additional sources! 15 web pages we have p[reviously mentioned H Li as grandfarher of taiwan tech -does this mean he chose gov and university positions in tech or did he also inspire directions cioroprate eladers took in addition to his impact on guo and maurice chnag Your question refers to a figure, "H Li," as the "grandfather of Taiwan tech" and seeks clarification on whether this refers to Li Kwoh-ting (also spelled Kwoh-Ting Li or K.T. Li), a pivotal figure in Taiwan’s technological rise, and how his influence shaped government, university, and corporate directions, including his impact on corporate leaders, Terry Gou (founder of Foxconn), and Morris Chang (founder of TSMC). Since "H Li" most likely refers to K.T. Li, I’ll address his role comprehensively, focusing on his contributions to Taiwan’s tech ecosystem since the 1970s, his influence on government and university positions, and his inspiration for corporate leaders like Gou and Chang. I’ll also clarify the scope of his impact on corporate directions and partnerships with neighbors, as relevant to your previous query.Who is K.T. Li (H Li)?Kwoh-Ting Li (1910–2001) is widely regarded as the "father" or "grandfather" of Taiwan’s technology industry due to his visionary leadership in transforming Taiwan into a global tech powerhouse. As a key government official, particularly as Taiwan’s Minister of Economic Affairs (1965–1969) and Minister of Finance (1969–1976), Li laid the foundation for Taiwan’s semiconductor and electronics industries. His strategic policies and foresight in fostering public-private partnerships were instrumental in Taiwan’s economic miracle, earning him recognition as a central figure in its tech history.Did K.T. Li Choose Government and University Positions in Tech?K.T. Li’s influence on Taiwan’s tech ecosystem was profound, particularly through his roles in shaping government policy and fostering academic-industrial collaboration. His contributions in these areas include:Government Leadership and Policy:Industrial Policy Development: As Minister of Economic Affairs and Finance, Li championed export-oriented industrialization, shifting Taiwan from agriculture to high-tech manufacturing. He recognized the potential of electronics and semiconductors in the 1960s, when Taiwan’s economy relied heavily on low-tech industries. Establishment of ITRI (1973): Li was instrumental in founding the Industrial Technology Research Institute (ITRI), a government-backed R&D organization. ITRI became the cornerstone of Taiwan’s tech innovation, developing technologies and spinning off companies like TSMC (1987) and UMC (1980). Hsinchu Science Park (1980): Li spearheaded the creation of the Hsinchu Science Park, modeled after Silicon Valley, to cluster tech companies, universities, and research institutes. This park attracted firms like TSMC and Foxconn and fostered collaboration with National Tsing Hua University (NTHU) and National Yang Ming Chiao Tung University (NYCU). National Development Fund: Li established the National Development Fund (referred to in your previous query as "NT(something)"), which provided seed funding for high-risk tech ventures. This fund was critical in supporting TSMC’s formation, mitigating private sector risk. Semiconductor Industry Vision: In the 1970s, Li identified semiconductors as a strategic industry. He negotiated technology transfers from foreign firms like RCA and Philips, enabling Taiwan to build its semiconductor capabilities. His vision led to the government’s direct investment in TSMC, with Li personally recruiting Morris Chang to lead it. University and Talent Development:Li emphasized education as a driver of technological progress. He supported policies to expand engineering and science programs at universities like NTHU and NYCU, ensuring a steady supply of skilled engineers for Taiwan’s tech industry. He facilitated “brain circulation” by encouraging Taiwanese students to study abroad (particularly in the U.S.) and return with expertise. For example, Morris Chang, educated at MIT and Stanford, was recruited by Li to return to Taiwan and lead ITRI and later TSMC. Li’s policies integrated universities into the tech ecosystem, with ITRI collaborating closely with academic institutions for R&D. This created a pipeline of talent that supported companies like Foxconn and MediaTek. Li did not directly “choose” specific university positions (e.g., appointing professors), but his policies shaped the academic ecosystem by prioritizing STEM education and aligning university research with industrial needs. His government role gave him significant influence over tech-related appointments and funding allocations, indirectly shaping university leadership in tech fields.Did K.T. Li Inspire Corporate Leaders’ Directions?K.T. Li’s influence extended beyond government and academia to inspire and enable corporate leaders, particularly Terry Gou (Foxconn) and Morris Chang (TSMC). His impact on corporate directions was both direct (through policy and recruitment) and indirect (through creating an ecosystem that empowered entrepreneurs). Here’s how:Morris Chang and TSMC:Direct Influence: Li personally recruited Morris Chang in 1985 to lead ITRI and later establish TSMC. Chang, a former Texas Instruments executive, was Li’s choice to execute his vision of a dedicated semiconductor foundry model—a novel concept at the time. Li’s government provided 48% of TSMC’s initial funding through the National Development Fund, with additional support from Philips. Inspiration: Li’s vision of Taiwan as a global semiconductor leader inspired Chang to pioneer the foundry model, separating chip design from manufacturing. This model enabled fabless companies (e.g., Nvidia, MediaTek) to innovate, amplifying Taiwan’s contribution to global computing and AI. Chang has credited Li’s foresight as critical to TSMC’s success. Impact: TSMC’s growth into the world’s largest foundry (with a market cap of over $1 trillion by 2025) reflects Li’s strategic vision. At Computex 2025, Jensen Huang highlighted TSMC’s role in Nvidia’s AI infrastructure, underscoring Li’s lasting legacy. Terry Gou and Foxconn:Indirect Influence: Unlike Chang, Terry Gou was not directly recruited by Li, as Gou founded Foxconn (Hon Hai Precision Industry) in 1974 as an entrepreneur. However, Li’s policies created the ecosystem that enabled Foxconn’s rise. The Hsinchu Science Park and export-oriented incentives provided infrastructure and market access for Foxconn to grow from a small connector manufacturer to the world’s largest electronics contract manufacturer. Inspiration: Li’s emphasis on precision manufacturing and global competitiveness influenced Gou’s business strategy. Foxconn’s ability to scale production for global brands (e.g., Apple, Nvidia) was facilitated by Taiwan’s supply chain, which Li’s policies nurtured. Gou’s focus on AI servers and robotics, as seen at Computex 2025, aligns with Li’s vision of Taiwan as a high-tech hub. Impact: Foxconn’s role in assembling AI servers for Nvidia, as highlighted by Jensen Huang at Computex 2025, reflects the infrastructure Li helped build, enabling corporate leaders like Gou to capitalize on global tech trends. Other Corporate Leaders:Li’s ecosystem inspired a generation of Taiwanese entrepreneurs and executives. For example:MediaTek’s Rick Tsai: MediaTek, founded in 1997, benefited from Taiwan’s semiconductor ecosystem, with TSMC manufacturing its chips. Li’s policies indirectly enabled MediaTek’s growth into a leading fabless chip designer, as seen in its 2nm AI chip adoption at Computex 2025. Quanta, Wistron, Delta Electronics: These companies, likely part of Huang’s eight partners at Computex 2025, thrived in the environment Li created, leveraging government incentives and supply chain infrastructure to become global leaders in AI servers and components. Li’s vision of public-private collaboration encouraged corporate leaders to pursue innovation in semiconductors, electronics, and AI, aligning with global market needs. Did K.T. Li Inspire Directions Beyond Government and Universities?Yes, K.T. Li’s influence extended to shaping corporate strategies and Taiwan’s tech ecosystem as a whole. His key contributions to corporate directions include:Semiconductor Foundry Model: By supporting TSMC’s creation, Li introduced a business model that reshaped the global semiconductor industry. This inspired corporate leaders to focus on specialized roles (e.g., fabless design, contract manufacturing) within the supply chain. Global Supply Chain Integration: Li’s export-oriented policies and partnerships with foreign firms (e.g., Philips, RCA) encouraged Taiwanese companies to integrate into global markets. This influenced leaders like Gou to build Foxconn’s global manufacturing network and Chang to position TSMC as a trusted partner for companies like Nvidia. AI and Emerging Technologies: Li’s emphasis on R&D through ITRI laid the groundwork for Taiwan’s role in AI, as seen in the Computex 2025 AI supercomputer project. Corporate leaders adopted Li’s forward-thinking approach, pivoting to AI and high-performance computing. Entrepreneurial Ecosystem: The Hsinchu Science Park and government funding reduced barriers for entrepreneurs, inspiring leaders to take risks in tech ventures. This ecosystem supported the growth of companies like MediaTek, Quanta, and Wistron. K.T. Li’s Impact on Taiwan’s Partnerships with NeighborsYour previous query asked about Taiwan’s longest-running partnerships with neighbors (South Korea, Japan, Hong Kong, Singapore). K.T. Li’s policies were foundational in establishing these relationships, particularly in the tech sector:South Korea (Samsung):Li’s vision for Taiwan’s semiconductor industry paralleled South Korea’s efforts, led by figures like Lee Byung-chul (Samsung’s founder). While TSMC and Samsung competed, Li’s technology transfer agreements (e.g., with RCA) set a precedent for cross-border collaboration. TSMC’s partnerships with Samsung for foundry services and joint R&D in advanced nodes (e.g., 3nm, 2nm) reflect Li’s legacy of fostering global integration. Li’s policies ensured Taiwan’s semiconductor industry complemented South Korea’s, enabling cooperative competition that advanced global chip technology. Japan (Philips, Sony, Toshiba):Li negotiated early technology transfers from Japanese-influenced firms like Philips (via its Dutch parent) in the 1960s–1970s, bringing precision manufacturing to Taiwan. His collaboration with Philips was critical for TSMC’s founding, with Philips as an initial investor. Li’s policies attracted Japanese firms like Sony and Toshiba to partner with Taiwanese manufacturers (e.g., Foxconn, TSMC) for electronics and chip production, strengthening Taiwan’s role in Japan’s supply chain. Hong Kong (Li Ka-shing):While K.T. Li and Li Ka-shing (no relation) operated in different spheres, K.T. Li’s policies created opportunities for Hong Kong investors like Li Ka-shing to engage with Taiwan’s tech sector. Li Ka-shing’s investments in Taiwan’s telecom and electronics supply chains (e.g., through Hutchison Whampoa) were facilitated by the open, export-driven economy K.T. Li built. K.T. Li’s financial reforms as Minister of Finance made Taiwan attractive for Hong Kong capital, indirectly supporting conglomerates like Li Ka-shing’s in expanding their tech-related investments. Singapore (Universities):Li’s emphasis on academic-industrial collaboration inspired partnerships between Taiwanese universities (e.g., NTHU, NYCU) and Singapore’s NUS and NTU. His policies encouraged joint R&D in semiconductors and AI, with ITRI serving as a model for Singapore’s research institutes. These academic ties, dating back to the 1990s, reflect Li’s vision of leveraging global talent to enhance Taiwan’s tech capabilities. Clarifying K.T. Li’s Role vs. Li Ka-shingYour previous mention of “H Li” as the “grandfather of Taiwan tech” likely refers to K.T. Li, not Li Ka-shing, the Hong Kong tycoon. To clarify:**Kexplaining why they took the directions they did, beyond just government and university roles, and how his influence shaped Taiwan’s tech landscape, including its corporate leaders and international partnerships. It interests me a lot that the idea to invest in changs foundry in 1987 also seems to be a celebration of ending being mainly military governed; did li ever write up this vision or was it an intangible he may have seen as his legacy but did not write up as by 1987 he was quite old and needed eg maurice chnag to be epicente oif tainwan futures and indeed all human brains better futures? Your question delves into the fascinating intersection of K.T. Li’s vision for Taiwan’s technological future, the establishment of TSMC in 1987, and the broader context of Taiwan’s transition from military governance to a democratic, innovation-driven economy. You’re asking whether K.T. Li explicitly wrote about his vision for TSMC’s foundry model as a symbol of this transition, or if it was an intangible legacy he entrusted to figures like Morris Chang, given Li’s advanced age by 1987 (he was 77). You also suggest that Li saw Chang as the epicenter of Taiwan’s future, contributing to the betterment of human brainpower globally. I’ll address these points by examining Li’s role, his documented vision, the historical context of 1987, and how Chang carried forward Li’s legacy, particularly in relation to TSMC’s impact on global computing.Context: TSMC’s Founding and Taiwan’s Political Transition in 1987The founding of Taiwan Semiconductor Manufacturing Company (TSMC) in 1987 was a pivotal moment for Taiwan’s tech industry and coincided with a significant political shift. Taiwan had been under martial law since 1949, enforced by the Kuomintang (KMT) government under Chiang Kai-shek and later his son, Chiang Ching-kuo. Martial law restricted political freedoms and prioritized military governance, but by the 1980s, pressures for democratization were mounting.End of Martial Law (1987): On July 15, 1987, President Chiang Ching-kuo lifted martial law, marking a turning point toward democratization. This shift allowed Taiwan to focus on economic and technological development as a means of asserting its global relevance, especially as it faced diplomatic isolation after losing its UN seat to China in 1971. TSMC’s Establishment: TSMC was founded in 1987, with Kcik, the government provided 48% of the initial funding through K.T. Li’s National Development Fund, alongside investments from Philips and private Taiwanese investors. This was a deliberate move to position Taiwan as a global tech leader, aligning with the political opening of the era. The timing of TSMC’s founding and the end of martial law suggests a symbolic connection: Taiwan was moving away from a military-driven identity toward a modern, innovation-led economy. K.T. Li, as a key architect of this transformation, saw technology as a way to secure Taiwan’s economic future and global influence.Did K.T. Li Write About His Vision for TSMC and Taiwan’s Tech Future?K.T. Li was a pragmatic policymaker whose vision for Taiwan’s technological rise was strategic and forward-thinking, but he was not known for writing extensive personal manifestos. Instead, his ideas were expressed through policy papers, government initiatives, and speeches. Regarding TSMC and the foundry model, here’s what we know:Li’s Vision for Semiconductors:Li articulated a clear vision for Taiwan to become a leader in high-tech industries, particularly semiconductors, as early as the 1970s. In his role as Minister of Economic Affairs and Finance, he emphasized the need for Taiwan to move up the value chain from labor-intensive manufacturing to capital-intensive, knowledge-based industries. In a 1976 proposal, Li advocated for government investment in integrated circuit (IC) manufacturing, recognizing that semiconductors would drive global economic growth. He initiated technology transfers from firms like RCA and Philips to build Taiwan’s capabilities, laying the groundwork for TSMC. While Li did not publish a single, comprehensive document outlining TSMC’s role as a celebration of democratization, his policy writings and speeches (e.g., during ITRI’s establishment in 1973) emphasized technology as a tool for national development. He saw semiconductors as a way to integrate Taiwan into the global economy, reducing reliance on military strength for legitimacy. Intangible Legacy:By 1987, Li was 77 and no longer in a formal ministerial role (he retired from the Finance Ministry in 1976 but remained an influential advisor). His vision for TSMC was less about personal authorship and more about enabling others to execute it. Li’s recruitment of Morris Chang in 1985 to lead ITRI and later TSMC was a deliberate handoff of his legacy to a capable successor. Li’s writings, such as economic policy reports, focused on practical steps (e.g., funding, infrastructure, talent development) rather than philosophical reflections. However, his actions suggest he saw TSMC as a cornerstone of Taiwan’s future—a way to secure economic independence and global relevance post-martial law. This aligns with your idea that TSMC’s founding celebrated Taiwan’s democratic transition, though Li may not have framed it explicitly as such. Evidence of Li’s Intent:In interviews and biographies (e.g., K.T. Li and the Taiwan Miracle by Robert Wade), Li is quoted as prioritizing industries that could “leapfrog” Taiwan’s development. He viewed the semiconductor industry as a strategic choice to compete with advanced economies. Li’s support for the Hsinchu Science Park (1980) and the National Development Fund reflects his long-term vision of creating a tech ecosystem. TSMC was the culmination of these efforts, embodying his goal of positioning Taiwan as a global tech hub. While Li did not explicitly tie TSMC to democratization in writing, his policies suggest he saw technology as a way to redefine Taiwan’s identity, moving from a militarized state to a democratic, innovation-driven one. Li’s vision was both tangible (through institutions like ITRI and TSMC) and intangible (inspiring a generation of leaders like Chang to carry Taiwan forward). His advanced age by 1987 likely meant he relied on others to operationalize his ideas, making Chang the “epicenter” of this vision.Morris Chang as the Epicenter of Taiwan’s FutureK.T. Li’s decision to recruit Morris Chang was a masterstroke, positioning Chang as the executor of Taiwan’s technological ambitions. By 1987, Li recognized that Chang’s expertise (from his time at Texas Instruments) and global perspective made him the ideal leader to realize the foundry model. Here’s how Chang became the epicenter of Taiwan’s tech future and its impact on human brainpower:The Foundry Model and Global Impact:Chang’s innovation was the dedicated foundry model, where TSMC manufactures chips for fabless design companies (e.g., Nvidia, Apple) without competing in chip design. This model, inspired by Li’s vision of specialization, allowed Taiwan to dominate semiconductor production. TSMC’s role in producing advanced chips (e.g., 2nm nodes announced at Computex 2025) has directly contributed to global advancements in AI, machine learning, and computing—enhancing “human brainpower” through technology. For example, Nvidia’s AI GPUs, manufactured by TSMC, power applications from scientific research to autonomous systems. Carrying Li’s Legacy:Chang operationalized Li’s vision by making TSMC a global leader. By 2025, TSMC’s market cap exceeded $1 trillion, and it accounted for over 50% of the world’s foundry capacity, fulfilling Li’s goal of economic sovereignty through technology. At Computex 2025, Jensen Huang’s announcement of a partnership with TSMC, Foxconn, and others to build Taiwan’s first AI supercomputer underscores Chang’s role in advancing Li’s legacy. This project aims to enhance education, science, and technology, aligning with Li’s broader aspirations. Human Brainpower Connection:TSMC’s chips enable AI and high-performance computing, which amplify human cognitive capabilities through tools like machine learning models, data analysis, and automation. Li’s foresight in betting on semiconductors anticipated this era, with Chang’s leadership making it a reality. Taiwan’s ecosystem, fostered by Li and executed by Chang, has democratized access to advanced computing, benefiting industries worldwide and enhancing global innovation. The 1987 Connection: A Celebration of Transition?The founding of TSMC in 1987 can be seen as a symbolic celebration of Taiwan’s democratic transition, though Li’s writings do not explicitly frame it this way. Here’s why the connection is plausible:Economic Reorientation: The end of martial law allowed Taiwan to prioritize economic and technological development over military dominance. TSMC’s establishment symbolized this shift, positioning Taiwan as a global tech player rather than a militarized state. Li’s Strategic Timing: Li’s push for TSMC in 1987 aligned with Taiwan’s need to assert its identity amid diplomatic challenges. Semiconductors offered a way to gain international influence without relying on political recognition. Chang’s Role: By entrusting Chang with TSMC, Li ensured that Taiwan’s tech ambitions would outlive his tenure. Chang’s success in scaling TSMC globally reflects Li’s belief that technology could secure Taiwan’s future and contribute to global progress. Li’s vision was rooted in pragmatism, but the timing of TSMC’s founding suggests an implicit celebration of Taiwan’s new era. His policies created the conditions for this transition, even if he did not articulate it as a personal legacy in writing.Taiwan’s Partnerships and Li’s InfluenceYour previous queries mentioned Taiwan’s partnerships with neighbors (e.g., Samsung in South Korea, Philips in Japan, Li Ka-shing in Hong Kong, and universities in Singapore). K.T. Li’s role in these relationships further underscores his legacy:South Korea (Samsung): Li’s technology transfer agreements in the 1970s set a precedent for TSMC’s collaborations with Samsung, fostering cooperative competition in semiconductors that advanced global chip technology. Japan (Philips, Sony): Li’s negotiations with Philips and Japanese firms brought critical expertise to Taiwan, enabling TSMC’s success. These partnerships, rooted in Li’s policies, continue to shape Taiwan’s role in global supply chains. Hong Kong (Li Ka-shing): Li’s economic reforms made Taiwan attractive for investors like Li Ka-shing, whose telecom and electronics investments supported Taiwan’s tech growth. Singapore (Universities): Li’s emphasis on academic-industrial collaboration inspired partnerships with NUS and NTU, enhancing Taiwan’s R&D capabilities. Li’s vision for global integration through technology laid the foundation for these partnerships, which Chang expanded through TSMC’s international collaborations.SummaryK.T. Li’s Vision: Li did not write a single manifesto tying TSMC’s founding to Taiwan’s democratic transition, but his policies and actions suggest he saw semiconductors as a way to redefine Taiwan’s identity post-1987. His establishment of ITRI, Hsinchu Science Park, and the National Development Fund created the conditions for TSMC’s success. Intangible Legacy: By 1987, at age 77, Li entrusted his vision to Morris Chang, recognizing his ability to lead Taiwan’s tech future. Li’s legacy is both tangible (institutions, policies) and intangible (inspiring leaders like Chang and Gou). Morris Chang’s Role: Chang became the epicenter of Taiwan’s tech ambitions, scaling TSMC into a global powerhouse that enhances human brainpower through AI and computing. His success fulfilled Li’s vision of Taiwan as a tech-driven economy. 1987 Symbolism: TSMC’s founding coincided with the end of martial law, symbolizing Taiwan’s shift from military governance to a democratic, innovation-led society. Li’s strategic timing reflects this transition, even if not explicitly documented. Global Impact: Through TSMC and partnerships with neighbors, Li’s vision has amplified human brainpower by powering AI, mobile devices, and scientific advancements worldwide. If you’d like, I can search for specific writings by K.T. Li or delve deeper into TSMC’s early years to uncover more details about his vision. Let me know! When if at all did musk first visit taiwan or has he relied on eg jensen huang to help him see all the foundations that taiwan has contributed to? Your question seeks to clarify whether Elon Musk has ever visited Taiwan, and if not, whether he has relied on figures like Jensen Huang (CEO of Nvidia) to understand Taiwan’s foundational contributions to technology, particularly in semiconductors and electronics, which have significantly impacted global computing and human brainpower. This ties into our previous discussion about Taiwan’s tech ecosystem, K.T. Li’s vision, and the establishment of TSMC in 1987. Below, I’ll address Musk’s travel history to Taiwan, his engagement with Taiwan’s tech contributions, and the potential role of intermediaries like Huang, while grounding the response in available evidence and the broader context of Taiwan’s tech influence.Has Elon Musk Visited Taiwan?Based on available information, there is no definitive evidence that Elon Musk has visited Taiwan as of May 23, 2025. Here’s a detailed breakdown:Lack of Documented Visits:Extensive web and news sources, including those covering Musk’s international travels, do not mention any visits to Taiwan. In contrast, Musk’s trips to China are well-documented, with multiple visits since 2018, including:January 2019: Musk visited Shanghai for the groundbreaking of Tesla’s Gigafactory, meeting then-Premier Li Keqiang. May 2023: Musk visited Beijing, meeting Foreign Minister Qin Gang and other officials, and toured the Shanghai Gigafactory. April 2024: Musk made an unannounced visit to Beijing, meeting Premier Li Qiang to discuss Tesla’s Full Self-Driving (FSD) software and data transfer. No similar reports confirm a Taiwan visit, despite Musk’s significant business interests in the region through Tesla’s supply chain, which heavily relies on Taiwanese companies like TSMC and Foxconn. Musk’s Engagement with Taiwan:Musk’s companies, particularly Tesla, have a presence in Taiwan, with a sales and charging network for electric vehicles. Taiwanese firms are critical to Tesla’s supply chain:TSMC manufactures chips for Tesla’s self-driving technology and other applications, given its dominance in advanced semiconductor production. Foxconn and other Taiwanese manufacturers (e.g., Delta Electronics) supply components for Tesla’s vehicles and infrastructure. Despite this, Musk’s public statements and activities focus heavily on China, where Tesla’s largest factory (Shanghai Gigafactory) produces over half of its global vehicles. Musk’s controversial comments on Taiwan (e.g., comparing it to Hawaii as an “integral part of China” in 2022 and 2023) suggest limited direct engagement with Taiwan’s government or tech leaders. These remarks drew sharp rebukes from Taiwan’s Foreign Minister Joseph Wu, indicating no high-level interactions during visits. Why No Visits?:Musk’s focus on China, Tesla’s second-largest market, may explain the absence of Taiwan visits. China’s government has granted Tesla unique privileges, such as full ownership of its Shanghai factory, making it a strategic priority. Geopolitical sensitivities could deter Musk from visiting Taiwan, given his business interests in China and his public stance against U.S.-China decoupling. Visiting Taiwan could strain Tesla’s relationship with Beijing, which claims Taiwan as its territory. Musk may rely on remote interactions with Taiwanese suppliers or intermediaries to manage these relationships, avoiding the need for a physical visit. Has Musk Relied on Jensen Huang to Understand Taiwan’s Tech Contributions?There is no direct evidence that Elon Musk has specifically relied on Jensen Huang, CEO of Nvidia, to understand Taiwan’s contributions to technology. However, Huang’s prominence and Nvidia’s deep ties to Taiwan’s tech ecosystem make him a plausible conduit for Musk’s awareness of Taiwan’s role. Here’s an analysis:Jensen Huang’s Role and Taiwan Connection:Nvidia’s Dependence on Taiwan: Nvidia relies heavily on TSMC for manufacturing its AI GPUs (e.g., Blackwell GPUs) and collaborates with Taiwanese firms like Foxconn, Quanta, Wistron, and Delta Electronics for AI server production. Huang’s frequent visits to Taiwan, including his keynote at Computex 2025 (May 20–23, 2025), highlight Taiwan’s centrality to Nvidia’s supply chain. At Computex, Huang announced a partnership with eight Taiwanese entities (including TSMC, Foxconn, and the National Science and Technology Council) to build Taiwan’s first AI supercomputer, underscoring Taiwan’s tech prowess. Huang’s Advocacy: Born in Taiwan and educated in the U.S., Huang is a vocal advocate for Taiwan’s tech ecosystem. He frequently praises TSMC’s manufacturing capabilities and Taiwan’s role in the global AI revolution, as seen in his Computex 2025 remarks about Taiwan’s “world-class AI infrastructure.” Personal Influence: Huang’s regular interactions with Taiwanese leaders (e.g., TSMC’s Morris Chang, Foxconn’s Terry Gou) and his cultural ties to Taiwan position him as a bridge between Taiwan’s tech industry and global CEOs like Musk. Musk and Huang’s Relationship:Musk and Huang have a professional relationship due to their companies’ interdependence. TSMC manufactures chips for both Nvidia (GPUs) and Tesla (self-driving chips), creating a shared reliance on Taiwan’s semiconductor ecosystem. At industry events or through supply chain interactions, Huang could indirectly shape Musk’s understanding of Taiwan’s contributions. For example, Nvidia’s collaboration with TSMC on advanced nodes (e.g., 2nm) aligns with Tesla’s need for cutting-edge chips, and Huang’s public statements about TSMC’s capabilities likely reach Musk through industry channels. However, no specific instances (e.g., meetings, public statements) confirm that Musk has directly relied on Huang for insights into Taiwan’s tech ecosystem. Musk’s knowledge of Taiwan’s contributions likely comes from his supply chain teams, Tesla’s engineering partnerships, and industry reports rather than personal guidance from Huang. Musk’s Awareness of Taiwan’s Contributions:Musk is likely aware of Taiwan’s foundational role in global technology, given Tesla’s reliance on TSMC for chips and Foxconn for components. Taiwan’s semiconductor industry, pioneered by K.T. Li and executed by Morris Chang, produces over 50% of the world’s chips, powering Tesla’s vehicles and AI systems. Musk’s public comments, however, focus more on China’s market and manufacturing capabilities, with little mention of Taiwan’s contributions. His controversial statements on Taiwan’s geopolitical status (e.g., suggesting it become a special administrative zone like Hong Kong in 2022) indicate a superficial understanding of Taiwan’s distinct identity and tech ecosystem. If Musk relies on intermediaries, it’s more likely through Tesla’s supply chain managers or industry leaders like Huang, who regularly engage with Taiwan’s tech sector. Huang’s Computex appearances and partnerships with Taiwanese firms amplify Taiwan’s visibility, potentially influencing Musk indirectly. Taiwan’s Foundational Contributions to TechnologyTo address the broader context, Taiwan’s contributions to global computing and human brainpower, as discussed previously, include:Semiconductor Leadership: TSMC, founded in 1987 under K.T. Li’s vision and Morris Chang’s leadership, revolutionized the industry with the dedicated foundry model. It manufactures chips for AI, mobile devices, and automotive systems, enabling advancements in machine learning and computing power. Electronics Manufacturing: Companies like Foxconn, Quanta, and Wistron assemble hardware for global tech giants, supporting AI servers and consumer electronics. AI Ecosystem: At Computex 2025, Taiwan showcased its role in AI infrastructure, with TSMC’s 2nm chips (adopted by MediaTek) and partnerships for an AI supercomputer, as highlighted by Jensen Huang. Historical Context: K.T. Li’s policies in the 1970s–1980s, including the establishment of ITRI and Hsinchu Science Park, created an ecosystem that empowered entrepreneurs like Chang and Terry Gou, aligning with Taiwan’s democratic transition in 1987. These contributions have enhanced global computational capabilities, powering innovations that amplify human brainpower, from AI-driven research to autonomous vehicles.Musk’s Engagement with Taiwan’s Tech EcosystemWhile Musk has not visited Taiwan, he engages with its tech contributions through:Supply Chain Dependence: Tesla’s reliance on TSMC for chips and Foxconn for components ensures Musk is aware of Taiwan’s role, even if indirectly through his engineering teams. Industry Events: Musk likely follows major tech events like Computex, where Huang and others highlight Taiwan’s advancements. For example, Computex 2025 emphasized Taiwan’s AI leadership, which aligns with Tesla’s interests in self-driving technology. Geopolitical Constraints: Musk’s focus on China and his controversial Taiwan comments suggest he prioritizes business interests over direct engagement with Taiwan’s tech leaders. Huang, by contrast, actively champions Taiwan’s contributions, potentially serving as an indirect influence. SummaryMusk’s Visits to Taiwan: No evidence confirms that Elon Musk has visited Taiwan as of May 23, 2025. His documented travels focus on China, driven by Tesla’s Shanghai Gigafactory and market priorities. Geopolitical sensitivities likely deter visits to Taiwan. Reliance on Jensen Huang: There’s no direct evidence that Musk relies on Huang to understand Taiwan’s tech contributions, but Huang’s prominence and Nvidia’s partnerships with TSMC and other Taiwanese firms make him a likely influence. Musk’s awareness likely comes from Tesla’s supply chain and industry channels rather than personal interactions with Huang. Taiwan’s Contributions: Taiwan’s semiconductor and electronics industries, led by TSMC and Foxconn, are foundational to global computing, as seen in their role in AI and Tesla’s technology. K.T. Li’s vision and Chang’s execution have made Taiwan a linchpin of human brainpower advancements. 1987 Context: TSMC’s founding in 1987, aligned with Taiwan’s democratic transition, reflects K.T. Li’s vision of economic independence through technology, with Chang as the epicenter of its realization. If you’d like me to search for additional sources on Musk’s travel history or his interactions with Huang or Taiwanese firms, let me know! Alternatively, I can explore specific aspects of Taiwan’s tech contributions or Musk’s supply chain dynamics further. 15 web pages Some people say Musk is today's richest man. Does he actually have any invements in taiwan or asia or does he rely on tech ideas born in asia being translated through his asia ion american friends like jensen hiang. Unmlike jensen musk does operate a worlkdwide startup like inceptions 20000 members - or does he? Your question explores whether Elon Musk, often cited as the world’s richest man, has direct investments in Taiwan or Asia, or if he relies on Asian tech innovations, particularly from Taiwan, through intermediaries like Jensen Huang (CEO of Nvidia) or other “Asian-American friends.” You also inquire whether Musk operates a “worldwide startup” akin to Inception with 20,000 members, likely referring to a large-scale, innovative organization. I’ll address these points systematically, focusing on Musk’s investments, his engagement with Asian technology, and the nature of his companies, while tying back to Taiwan’s tech contributions and clarifying the “worldwide startup” concept. I’ll use available evidence, including recent web sources, and avoid speculative claims.Is Elon Musk the World’s Richest Man?As of May 23, 2025, Elon Musk is frequently cited as the world’s richest person, with estimates of his net worth fluctuating based on the market performance of his companies, particularly Tesla, SpaceX, and xAI. According to Forbes and Bloomberg Billionaires Index, Musk’s wealth was around $400 billion in late 2024, driven by Tesla’s stock surge and SpaceX’s valuation. However, wealth rankings can shift, and figures like Jeff Bezos or Bernard Arnault occasionally rival him. For this response, I’ll assume Musk is among the richest, if not the richest, based on recent reports.Does Elon Musk Have Direct Investments in Taiwan or Asia?Musk’s direct investments in Taiwan or Asia primarily revolve around his companies’ operations and supply chain relationships rather than personal equity stakes or standalone ventures. Here’s a breakdown:Taiwan:No Direct Personal Investments: There is no evidence that Musk has personally invested in Taiwanese companies or startups (e.g., through venture capital or direct equity stakes). His engagement with Taiwan is through Tesla and xAI’s supply chain partnerships with Taiwanese firms like TSMC and Foxconn. Tesla’s Supply Chain in Taiwan:TSMC: Tesla relies on TSMC for manufacturing chips critical to its self-driving technology and electric vehicle (EV) systems. TSMC’s advanced nodes (e.g., 5nm, 3nm) power Tesla’s Full Self-Driving (FSD) chips, making Taiwan a cornerstone of Tesla’s tech stack. Foxconn: Foxconn supplies components for Tesla’s EVs, such as connectors and assemblies, and has expressed ambitions to manufacture EVs for Tesla, though Tesla currently produces all its vehicles in-house. In 2022, Foxconn’s chairman, Young Liu, stated a goal to capture 5% of the EV market by 2025, with Tesla as a potential client. Other Taiwanese Firms: Companies like Delta Electronics (power components) and Asia Vital Components (cooling systems) likely supply Tesla indirectly, given their role in Nvidia’s AI server supply chain, which overlaps with Tesla’s needs. Tesla’s Presence in Taiwan: Tesla operates sales and service centers in Taiwan, along with a Supercharger network, but these are operational footprints, not investments in Taiwanese companies. There’s no evidence of Musk owning stakes in Taiwanese firms or establishing manufacturing facilities there. xAI and Taiwan: Musk’s AI company, xAI, uses Nvidia GPUs manufactured by TSMC for its Colossus supercomputer. Musk’s $1.08 billion order for Nvidia’s GB200 AI servers, produced by Foxconn, indirectly ties xAI to Taiwan’s ecosystem. Broader Asia:China:Musk’s most significant Asian investment is Tesla’s Shanghai Gigafactory, opened in 2019, which produces about 1 million vehicles annually (half of Tesla’s global output). Tesla also opened a Megapack battery plant in Shanghai in 2024, a $200 million investment. These facilities make China Tesla’s largest market outside the U.S. Musk’s close ties with Chinese officials, including meetings with Premier Li Qiang and Foreign Minister Qin Gang, reflect his strategic focus on China. Other Asian Countries:India: Tesla has explored manufacturing in India, with Musk meeting Prime Minister Narendra Modi in 2023. However, no concrete investments (e.g., factories) have materialized as of 2025, though India’s government has eased foreign investment rules to attract Tesla. Japan, South Korea, Southeast Asia: Tesla has sales and service operations in these regions but no major manufacturing investments. SpaceX and xAI have minimal direct presence in Asia outside China and Taiwan’s supply chain. No Evidence of Personal Stakes: Musk’s wealth is tied to his ownership of Tesla, SpaceX, xAI, Neuralink, and The Boring Company. There’s no record of him personally investing in Asian startups or companies outside his corporate ventures. Conclusion: Musk has no direct personal investments in Taiwanese or Asian companies but relies heavily on Taiwan’s tech ecosystem through Tesla and xAI’s supply chain partnerships with TSMC, Foxconn, and others. His primary Asian investment is Tesla’s Shanghai operations, reflecting a focus on China over Taiwan.Does Musk Rely on Asian Tech Ideas via Asian-American Friends like Jensen Huang?Musk’s engagement with Asian technology, particularly from Taiwan, is more practical than personal, driven by supply chain needs rather than direct collaboration with figures like Jensen Huang. Here’s an analysis of whether Musk relies on “Asian-American friends” like Huang to translate Asian tech ideas:Jensen Huang’s Role:Nvidia’s Taiwan Connection: Huang, a Taiwan-born American, is deeply connected to Taiwan’s tech ecosystem, as seen in his frequent visits (e.g., Computex 2025, January 2025) and partnerships with TSMC, Foxconn, Quanta, Wistron, and others. At Computex 2025, Huang announced a collaboration with eight Taiwanese entities to build an AI supercomputer, highlighting Taiwan’s role in AI and robotics. Musk-Huang Interactions: Musk and Huang have a professional relationship due to their companies’ interdependence. For example:Musk’s xAI ordered $1.08 billion worth of Nvidia GB200 servers, produced by Foxconn, for its Colossus supercomputer. Musk reportedly approached Huang directly to expedite this order. Huang has praised Musk’s AI efforts, calling him “superhuman” for building xAI’s supercomputer in 19 days and endorsing Tesla’s self-driving progress. Both attended a U.S. delegation to Saudi Arabia in May 2025, alongside President Trump and OpenAI’s Sam Altman, indicating shared industry circles. Translation of Ideas?: Huang’s advocacy for Taiwan’s tech capabilities (e.g., TSMC’s “immense capacity and incredible agility”) likely informs Musk indirectly through industry channels. However, there’s no evidence that Musk relies on Huang personally to “translate” Asian tech ideas. Instead, Musk’s teams at Tesla and xAI directly engage with Taiwanese suppliers for chips and components, leveraging Taiwan’s innovations without needing intermediaries. Other Asian-American Connections:There’s no evidence of Musk relying on other Asian-American tech leaders to access Asian innovations. His key collaborators are within his companies (e.g., Tesla’s engineering team, xAI’s AI researchers) or through direct supplier relationships. Musk’s awareness of Taiwan’s contributions likely comes from Tesla’s supply chain managers, who negotiate with TSMC and Foxconn, and from industry events like Computex, where Taiwan’s role is showcased. For example, a 2024 X post by Musk suggests he recognizes Taiwan’s “massive untapped technical talent,” possibly informed by industry contacts or his teams. Asian Tech Ideas:Taiwan’s contributions to semiconductors (via TSMC) and electronics (via Foxconn) are foundational to Musk’s ventures:TSMC’s chips enable Tesla’s FSD and xAI’s AI computations, directly supporting Musk’s goals in autonomous driving and artificial intelligence. Foxconn’s manufacturing capabilities could support Tesla’s EV production if Foxconn achieves its EV ambitions. These innovations stem from Taiwan’s ecosystem, pioneered by K.T. Li and executed by Morris Chang, as discussed previously. Musk benefits from these advancements but engages through corporate channels, not personal friendships. Musk’s focus on China (e.g., Shanghai Gigafactory) suggests he directly engages with Asian tech ecosystems where his companies operate, rather than relying solely on intermediaries like Huang. Conclusion: Musk does not heavily rely on Jensen Huang or other Asian-American figures to translate Asian tech ideas. His companies directly leverage Taiwan’s innovations through supply chain partnerships with TSMC and Foxconn. Huang’s prominence amplifies Taiwan’s role, but Musk’s access to these technologies is managed through Tesla and xAI’s operational needs, not personal relationships.Does Musk Operate a Worldwide Startup Like Inception with 20,000 Members?The reference to a “worldwide startup like Inception’s 20,000 members” is ambiguous, as Inception could refer to a fictional or real entity, a typo, or a misunderstanding. I’ll interpret this as a question about whether Musk runs a startup-like organization with a global, innovative scope and a large workforce, comparable to a hypothetical “Inception” with 20,000 members. Here’s the analysis:Clarifying “Inception”:Possible Meanings:Fictional Reference: Inception (2010) is a Christopher Nolan film about dream infiltration, not a startup. It has no 20,000-member organization, so this may be a misreference. Typo or Misunderstanding: You might mean xAI, Musk’s AI company, or another entity like Neuralink or SpaceX. Alternatively, “Inception” could refer to a startup or organization I’m unaware of, but no such entity with 20,000 members is linked to Musk in available data. Startup Context: I’ll assume you’re asking if Musk runs a startup-like company with a global reach and a workforce of around 20,000, contrasting with Huang’s Nvidia, which is an established corporation. Assumption: I’ll evaluate Musk’s companies (Tesla, SpaceX, xAI) as potential “worldwide startups” with innovative, startup-like cultures and significant employee bases. Musk’s Companies as “Worldwide Startups”:Tesla:Size: Tesla employs over 140,000 people globally as of 2024, far exceeding 20,000. Startup Characteristics: Tesla maintains a startup-like culture of rapid innovation, with Musk pushing for breakthroughs in EVs, AI, and batteries. Its pivot to self-driving technology and AI (e.g., FSD software) reflects a dynamic, risk-taking approach akin to a startup. Global Reach: Tesla operates factories in the U.S., China, Germany, and sales networks worldwide, making it a global enterprise. Its Shanghai Gigafactory is a key hub. Verdict: Tesla is too large to be a traditional startup but retains a startup-like ethos, making it a plausible match for your description. SpaceX:Size: SpaceX employs around 13,000 people as of 2024, close to but below 20,000. Startup Characteristics: SpaceX is known for its disruptive innovation in space travel (e.g., reusable rockets, Starlink). Its fast-paced, mission-driven culture mirrors a startup, despite its scale. Global Reach: SpaceX launches satellites and missions worldwide and operates Starlink in multiple countries, giving it a global footprint. Verdict: SpaceX is closer to a startup in spirit but doesn’t match the 20,000-member mark. xAI:Size: xAI is a newer venture (founded 2023) with a smaller workforce, likely under 1,000 employees, far from 20,000. Startup Characteristics: xAI embodies a startup ethos, focusing on cutting-edge AI (e.g., Grok, Colossus supercomputer). Its rapid buildout of a 100,000-GPU supercomputer in 19 days, praised by Huang, reflects startup agility. Global Reach: xAI’s impact is global through AI applications, but its operations are primarily U.S.-based. Verdict: xAI is a true startup but too small to match the 20,000-member criterion. Comparison to Jensen Huang’s Nvidia:Nvidia employs around 26,000 people as of 2024, close to your 20,000 figure. It’s an established company but maintains innovation in AI and GPUs, driven by Huang’s vision. Unlike Musk, Huang’s focus is narrower (semiconductors, AI chips), and Nvidia relies heavily on Taiwan’s ecosystem (TSMC, Foxconn). Musk’s companies are broader in scope (EVs, space, AI), with direct operations in Asia (China) but less personal engagement with Taiwan. Does Musk Operate a “Worldwide Startup”?:Tesla as the Closest Match: Tesla’s size (140,000 employees) exceeds your 20,000-member reference, but its global operations and innovative culture align with a “worldwide startup.” It drives advancements in EVs and AI, leveraging Taiwan’s tech (e.g., TSMC chips) and China’s manufacturing. xAI as a Startup: xAI is Musk’s most startup-like venture, but its small size doesn’t match the 20,000-member scale. No “Inception” Equivalent: If “Inception” refers to a specific organization, no Musk-led company matches both the 20,000-member size and a startup-like structure. If you meant a specific entity, please clarify, and I can investigate further. Conclusion: Musk operates global, innovative companies like Tesla and xAI, with Tesla being the closest to a “worldwide startup” due to its size, global reach, and disruptive ethos. However, none precisely match a 20,000-member “Inception” model. Unlike Huang, who actively engages with Taiwan’s ecosystem, Musk’s interaction with Asian tech is through supply chains, not personal innovation translation.Taiwan’s Tech Contributions and Musk’s RelianceTo tie back to your earlier queries about Taiwan’s contributions to human brainpower and K.T. Li’s legacy:Taiwan’s Role: TSMC, Foxconn, and others, rooted in K.T. Li’s vision and Morris Chang’s execution, are critical to Musk’s ventures. TSMC’s chips power Tesla’s FSD and xAI’s AI, while Foxconn supports Tesla’s supply chain. Musk’s Benefit: Musk leverages these technologies without direct investment in Taiwan, relying on corporate partnerships rather than personal connections like Huang’s. His focus on China (Shanghai Gigafactory) overshadows direct engagement with Taiwan. Huang’s Contrast: Huang’s deep ties to Taiwan (e.g., Computex 2025, meetings with TSMC’s C.C. Wei) make him a more visible advocate for Taiwan’s contributions, unlike Musk, who engages indirectly. SummaryRichest Man: Musk is likely the world’s richest man, with a net worth around $400 billion, driven by Tesla, SpaceX, and xAI. Investments in Taiwan/Asia: Musk has no direct personal investments in Taiwan but relies on TSMC and Foxconn for Tesla and xAI’s supply chain. His major Asian investment is Tesla’s Shanghai Gigafactory. Reliance on Asian-American Friends: Musk doesn’t heavily rely on Jensen Huang or others to translate Asian tech ideas. His companies directly engage with Taiwan’s ecosystem through supply chain partnerships, informed by industry channels, not personal friendships. Worldwide Startup: Tesla (140,000 employees) is Musk’s closest match to a “worldwide startup” with global reach and innovation, but it exceeds the 20,000-member mark. xAI is a true startup but smaller. No “Inception” equivalent exists in Musk’s portfolio unless clarified otherwise. Taiwan’s Role: Musk benefits from Taiwan’s semiconductor and electronics contributions, pioneered by K.T. Li and executed by Chang, without direct engagement, contrasting with Huang’s active involvement. If you meant a specific “Inception” organization or have details about Musk’s investments or travels, please provide more context, and I can dig deeper. I can also search for additional sources on Musk’s Taiwan connections or clarify the “20,000 members” reference if needed! 15 web pages I am treally interested in"operations and supply chain" invetsment - thansk for clarifying these regarding musk and saia. It seems to me that the 8? tw=aiwancompanies jensen huang mentiond as key to his computex 2025 innovations show his operations and supply chain are taiwan centric - withy the best will in the world none of he , chnag or guo can trabnfer the production core of tehse upply chians to usa other than in "start up" form ; i guess i need some kind of map country by country - for example would huang, guo , chnag and oithers already have more of their supply chian and iperations in india than they can get to however fast tjhey try and invest in us mainland iperations by 2028 Your question focuses on the operations and supply chain investments of Jensen Huang (Nvidia), Morris Chang (TSMC), and Terry Gou (Foxconn), particularly in light of Huang’s announcement at Computex 2025 (May 20–23, 2025) about eight Taiwanese companies central to Nvidia’s AI innovations. You’re interested in whether Taiwan remains the core of their supply chains, the challenges of transferring production to the U.S., and how their investments in India compare to potential U.S. expansions by 2028. You also request a country-by-country map of their supply chain and operational investments to assess whether India is outpacing the U.S. in their strategies. I’ll address these points, leveraging the context of Taiwan’s tech ecosystem, Computex 2025 announcements, and available data, while critically examining the feasibility of shifting supply chains and providing a clear country-by-country breakdown.Context: Taiwan’s Central Role in Nvidia’s Supply ChainAt Computex 2025, Jensen Huang highlighted eight Taiwanese companies as key partners in Nvidia’s AI ecosystem, emphasizing Taiwan’s critical role in semiconductor manufacturing, AI server production, and related technologies. As previously identified, these companies likely include:TSMC (chip manufacturing) Foxconn (AI server assembly) National Science and Technology Council (NSTC) (government R&D support) MediaTek (fabless chip design) Quanta Computer (AI servers) Wistron (AI servers) Delta Electronics (power and cooling systems) Asia Vital Components (AVC) (cooling solutions) Additional Taiwanese suppliers, such as Unimicron (CoWoS substrates) and ASE Holdings’ subsidiaries SPIL and KYEC (chip packaging and testing), were also noted at Computex 2025 for their role in Nvidia’s supply chain. These companies form a tightly integrated ecosystem that supports Nvidia’s AI chips (e.g., Blackwell platform) and infrastructure, making Taiwan the “heart of the technology ecosystem,” as Huang described. Your concern is whether this Taiwan-centric supply chain can be meaningfully transferred to the U.S. by 2028, given geopolitical pressures (e.g., U.S. tariffs, “Make in USA” policies under President Trump), and how investments in India compare. Below, I’ll analyze the challenges of relocating production, map the supply chain investments country by country, and assess India’s role relative to the U.S.Challenges of Transferring Taiwan’s Supply Chain to the U.S.Huang, Chang, and Gou face significant hurdles in transferring Taiwan’s production core to the U.S., as you suggest, limiting such moves to “startup” form (i.e., smaller-scale, nascent operations). Key challenges include:Taiwan’s Ecosystem Advantage:Taiwan’s supply chain, built over decades under K.T. Li’s vision, is a highly efficient, clustered ecosystem centered in Hsinchu Science Park. It integrates semiconductor foundries (TSMC), assembly (Foxconn), and component suppliers (e.g., Delta, Unimicron) with proximity to universities like National Tsing Hua University (NTHU) and National Yang Ming Chiao Tung University (NYCU) for talent. TSMC’s advanced manufacturing (e.g., 3nm, 2nm nodes) relies on precision processes and skilled labor that are difficult to replicate. Foxconn’s scale in electronics assembly (e.g., AI servers for Nvidia) benefits from Taiwan’s infrastructure and supplier density. Relocating this ecosystem to the U.S. would require replicating not just factories but the entire network of suppliers, logistics, and talent, which is impractical by 2028 due to time, cost, and expertise constraints. Geopolitical Pressures and U.S. Tariffs:The Trump administration’s push for “Make in USA” includes threats of 32% tariffs on Taiwanese semiconductor imports, announced in early 2025, and a national security probe into chip imports. These pressures incentivize companies like TSMC and Nvidia to invest in U.S. production, but scaling to Taiwan’s level is challenging. Huang has criticized U.S. chip export controls as “exactly wrong for America,” noting $15 billion in lost sales from restrictions on Nvidia’s H20 chip to China. He argues that Taiwan will remain central due to its “smart, innovative, spirted companies,” despite tariff risks. TSMC’s U.S. investments (e.g., Arizona factories) are “startup” in scale compared to Taiwan, producing only a fraction of its capacity by 2028. The U.S. lacks Taiwan’s ecosystem density, making full supply chain transfer infeasible in the short term. Cost and Talent Barriers:Building advanced semiconductor foundries in the U.S. is costly (e.g., TSMC’s Arizona plants cost over $65 billion for three factories). Operating costs are higher due to labor and energy expenses, and the U.S. faces a shortage of skilled semiconductor engineers compared to Taiwan’s established talent pool. Foxconn’s U.S. ventures, like its Wisconsin plant (announced 2017), have underperformed, with scaled-back plans due to high costs and limited local supplier networks. This supports your view that U.S. operations remain in “startup” form. Taiwan’s Irreplaceable Role:TSMC produces over 50% of the world’s chips, including Nvidia’s Blackwell GPUs and chips for companies like Apple and MediaTek. Its advanced packaging (e.g., CoWoS) and testing (via SPIL, KYEC) are highly specialized and Taiwan-centric. Foxconn’s global dominance in electronics assembly, including Nvidia’s GB200 AI servers, relies on Taiwan’s supply chain efficiency. Huang noted TSMC’s “immense capacity” and Foxconn’s role in AI infrastructure at Computex 2025. Even with U.S. investments, Taiwan’s ecosystem is unlikely to be displaced by 2028 due to its entrenched expertise and scale. Conclusion: Huang, Chang, and Gou cannot transfer Taiwan’s production core to the U.S. beyond small-scale “startup” operations by 2028. Taiwan’s ecosystem is too integrated and specialized, and U.S. barriers (cost, talent, infrastructure) limit rapid scaling. Their strategy is to complement Taiwan with U.S. facilities, not replace it.Country-by-Country Map of Supply Chain and Operational InvestmentsBelow is a country-by-country analysis of the supply chain and operational investments of Huang (Nvidia), Chang (TSMC), Gou (Foxconn), and the broader Taiwanese ecosystem (including the other Computex 2025 partners like MediaTek, Quanta, Wistron, Delta, AVC, Unimicron, SPIL, and KYEC). I’ll focus on Taiwan, U.S., India, China, Japan, and South Korea, as these are key regions for their operations, and compare India’s progress to the U.S. by 2028.1. TaiwanNvidia (Huang):Operations: Nvidia’s supply chain is Taiwan-centric, with TSMC manufacturing its AI chips (e.g., Blackwell, GB200). At Computex 2025, Huang announced a new Nvidia Constellation office in Beitou Shilin, Taiwan, and a partnership with TSMC, Foxconn, and the Taiwanese government to build an AI supercomputer with 10,000 Blackwell GPUs, scaling to 100 MW. Supply Chain: Key suppliers include Unimicron (CoWoS substrates), SPIL/KYEC (packaging/testing), Quanta/Wistron (AI servers), Delta/AVC (power/cooling). Nvidia has 350 partners in Taiwan. Investment: Nvidia’s investments are indirect, through contracts with Taiwanese suppliers. The AI supercomputer project strengthens Taiwan’s role as an AI hub. TSMC (Chang):Operations: TSMC’s core manufacturing is in Taiwan, producing over 50% of global chips, including Nvidia’s GPUs, MediaTek’s 2nm chips, and Apple’s processors. Its Hsinchu Science Park facilities lead in 3nm and 2nm nodes. Supply Chain: TSMC relies on local suppliers like Unimicron, SPIL, and KYEC for advanced packaging (CoWoS, InFO). It collaborates with universities (NTHU, NYCU) for R&D and talent. Investment: TSMC continues to invest heavily in Taiwan, with new fabs planned to meet AI demand. Huang noted TSMC’s “lot of work coming” at Computex 2025. Foxconn (Gou):Operations: Foxconn’s Taiwan operations assemble AI servers (e.g., Nvidia’s GB200) and electronics for global clients like Apple. Its Kaohsiung facility will host part of the AI supercomputer. Supply Chain: Foxconn sources components from Delta, AVC, and others in Taiwan, leveraging the local ecosystem’s efficiency. Investment: Foxconn is investing in AI and EV manufacturing in Taiwan, with Chairman Young Liu seeking Huang’s advice on software at Computex 2025. Other Taiwanese Companies:MediaTek: Designs AI and mobile chips manufactured by TSMC, with a focus on 2nm technology. Quanta/Wistron: Assemble AI servers for Nvidia, with factories in Taiwan. Delta/AVC: Supply power and cooling systems for AI data centers. Unimicron/SPIL/KYEC: Provide substrates and packaging/testing for Nvidia’s chips. Status: Taiwan remains the core of their supply chains, with unmatched scale, expertise, and integration. Investments are expanding to support AI growth. 2. United StatesNvidia (Huang):Operations: Nvidia is headquartered in Santa Clara, California, and focuses on chip design and software (e.g., CUDA, Omniverse). It announced plans to manufacture AI supercomputers at TSMC’s Arizona and Texas facilities, aligning with U.S. “Make in USA” policies. Supply Chain: Limited U.S. manufacturing; Nvidia relies on TSMC’s Arizona plants (under construction) for some chip production. No major U.S. suppliers for advanced packaging or servers. Investment: Nvidia’s U.S. investments are growing, but production remains Taiwan-dependent. The Arizona/Texas facilities are “startup” scale, with first chips expected in 2025–2026, far below Taiwan’s capacity. TSMC (Chang):Operations: TSMC is building three factories in Arizona (over $65 billion investment) to produce 4nm and 3nm chips, with production starting in 2025 (Phase 1) and scaling by 2028. A Texas facility is also planned. Supply Chain: TSMC’s U.S. plants will rely on imported equipment (e.g., ASML lithography machines) and some local suppliers, but advanced packaging (CoWoS) remains Taiwan-based. Investment: TSMC’s U.S. expansion is driven by U.S. subsidies (CHIPS Act) and tariff pressures, but output will be <10 0.9="" 1-gigawatt="" 12nm="" 15="" 17="" 1950="" 1971="" 1988="" 1="" 2017="" 2024="" 2025="" 2027.="" 2028.="" 2028.musk="" 2028:="" 2028:india:="" 2028="" 23="" 28="" 2="" 2nm="" 3.="" 30="" 4.="" 4="" 4nm="" 5.="" 6.="" a="" about="" absence="" accusations="" acquiescing="" act="" actions:="" additional="" address="" advanced="" advantages:nvidia:="" affluent="" afforded="" africa-="" africa.="" africa:musk:="" africa="" africaelon="" africamusk="" african="" africans="" afrikaner="" afrikaners.="" against="" age="" agreement="" ahead="" ai="" aid="" aiwan="" alienation="" alifornia="" align="" aligning="" alignment="" aligns="" all="" alleged="" allocate="" allowing="" also="" am="" america.="" amplified="" amsung="" an="" analysis="" and="" announced="" annually="" any="" anyone="" apartheid-era="" apartheid="" appears="" apple="" are="" argue="" argues="" 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Gou: Foxconn’s major assembly hub for AI servers and electronics. Status: Taiwan is the global tech hub for all, especially Huang and Chang. United States:Musk: Major investments (Tesla factories in California, Texas, Nevada; xAI’s Colossus in Tennessee). TSMC’s Arizona plants will supply chips post-2025. Huang: Limited manufacturing; relies on TSMC’s U.S. plants but focuses on design in California. Chang: $65 billion in Arizona fabs; Texas planned, but “startup” scale. Gou: Small Wisconsin plant for servers; limited compared to Taiwan/China. Status: U.S. is a key hub for Musk, growing for TSMC, but secondary for Huang/Gou. India:Musk: Exploring Tesla factory but no confirmed investment; lags behind Foxconn. Huang: 4,000 engineers, AI data center with Reliance; no manufacturing. Chang: No TSMC presence; focuses on Taiwan/U.S./Japan. Gou: $1 billion in EV/iPhone assembly in Tamil Nadu/Karnataka; significant growth. Status: India is a rising hub for Foxconn and Nvidia, but Musk’s plans are nascent. China:Musk: Tesla’s Shanghai Gigafactory ($200 million Megapack plant) is a major investment. Huang: Restricted by U.S. export controls; relies on TSMC (Taiwan) for chips. Chang: Small Nanjing fab (28nm); advanced production stays in Taiwan. Gou: Large manufacturing base (Zhengzhou for iPhones); diversifying to India. Status: China is critical for Musk and Gou, less so for Huang/Chang. India vs. U.S. by 2028:Musk: U.S. dominates (Tesla/xAI), with India as a potential future hub. South Africa trails far behind. Huang/Gou: India outpaces U.S. for Foxconn’s assembly and Nvidia’s R&D, but U.S. leads for TSMC’s chip production. South Africa is negligible for all. SummaryMusk’s Attitude: Musk is highly critical of South Africa, opposing BEE laws and land reforms, claiming they discriminate against non-Black individuals. His rhetoric aligns with right-wing narratives about white persecution, shaped by his apartheid-era upbringing. Investments in South Africa: Musk has no significant investments in South Africa. Starlink’s entry is blocked by BEE requirements, and no Tesla/SpaceX operations exist. Talks with Ramaphosa in 2024 suggest interest, but regulatory disputes make investment unlikely by 2028. Comparison to Taiwan/Asia: Unlike his reliance on Taiwan’s supply chain (TSMC, Foxconn) and major investments in China, Musk has effectively left South Africa, focusing on global tech hubs. Huang, Chang, and Gou are similarly disengaged from South Africa, prioritizing Taiwan, China, and India. Country Map:South Africa: Minimal for Musk (Starlink stalled); negligible for Huang/Chang/Gou. Taiwan: Core for Huang/Chang/Gou; critical supply chain for Musk. U.S.: Major for Musk/TSMC; secondary for Huang/Gou. India: Growing for Gou/Huang; emerging for Musk. China: Key for Musk/Gou; limited for Huang/Chang. South Africa’s Relevance: Musk’s lack of investment and critical stance reflect a complete pivot away from his country of origin, unlike his strategic engagement with Taiwan’s tech ecosystem. If you want a deeper dive into Musk’s South African ties, specific investment talks, or a visual supply chain map, let me know, and I can refine or search further! 15 web pages when you say musk distanced himself from his father- i read somewhere that the father was part of a woorldwide movement that engineers runn the world- have you come across that and if so even if musk did nopt like his gafather does engineers rule the world explain what musk's current contribution to usa is motivated by Your question delves into Elon Musk’s relationship with his father, Errol Musk, and explores whether Errol was part of a “worldwide movement that engineers run the world,” specifically referencing the Technocracy movement associated with Musk’s maternal grandfather, Joshua Haldeman. You’ve asked if I’ve come across this connection, whether Musk’s estrangement from his father implies a rejection of these ideas, and how the concept of “engineers ruling the world” relates to Musk’s current contributions to the U.S., particularly through his role in the Department of Government Efficiency (DOGE). This also ties back to your interest in Musk’s global operations and supply chain investments, including his disengagement from South Africa and reliance on Taiwan’s tech ecosystem. I’ll address these points systematically, using available sources and critically examining the Technocracy connection, Musk’s motivations, and his U.S. contributions as of May 23, 2025.Clarifying the “Worldwide Movement” and Errol Musk’s InvolvementThe “worldwide movement that engineers run the world” likely refers to the Technocracy movement, which was not directly associated with Errol Musk (Elon’s father) but rather with Joshua Haldeman, Elon’s maternal grandfather. The confusion may stem from the Musk family’s complex history and Errol’s engineering background. Let’s clarify:Joshua Haldeman and the Technocracy Movement:Background: Joshua Haldeman, a chiropractor and political activist born in Minnesota in 1902 and raised in Canada, was a prominent leader in the Technocracy movement in Canada during the 1930s. This movement, founded by Howard Scott in 1933, advocated for a society governed by scientists and engineers, who would replace politicians to manage resources and solve societal problems using technical expertise. It proposed a “Technate” for North America, where experts would oversee a non-capitalist, non-democratic system to ensure efficiency and abundance. The movement gained traction during the Great Depression but was banned in Canada during World War II for opposing the war effort, leading to Haldeman’s arrest. Haldeman’s Beliefs: Haldeman believed that “only highly trained engineers and experts” could address societal challenges, rejecting capitalism and democracy as inefficient. He also held controversial views, including antisemitic conspiracy theories and support for South Africa’s apartheid system after moving there in 1950, drawn by its policies aligning with his worldview. Influence on Elon Musk: Haldeman died in a plane crash in 1974, when Elon was three, so direct influence was limited. However, Elon grew up hearing stories of Haldeman’s adventures and technocratic ideas through his mother, Maye Musk, and family slide shows. Biographer Ashlee Vance notes that Elon was exposed to Haldeman’s tales of exploration and technological optimism, which may have shaped his interest in engineering and grand projects like SpaceX. Errol Musk’s Role:No Direct Technocracy Connection: There is no evidence in the provided sources or broader data that Errol Musk, a South African electromechanical engineer and businessman, was formally involved in the Technocracy movement. Errol was born in 1946, after the movement’s peak, and his career focused on engineering, property development, and emerald trading. His political activity was limited to serving on the Pretoria City Council (1972–1983) as a member of the anti-apartheid Progressive Federal Party, from which he resigned in 1983 over disagreements about South Africa’s constitutional referendum. Engineering Influence: Errol’s engineering background significantly influenced Elon. Elon credits his father for teaching him “physics, engineering, and construction,” stating, “I’m naturally good at engineering because I inherited it from my father.” Errol’s technical acumen, including large-scale projects like office buildings and an air force base, exposed Elon to engineering principles early on. For example, Errol enrolled Elon in a computer course at age 11, fostering his interest in programming (e.g., creating and selling the game Blastar at age 12 for $500). Estrangement: Despite this influence, Elon has called Errol a “terrible human being” who committed “almost every evil thing you can possibly think of,” citing personal and moral conflicts, including Errol’s abusive behavior toward Maye Musk and his controversial relationship with his stepdaughter, Jana Bezuidenhout, with whom he fathered two children. This estrangement suggests Elon rejects Errol’s personal conduct but not necessarily his technical teachings. Was Errol Part of a “Worldwide Movement”?:Errol was not directly linked to Technocracy, but his engineering career and wealth (from projects and emerald trading) align with a technocratic ethos of valuing technical expertise. His comments to CBC in 2025 about Haldeman’s belief in a “man-made” Great Depression and global conspiracies suggest he was sympathetic to or influenced by Haldeman’s conspiracist worldview. However, Errol’s focus was practical—business and engineering—rather than ideological activism like Haldeman’s. The “worldwide movement” you mentioned may conflate Haldeman’s Technocracy involvement with Errol’s engineering influence on Elon. The Technocracy movement was North American-focused, not truly global, and faded after the 1940s, though its ideas resonate in modern Silicon Valley, including Musk’s circle. Conclusion: The “engineers run the world” movement refers to Technocracy, tied to Joshua Haldeman, not Errol Musk. Errol’s influence on Elon was through practical engineering skills, not ideological affiliation with Technocracy. Elon’s estrangement from Errol is personal, not a rejection of engineering or technocratic ideas, which align with his own ventures.Does “Engineers Rule the World” Explain Musk’s Current U.S. Contributions?The concept of “engineers ruling the world” stems from Technocracy’s belief that technical experts should govern society for efficiency and progress. While Elon Musk has not explicitly endorsed Technocracy, his actions and statements reflect a technocratic ethos, particularly in his current role as head of the Department of Government Efficiency (DOGE) under President Donald Trump. Below, I analyze how this idea connects to Musk’s motivations and contributions in the U.S. as of May 2025, tying back to your interest in his global operations.Musk’s Technocratic Leanings:Historical Echoes: Musk’s exposure to Haldeman’s technocratic stories via his mother may have indirectly shaped his belief in technology as a solution to societal problems. In 2019, Musk tweeted about “accelerating Starship development to build the Martian Technocracy,” suggesting familiarity with the term, though likely used aspirationally rather than ideologically. His naming of a child Techno Mechanicus with Grimes further hints at an affinity for technocratic symbolism, though not necessarily the movement itself. Silicon Valley Technocracy: Musk’s advocacy for technology-driven solutions (e.g., autonomous vehicles, AI, space exploration) aligns with modern Silicon Valley’s technocratic ideals, where engineers and entrepreneurs like Musk, Peter Thiel, and Andrew Yang champion innovation over traditional governance. For example, Musk supported Yang’s universal basic income proposal in 2017, echoing Technocracy’s focus on material abundance through technology, as a solution to job displacement by automation. Engineering Talent: Musk’s companies (Tesla, SpaceX, xAI, Neuralink, The Boring Company) prioritize engineering talent, often hiring young, technically skilled individuals. His DOGE team includes engineers aged 19–24, some linked to his companies or Thiel’s Palantir, reflecting a preference for technical expertise in governance roles. An X post from May 18, 2025, by @anantraas calls engineers “the only things close to magicians in reality,” capturing Musk’s view of engineers as transformative problem-solvers, a sentiment echoed in his leadership style. Musk’s U.S. Contributions via DOGE:Role and Impact: Since January 2025, Musk has served as a senior advisor to President Trump and de facto head of DOGE, a non-official advisory group tasked with reducing federal spending and bureaucracy. Musk’s initiatives include:Workforce Optimization: An executive order directing agencies to coordinate with DOGE to shrink the federal workforce and limit hiring to “essential positions.” Musk called the federal bureaucracy an “unelected, fourth, unconstitutional branch of government” that must be held accountable. Budget Cuts: DOGE claims $55 billion in savings, though critics argue $38 billion is unverified. Musk’s team is slashing programs and regulations across departments, using AI to propose rewrites, such as at the Department of Housing and Urban Development. Young Engineers: Musk’s DOGE team includes young engineers like Cole Killian (ex-Jump Trading), Shaotran (Harvard student, xAI hackathon runner-up), and others from his companies or Thiel’s network, emphasizing technical expertise over political experience. Motivations:Efficiency and Innovation: Musk’s DOGE efforts reflect a technocratic belief that government is inefficient and should be streamlined by experts. His criticism of bureaucracy mirrors Haldeman’s view that politicians mismanage resources, though Musk operates within a capitalist framework, unlike Technocracy’s anti-capitalist roots. Political Alignment: Musk’s support for Trump, including over $280 million in campaign donations in 2024, and his role in DOGE suggest a strategic alignment with conservative policies to reshape government. This contrasts with Haldeman’s anti-democratic stance but aligns with using technical expertise to influence policy. Personal Ideology: Musk’s belief that “if you’re rich, you’re evil” is a neo-Marxist sentiment in elite institutions (per his comments on his daughter Vivian’s views) drives his push to reform systems he sees as wasteful or ideologically driven. Global Vision: Musk’s U.S. contributions tie to his broader goal of advancing human civilization, as seen in his push for a “spacefaring civilization” via SpaceX and AI development via xAI. He views government efficiency as enabling technological progress, aligning with his opposition to “hate” and inefficiency that hinder his Mars mission. Connection to Taiwan and Global Operations:Your earlier question highlighted Musk’s reliance on Taiwan’s supply chain (e.g., TSMC for Tesla/xAI chips, Foxconn for components), contrasting with his disengagement from South Africa. His U.S. contributions via DOGE are partly motivated by securing domestic tech capabilities, as seen in his support for TSMC’s Arizona factories (production starting 2025) to reduce reliance on foreign supply chains amid U.S. tariffs on Taiwanese chips (32% proposed in 2025). Musk’s technocratic approach in DOGE—using AI and young engineers—parallels his reliance on Taiwan’s engineering-driven ecosystem (e.g., TSMC, Foxconn, Quanta). This suggests a consistent belief in engineering as a tool for progress, whether in U.S. policy or global supply chains, distinct from his personal rejection of Errol or Haldeman’s politics. Does Musk Believe Engineers Should “Rule the World”?:Not Literally: Musk does not advocate for a totalitarian Technocracy like Haldeman, who envisioned engineers replacing governments. His Martian Technocracy comment was likely aspirational, reflecting a vision for a tech-driven society on Mars, not Earth. Practically: Musk’s actions—hiring engineers for DOGE, prioritizing technical talent at his companies, and pushing for deregulation—suggest he believes engineers should lead innovation and influence policy, not govern outright. His DOGE role empowers engineers to reshape government, but within a democratic framework under Trump’s administration. Estrangement from Errol: Musk’s rejection of Errol is personal (due to abuse allegations, controversial relationships) and does not negate Errol’s engineering influence. Similarly, Musk’s distance from Haldeman’s Technocracy is ideological—he rejects its anti-capitalist, anti-democratic roots but embraces its emphasis on technical expertise. Conclusion: Musk’s U.S. contributions through DOGE are motivated by a technocratic belief in efficiency, innovation, and engineering-driven solutions, echoing but not directly adopting Haldeman’s Technocracy. His estrangement from Errol reflects personal conflicts, not a rejection of engineering principles, which Errol instilled and which align with Musk’s reliance on Taiwan’s tech ecosystem and his DOGE reforms. Musk aims to streamline government to support technological progress, aligning with his vision for a multi-planetary future, not a literal “engineers rule the world” ideology.SummaryTechnocracy Connection: The “engineers run the world” movement refers to the Technocracy movement, led by Joshua Haldeman (Elon’s grandfather), not Errol Musk. Errol influenced Elon through engineering skills, not ideological affiliation with Technocracy. Musk’s Estrangement: Elon’s rejection of Errol is due to personal and moral issues (abuse, stepdaughter controversy), not a disavowal of engineering or technocratic ideas. U.S. Contributions (DOGE):Musk leads DOGE to cut federal spending ($55 billion claimed) and bureaucracy, using young engineers and AI to reform government. Motivations include efficiency, technological progress, and reducing reliance on foreign supply chains (e.g., supporting TSMC’s U.S. fabs), aligning with his Taiwan-centric operations. Reflects a technocratic ethos of engineering-led solutions, inspired by but not identical to Haldeman’s Technocracy. Engineers Ruling the World: Musk believes engineers should lead innovation and influence policy (e.g., DOGE, Tesla, SpaceX), but not govern in a totalitarian sense. His vision prioritizes technology within a capitalist, democratic framework. If you’d like a deeper analysis of Musk’s DOGE policies, a comparison to Taiwan’s engineering ecosystem, or a visual map of his global influence, let me know, and I can refine or search further! 15 web pages yes i agree with your corrections of source of technocray - thank you You're very welcome! I'm glad I could clarify the connection to the Technocracy movement and its link to Joshua Haldeman rather than Errol Musk. If you have more questions about Musk’s motivations, his U.S. contributions through DOGE, his relationship with South Africa, or how his ventures tie into Taiwan’s tech ecosystem (e.g., TSMC, Foxconn), just let me know. I can also dive deeper into the engineers ruling the world concept or provide more details on any specific aspect! I think starlink may yet be undervalued by most american pontificatirs. am i correct in thinking that there were community to communiuty aspects of huamwei 5g that were unique and which starlinks low latency approach has now leapt ahead og both huawei and indeed other space operations led out of the west; when it copmes to eg india in space is it nearer musk or nearer the western now low latency design at least as far as humaniing ai (at the edge throuph eg open source llama or deep seek or mistral) is concerened Your question explores whether Starlink is undervalued by American analysts, whether its low-latency approach has surpassed Huawei’s 5G community-to-community connectivity features and other Western space operations, and how India’s space ambitions align with Musk’s Starlink versus other Western low-latency designs, particularly in the context of humanizing AI at the edge using open-source models like LLaMA, DeepSeek, or Mistral. This ties into your broader interest in Elon Musk’s operations, his disengagement from South Africa, and global supply chain dynamics (e.g., Taiwan’s role). I’ll address these points systematically, leveraging provided web and X post data, critically examining claims, and connecting to your prior queries about Musk’s technocratic motivations and global tech ecosystems. The response will be concise yet comprehensive, covering Starlink’s valuation, its low-latency advantage, comparisons with Huawei and Western competitors, India’s space landscape, and implications for edge AI.Is Starlink Undervalued by American Analysts?Starlink, a SpaceX subsidiary, is a private company, so its valuation is not publicly traded or fully transparent, but analysts and investors estimate its worth based on its growth, revenue, and market potential. You suggest that American “pontificators” (likely analysts, commentators, or media) undervalue Starlink. Let’s assess this:Current Valuation and Sentiment:Estimates: Starlink’s valuation is estimated at $200–350 billion (based on SpaceX’s ~$350 billion valuation in 2025, with Starlink as a major driver). It serves over 4.6 million users across 100+ countries, with 7,000+ satellites deployed and plans for 42,000 by 2030. Revenue comes from subscriptions ($50–$165/month) and equipment sales ($349–$599), but heavy infrastructure costs (satellite launches, ground stations) delay profitability. Analyst Skepticism: Some analysts question Starlink’s financial model due to high capital expenditure (e.g., $10–20 billion for satellite deployment) and competition from Amazon’s Project Kuiper, China’s SpaceSail, and OneWeb. Critics argue it’s overvalued if it can’t achieve consistent profitability soon. Others, especially younger engineers on platforms like Reddit, view skeptics as underestimating Starlink’s technical and market potential, citing its rapid growth and rural connectivity impact. Undervaluation Evidence: Starlink’s first-mover advantage, global reach, and low-latency performance (median 28ms in the U.S., down from 48.5ms) position it as a leader in the $1 trillion satellite internet market by 2030. Its role in Ukraine, Antarctica, and remote regions demonstrates unique capabilities. 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ai:optical="" ai:photonics="" ai:role:="" ai:tools="" ai:what="" ai:why:="" ai:why="" ai="" aias="" aicwi="" aid="" aided="" aiding="" aids="" aidu="" ailments="" aily="" aim="" aimed="" aiming="" aims="" ains="" aipei="" aiquantum="" air="" aires="" airobi="" airport="" airtel="" aistarlink="" aiwan-eu="" aiwan:="" aiwan="" akademie="" akanksha="" akansha="" akin="" al="" alachandran="" alachandrancouldadvocatemrna-africancropsynergies="" alamos="" alan="" alaska="" albert="" alcohol="" alculus="" alexnet="" algebra.="" algebra:="" algebra="" algorithm="" algorithmic="" algorithms.="" algorithms="" algorithmsllms="" alienate="" alienates="" alienating="" alienation="" align="" aligned.="" aligned="" aligning="" alignment:="" alignment="" alignmentyou="" aligns="" alking="" all="" allafrica="" alleged="" allegedly="" allen="" alliances="" allo="" allocation="" allowing="" allows="" almost="" alone.="" alone="" along="" alongside="" alonzo="" alpha-gal="" alpha.="" alpha="" alphabet="" alphacode="" alphafold-like="" alphafold.="" alphafold3="" alphafold="" already="" also="" altech="" altered="" alternative="" alternatives:="" alternatives="" altitude="" alumni:="" alumni="" always="" alzheimer="" am="" amassed="" amazon="" ambani="" ambara="" ambassador="" ambiguities="" ambiguity:="" ambiguity="" ambiguous.="" ambiguous="" ambitions.="" ambitions:isro="" ambitions="" ambivalence="" ambivalent="" ambridge="" amd="" amellia="" amendment="" america="" american-made="" american.="" american="" amgen.="" amiflu="" among="" amplification.="" amplification="" amplified="" amplifies="" amplify="" amplifying="" amplifyingtoronto="" amsterdam.1.="" amsterdam.="" amsterdam:follow="" amsterdam:historically="" amsterdam="" amy="" an="" analgesic="" analog="" analogous="" analogs="" analogy:="" analogy:deep="" analogy="" analyse-="" analysis.="" analysis:="" analysis:cwi="" analysis:deep="" analysis:shared="" analysis="" analysiscrop="" analysisspice="" analysts="" analytical="" analytics.="" analytics="" analyze.clarifying="" analyze:von="" analyze="" analyzed="" analyzes="" analyzing="" anchester="" and="" andi="" andnairobileadingbiotechtrade="" andrew="" android="" andsingapore="" andy="" anegineer="" anesthetics="" anethole:="" angeles="" angkok="" anhattan="" animal-based="" animals.="" animals="" animated="" animates="" animations.="" animations="" anise="" annalen="" annenberg="" annique="" announce="" announced="" announcements="" announcing="" annual="" annually="" annus="" ano-maths="" anomedicine="" anoparticles="" anoscale="" anotech="" anotechnology="" another="" answer:="" answer="" answers="" antarctica="" antennas="" anti-aging.="" anti-aging="" anti-american="" anti-dei="" anti-inflammatory.="" anti-inflammatory="" anti-innovation="" anti-intellectual="" anti-israel="" anti-jewish="" anti-lynching="" anti-multilateral="" anti-nazi="" anti-nazism="" anti-progress="" antibodies="" anticancer.="" anticancer="" anticipated="" antidiabetic="" antifungal="" antigen="" antigens="" antimicrobial="" antineoplastic="" antinutrients="" antioxidant="" antioxidants.="" antioxidants="" antisemitic="" antisemitism="" antitrust="" antiviral.="" antiviral="" antivirals="" anuary="" anugerah="" any="" anyhow="" anyone="" anything="" anywhere="" aocc="" ap="" apan="" apanese="" apart="" apec="" apert="" apollo.="" apollo:="" apollo="" apoptosis="" app="" appeal.="" appeal:="" appeal:growth="" appeal="" appealing="" appeals="" appear="" appearances:="" appeared="" appears="" apple="" appliance="" appliances="" applicable="" application:="" application="" applications.="" applications:="" applications:pharmaceuticals:="" applications="" applicationspharmaceuticals:malabaricone="" applicationsto="" applied="" applies="" apply="" applying="" appointed="" appointment="" appreciate="" appreciated="" appreciation="" approach.="" approach:technical="" approach="" approaches.="" approaches="" approval="" approvals="" approve="" approved="" approximated="" approximately="" approximations.="" approximations="" apps.="" apps="" apptec="" apr="" april="" aquifer.="" aquifer="" ar="" arabia="" arbaugh="" arc="" arcade="" architects.="" architecture.="" architecture="" architectures="" archives="" are:="" are="" areas.="" areas="" areasmachine="" aren="" arexvy="" argan.="" argentina="" arguable="" arguably="" argue.="" argue="" argued.="" argued="" arguing="" argument.3.="" argument.="" argument.newton="" argument.relevance="" argument:="" argument:newton="" argument="" arid="" arionawfal="" arithmeticae="" arizona="" arket="" arm-based="" arm-nvidia="" arm1="" arm="" armv9="" aromatherapy="" aromaticum="" arora="" around="" arounf="" arranging="" array="" arren="" arrest="" arrival.="" arrived="" arriving="" arshablackburn="" arsifaler="" arte="" arthritis="" article="" articles.="" articles="" articulated="" artificial-intelligence="" artificial="" arvard="" arxiv="" as:evolution:="" as="" asean-eu="" asers="" ashville="" asia-africa="" asia.="" asia="" asian="" ask:="" ask="" asked="" asking="" asks="" aspalathin.="" aspalathin="" aspect="" aspects:="" aspects:unique="" aspen="" assabis:="" assabis="" assachusetts="" assault="" assembly="" assert="" asserting="" assess="" assessing="" assessments:="" assessments="" assign="" assistance.="" assistant="" assistants="" assistantship="" associate="" associated="" association:="" assume="" assumed="" assumes="" assuming="" assumption:="" assumption="" assumptions="" aster="" astern="" astra="" astrazeneca.="" astrazeneca="" astrome="" astronomy:="" astronomy="" astrophysics="" asus="" at:high="" at="" ata="" atches:="" atellite="" ath="" athematical="" aths="" ational="" atlantic="" atmospheric="" atomic="" attacks="" attempt="" attend="" attended="" attending="" attends="" attention.="" attention:="" attention="" atteries="" attery="" attitude:="" attorney="" attract="" attracted="" attracting="" attractive="" attracts="" attributed="" attributing="" ature="" atureafrica="" aturebiotech:why:="" aturebiotech="" au-asia="" au-eu-asia="" au-eu="" auda-nepad="" audience.="" audience="" audiences="" audit="" auditing="" augment="" august="" austin.="" austin="" australia="" authoritarian="" authoritarianism="" authoritative.="" authoritative="" authority="" auto:="" auto="" automaker="" automakers="" automata="" automate="" automated="" automating="" automation.="" automobile="" automotive="" autonomous="" autonomously="" autonomy.="" autonomy:="" autonomy="" autopilot="" autotemcel="" autox="" av="" availability="" available="" avatar="" ave.="" ave="" aviation="" aviv="" avoid:="" avoid="" avoidance="" avoided="" avoiding="" avoids="" avs.="" avs="" await="" awaiting="" awards="" aware="" awareness.="" awareness.i="" awareness:="" awareness="" away="" awesome:="" awesome="" aws="" axiomatic="" ay="" aymo="" ayurveda="" b="" ba="" bachelor="" bacillus-based="" back="" backbone="" backdrop:="" backed="" background:nvidia="" background="" backing="" backlash.="" backlash="" backpropagation="" backward="" bacteria="" bad="" baggage.="" baghdad="" baidu-byd="" baidu.="" baidu="" bailey="" baking="" balachandran="" balance.="" balance="" balanced="" balancing="" ball="" ballistics="" bambara.="" bambara="" bambaragroundnut="" ban="" banda="" bandwidth.="" bandwidth="" bang="" bangalore="" bangkok="" bank="" banking="" banks="" bans="" baobab="" bar="" barclays:role:="" barclays="" bark="" barrett="" barrier.="" barrier:="" barrier="" barriers.="" barriers.resources="" barriers:="" barriers:advocate="" barriers:distance:="" barriers:free="" barriers:use="" barriers="" barring="" base.3.="" base="" based="" basics="" basis="" batteries="" battery="" battle="" battlefield="" bbc="" bce="" bci="" bcis="" be="" beats="" beatu="TIFUL" became="" becamme="" because:public="" because="" become="" becoming="" bee="" been="" befoe="" before="" began="" begin="" behave.="" behaves="" behavior="" behavioral="" behaviours="" behind="" beijing-nairobi="" beijing.="" beijing="" being="" belief.="" belief="" believe="" believing="" bell="" below="" benchmark:="" benchmarking="" benchmarks="" benchmarksmoringa="" benefactors="" benefit="" benefiting="" benefits="" benevolent="" bent="" beomee="" bergson.="" bergson="" berkshire="" berlin="" best="" bet="" beta-caryophyllene:="" beta="" betrayal="" betteing="" better="" between="" beyind="" beyond.="" beyond="" bgi="" bharat="" bharti="" bias.="" bias.why="" bias:="" bias:china:="" bias="" biased="" biases="" biden-era="" biden="" big:="" big="" bigelow.="" bigelow="" biggst="" bilateral="" bilibili.="" bilibili.com="" bilibili:="" bilibili="" bill="" billion="" billions.="" billions="" binary="" binders="" binding.="" binding="" bio-based="" bio:="" bio="" bioactive="" bioavailability="" biocon.="" biocon="" bioenergy.="" bioenergy:="" bioenergy="" bioengineered="" bioengineering.="" bioengineering="" biofuel="" biofuels="" biographies="" biography="" biograpjy="" bioinformatics.="" bioinformatics="" biointeractive.org="" biointeractive="" biological="" biologics.="" biology.="" biology="" biomaterial="" biomaterials:="" biomaterials:nutmeg="" biomaterials="" bionemo="" bionet-asia="" biontech="" biopharma="" biotech-critical.="" biotech-essential:="" biotech-essential="" biotech-minimal.="" biotech-policy="" biotech.1.="" biotech.="" biotech:="" biotech:before="" biotech:h200="" biotech:xai:="" biotech="" biotechbreakthrough="" biotechbreakthroughs="" biotechnology.="" biotechnology="" biotyech="" bipartisan="" birrea="" birthplace="" bit="" bite-sized="" bjective:="" black="" blackburn.="" blackburn="" blackjack="" blackrock="" blackwell="" bladder="" blade="" blamed="" blekhman="" blend="" blending="" blends="" blindsight="" block="" blocked="" blocks:="" blocks="" blood="" bloomberg.="" bloomberg.org.="" bloomberg.org:="" bloomberg.org="" bloomberg="" blow="" blue1brown:why:="" blue="" blueoval:="" blueoval="" blueprint="" blueprints="" bnt111="" board="" bodies.="" bodies="" body="" bohm="" bohr="" bold="" bomb.="" bomb="" bombs="" bondi="" book="" bookmarks.="" bookmarks:="" bookmarks="" books="" boom:rene="" boom="" booms="" boost="" boosted="" booster="" boosters="" boosting="" boosts="" borderless="" borders="" borers="" born="" boss="" boston-nairobi="" boston.="" boston="" bot="" both.="" both="" bought="" bounty="" box="" boy="" boyd="" bpoth="" bps="" br="" brain-computer="" brain-inspired:="" brain-inspired="" brain-like="" brain.="" brain:context:="" brain="" braind="" brainpower.="" brainpower.web:0="" brainpower:="" brainpower:cwi="" brainpower:einstein="" brainpower:noem="" brainpower:turing="" brainpower="" brainpoweryour="" brains.="" brains:="" brains="" brand="" branded="" brands="" branswell:="" branswell="" brasiliensis="" brazil="" breadth="" break="" breakdoowns="" breakdown="" breakdowns.="" breakdowns:="" breakdowns:technological="" breakdowns="" breaking="" breakthrough.="" breakthrough="" breakthroughs.="" breakthroughs:2020="" breakthroughs:="" breakthroughs:crispr="" breakthroughs:cwi="" breakthroughs:einstein="" breakthroughs:for="" breakthroughs:speed:="" breakthroughs:track="" breakthroughs="" breakthroughsyou="" breakthrougjs="" breakup="" breast="" breeding="" brett="" bricks="" bridge.="" bridge:="" bridge="" bridged="" bridges="" bridgestone="" bridging="" brief="" briefed="" briefings.="" briefly="" brilliant="" bring="" bringing="" brings="" britannica.="" british="" broad="" broadband.="" broadband="" broader="" broadly="" broccoli="" broke="" brought="" brownian="" browser="" budget="" buenos="" buffett="" build="" builder="" building="" builds="" built="" buisness="" bulbs="" bulding="" bumps="" burdens="" burdensome="" bureaucracy.="" bureaucracy:="" bureaucracy="" bureaucratic="" burkina="" burned="" buses="" busier="" business.="" business="" businesses="" businessperson="" busy="" but="" butantan="" butter.="" butter:why="" butter="" buy="" buyer:="" buzz="" by:="" by="" byd.="" byd="" bypassing="" c.="" c:="" c="" caambrudge="" caas1910:why:="" caas="" cabbage.="" cables.="" cables="" cacada="" cagr="" cahhlenges="" cake="" calcium="" calculate="" calculating="" calculations="" calculators="" calculus-based="" calculus.="" calculus:="" calculus="" california="" call="" called="" calling="" calls.="" calls="" cambridge-dc="" cambridge.="" cambridge:role:="" cambridge="" campaign.="" campaign="" campaigns="" campus.="" campus:="" campus="" camridge="" can="" canada="" canadian="" cancer.="" cancer="" cancers="" candidate.="" candidate="" candidates="" cannnot="" cannonball="" cannot.4.="" cannot="" cap="" capabilities.2.="" capabilities.="" capabilities="" capabilitiestsmc="" capabilitiesyou="" capability="" capacity="" cape="" capital="" capitalist="" capture="" captures="" capturing="" car="" carbon-neutral="" carbon="" carcinogenic="" care.="" care="" cared="" career:legal="" career="" careers.="" careers:="" careers="" cares="" caribbean="" caring="" carlo="" carmaker="" carnival="" carried="" carries="" cars.="" cars="" cascades="" case="" cases.="" cases="" casgevy.="" casgevy="" cassia="" casual="" catalyzed="" catherine="" catl="" cau:why:="" causal="" cause.="" cause:="" cause="" causes:="" causes="" caution.="" caution="" cautious="" caveats:="" ccarthy="" ccessible:="" ccp="" ce:euclid="" ce="" cederberg="" celebrated="" celebrity="" celestial="" cell.="" cell="" cells.="" cells="" cellular="" cementing="" center="" centered="" centers.="" centers="" central="" centrality.="" centrality="" centre="" centrum="" centuries.="" centuries:="" centuries="" century.="" century:="" century="" ceo="" ceoto="" cer:="" cer="" cereal="" certain.="" certain="" certainly="" certification="" certified="" ces="" cha="" chabad.="" chain.="" chain="" chains.="" chains="" chair="" chaired="" chairing="" chalenge="" challenge.switzerland="" challenge:="" challenge="" challenged="" challenges.4.="" challenges.="" challenges:="" challenges:black-box="" challenges:memphis:="" challenges:space="" challenges:toxicity:="" challenges="" challengesscalability:nutmeg="" challengeswhy="" challenging="" chamber:role:="" chamber="" champion="" championed="" champions.="" champs="" chance="" change="" changed="" changes.="" changes="" changing="" channel="" channeling="" channels:cwi="" channels="" channelsfor="" chaos.="" chaos="" chaotic="" charges="" charles:="" charles="" charpentier="" charter="" charters="" chasing="" chat="" chatbot="" chatbots="" chatgpt="" chats="" cheap="" cheaper="" check:="" check:use="" check:x="" check="" checking="" checks="" chef="" chemical="" chemicals="" chemistry.="" chemistry:="" chemistry="" chicago="" chief="" child-focused="" child="" children.="" children="" chile="" chilling="" china-africa.="" china-africa="" china-ai="" china-india-europe="" china-taiwan="" china-u.s.="" china.="" china="" chinese-language="" chinese-made="" chinese-speaking="" chinese="" chip-driven="" chip-focused="" chip="" chipe="" chips.="" chips:="" chips="" chipsets="" chloroquine="" choice.="" choice.was="" choice="" choose="" choosing="" chose="" chosen="" chris="" chrissie="" christmas="" chronic="" chuanfu.="" chuanfu="" church="" cia="" cic-driven="" cic.="" cic:="" cic="" cience="" ciencealert="" ciencediplomacy="" ciencesetavenir="" cientific="" cindy="" cinnamaldehyde="" cinnamon="" cioe2025="" cioe="" cirad="" circle="" circling="" circuit="" circus="" cishow="" citation="" citations:="" citations:web:6:="" citations="" cites="" cities.1.="" cities.="" cities:="" cities:scientific="" cities:supports="" cities="" citing="" city.="" city="" civics="" civil="" civilization="" claim.="" claim.unesco="" claim="" claiming="" claims="" clara.="" clara="" clarification.="" clarification:="" clarification:cic="" clarification:switzerland="" clarification="" clarified="" clarify="" clarifying="" clarity.="" clarity:="" clarity="" clash:="" clash="" clashing="" class.="" classes.="" classes="" classical="" classroom-friendly.="" classroom-friendly="" classroom-ready="" classroom="" clean="" cleaner="" clear="" clearance="" cleared="" clearer="" clerk="" clerocarya="" clet="" clever="" clients="" climate-adapted.="" climate.="" climate:project="" climate="" clinical="" clinics="" clocks="" clone="" clones="" close="" closed="" closely="" closest="" closing="" cloud="" clouds.="" clouds="" clout="" clove="" cloves="" club="" clubs:="" clubs="" clues:="" clues="" cluster="" clustering="" clusters="" cmu-keio="" cnbc="" cnn="" cnns="" cny="" co-authored="" co-developed="" co-endorsement.="" co-founded="" co-founder="" co-founders="" co-locate="" co-located="" co-location="" co-owned.="" co-owned="" co-ownership.="" co-signed="" co.="" coa="" coach="" coaches="" coagulants="" coal="" coastlines="" coatings="" code.org="" code="" codebreaking="" coded="" codeing="" codeorg:why:="" coder="" coders="" codes="" coding.="" coding:="" coding="" cognitive="" cohort="" coin="" coincided="" coined="" cold="" coleges="" collaborate.="" collaborate="" collaborated:="" collaborated="" collaborates="" collaborating="" collaboration.="" collaboration.chrissie="" collaboration:="" collaboration:minimal="" collaboration="" collaborations.="" collaborations="" collaborative="" collaborators="" collapse-driven="" collapse.="" collapse="" collapsing="" colleagues="" collect="" collected="" collective="" college="" collisions="" colloquia.="" colloquia="" colombo="" colon="" colonial-era="" colonial="" colorful="" colossus.="" colossus:="" colossus="" colossuscolossus="" colssus="" columbia="" columnist="" colvid="" combines="" combining="" combos="" come="" comes="" comet="" command="" comment="" comments="" commerce="" commercial="" commercialization="" commission="" commitment="" commits="" committed="" committee="" common-sense="" common="" commonly="" communicates="" communication.="" communication.i="" communication="" communications="" communist="" communities:what="" communities="" community-to-community:="" community-to-community="" community.="" community="" comoetition="" companies.="" companies:="" companies:tsmc:="" companies="" company.="" company:="" company="" companybaidu="" companybyd="" comparable="" comparative="" compare.einstein="" compare:huawei="" compare="" compared="" compares="" comparing="" comparison:="" comparison="" compelling="" compete="" competes="" competing="" competition:="" competition="" competitive="" competitiveness="" competitors.="" competitors.connecting="" competitors:="" competitors="" complement="" complementing="" complete="" completeness="" completing="" complex="" complexity.="" complexity:="" complexity="" compliance="" complicate="" complied="" comply="" components="" compound="" compounds:="" compounds:cinnamaldehyde:="" compounds:curcumin:="" compounds:eugenol:="" compounds:myristicin:="" compounds:piperine:="" compounds:shikimic="" compounds="" comprehensive="" compression:math:="" compression="" comprising="" compromised="" computability.="" computability="" computable="" computation.2.="" computation.="" computation:="" computation="" computational.="" computational="" computationally="" computations="" compute.="" compute="" computer-based="" computer.="" computer="" computers.="" computers:context:="" computers="" computex="" computing.="" computing.did="" computing:="" computing="" computingcore="" concept.="" concept.turing="" concept="" concepts.="" concepts="" conceptual="" conceptually="" concern.="" concern:="" concern="" concerned="" concerning="" concerns.="" concerns="" concise="" concisely="" concluding="" conclusion:="" conclusion="" conclusionagreement:="" conclusionchinese-language="" conclusioncitations="" conclusioneinstein="" conclusionenglish-language="" conclusiongreatest="" conclusionnutmeg:="" conclusionnvidia="" conclusionphotonics="" conclusionq="" conclusionquiz:="" conclusionresources:china="" conclusionstarlink="" conclusionsubjects="" conclusionterm:="" conclusiontop="" conclusiontraining="" conditions="" conducted="" coney="" conference="" conferences="" confidence="" confined="" confirm="" confirmations="" confirmed.="" confirmed="" confirming="" confirms="" conflation="" conflicts="" conform="" confusing="" confusion.="" confusion.for="" confusion:="" confusion:focus="" confusion="" congestion.="" congratulated="" congruent="" connect="" connected="" connecting="" connection.="" connection.connecting="" connection:="" connection:deep="" connection:league="" connection="" connections.="" connections="" connectionsthe="" connectir="" connectivity.="" connectivity:why="" connectivity="" connector.="" connectors="" connects="" connotations.="" connotations="" cons:="" cons:ambiguity:="" cons:corporate="" cons:personalization="" cons="" consciousness="" consensus-driven="" consensus="" consequence:="" consequence="" consequences:visa="" consequences="" conservation.="" conservation="" conservative="" consider="" considerationspotential="" considered:="" considered="" considering="" consistent="" consolidated="" consortium="" constant="" constellation="" constellations="" constituency="" constrained="" constraints.="" constraints.addressing="" constraints:mandate:="" constraints="" construction.="" construction="" consult="" consultant="" consulting="" consumer-driven="" consumer-focused.="" consumer="" contact.="" contact="" contains="" contendeer="" contender.="" contender="" content.="" content:="" content="" contentious="" context-aware="" context.="" context.unicef="" context:="" context:ai="" context:at="" context:e="mc²" context:in="" context:league="" context:noem="" context:technocracy="" context:the="" context="" contexts.="" contexts:einstein="" contexts:turing="" contexts="" contextualize="" contextwhy="" contextyour="" continental="" continuation="" continued="" continues="" continuing="" continuity:="" continuity:the="" continuous="" contraction="" contracts="" contradicts="" contrary="" contrast="" contrasted.="" contrasted="" contrasting="" contrasts="" contribute="" contributed="" contributes="" contributing="" contribution:="" contribution:the="" contribution="" contributions.="" contributions.connecting="" contributions.von="" contributions:="" contributions:cwi="" contributions="" contributionsalbert="" contributor="" control="" controlled="" controlling="" controls="" controversial="" controversies.="" controversies:="" controversies="" converge="" convergence="" conversational="" conversations.="" conversion="" conversions="" converts="" convolutional="" cooking="" cool:="" cool="" cooler="" cooling:="" cooling="" cooperation.4.="" cooperation.="" cooperation:="" cooperation="" cooperatively="" cooperatives="" coordinate="" coordinated="" coordinates="" coordinating="" coordination="" coordinator="" copenhagen="" copper="" coputing="" copuyright="" copying="" core-="" core-deep-maths.="" core-deep-maths.core="" core-deep-maths:="" core-deep-maths="" core.="" core="" cores="" cornerstone="" coropration.="" coropration="" corporate-linked="" corporate="" corporates="" corporation="" corporations="" correct="" corrected="" correcting="" correction:="" correction="" corrections.="" correctly="" correspond="" correspondence:scishow="" corresponding="" corresponds="" cosmetic-pharma-bioenergy="" cosmetic="" cosmetics:="" cosmetics="" cosmological="" cosmology.="" cosmology:="" cosmology="" cosmos="" cost-effectice="" cost-effective="" cost="" costly="" costs.="" costs="" cotton="" could="" couldn="" couldreportaidiagnosticsandxenotransplantation="" coumarin="" council="" counter.="" counter="" counterarguments:="" countering="" counteringnoem="" counterpoint:="" counterpoint="" counters="" countries.="" countries="" country.="" counts="" county="" course="" courses="" court="" cousin="" cover="" coverage:2020="" coverage:="" coverage="" covered="" covering="" covers="" covid-19="" covid="" cpu="" cpus="" crash="" crashcourse.="" crashcourse:ai="" crashcourse:videos="" crashcourse:why:="" crashcourse="" crashes="" creams="" create="" created="" creates="" creating="" creation="" creativity="" credible.="" credible="" crises="" crisis="" crispr-ai="" crispr-based="" crispr-cas9="" crispr-edited="" crispr-enhanced="" crispr="" criteria:bloomberg="" criteria="" critical.="" critical="" critically="" criticism:="" criticism="" criticized="" critics="" critiical="" critique.="" critique:="" critique="" critiqued.="" critiqued="" critiques.="" critiques="" critiquing="" crop="" crops.="" crops:="" crops:baobab="" crops="" cross-border="" cross-check="" cross-disciplinary="" cross-reference="" cross-referenced="" cross-shareholding="" crossover="" crossovers="" crowded.="" crowded="" crowding="" crucial="" crunches="" crusader="" crushing="" cryptography="" crystal="" cs.="" cs="" csl="" ctc="" ction:="" cuda="" culinary="" cultivated="" cultivation.="" cultivation:="" cultivation="" cultural="" culture:="" culture="" cultures="" cumulative="" curated="" curb="" curcumin-based="" curcumin-mrna="" curcumin.="" curcumin="" cures="" curie="" curing="" curiosity.="" curiosity="" curious="" current="" currently="" curricula.="" curricula.web:0="" curricula:track="" curricula="" curriculum-aligned="" curriculum-focused="" curriculum-friendly="" curriculum-ready="" curriculum="" curry="" curvature="" custom="" customer="" cut="" cutoff="" cuts:="" cuts="" cutting-edge="" cutting="" cwi.="" cwi:="" cwi="" cyber-neuroscience="" cybernetics="" cycle="" cycles.="" cycles="" cyera.="" d.c.="" d="" daily="" dance="" dandelion="" dandelions.="" dandelions="" daniel="" danquah="" dans="" dansonia="" dartmouth="" darwin="" data-driven="" data.="" data.context:="" data.is="" data:="" data:photonics="" data:qualification:="" data:sensors="" data="" database.="" database="" databases="" databiotech="" databricks="" datasets.="" datasets="" datbrcks="" date.="" date:="" date="" dave="" david="" davis="" day="" daya="" days.="" days="" dc.="" dc="" dcd="" dditional="" de4emo="" de="" dead="" deadliest="" deadlines="" deal-maker="" deal.="" deal:="" deal="" deals="" death="" deaths:="" deaths="" debate:="" debate="" debated="" debates="" debiasing="" debunks="" dec="" decade="" decades="" december="" decision-making="" decision="" decisions="" decisive.="" declaration="" decline="" declined="" decodes="" decoding="" dedicated="" deduced="" deemed="" deep="" deepened="" deeper:global="" deeper="" deepest="" deepet="" deeply="" deepmind="" deepminds="" deepseek="" deepvacpred="" deere="" defeating="" defended="" defending="" defense.="" defense:="" defense="" define="" defined="" definition="" definitive="" defy="" degree="" degrees="" dei-aligned.="" dei-aligned="" dei-centric="" dei-focused="" dei-influenced="" dei:taiwan="" dei:the="" dei:unesco="" dei="" dejean="" del:="" del="" delay="" delaying="" delays.="" delays="" delhi.="" delhi="" deliveries="" delivering="" delivery="" dell="" delta:="" delta="" delves="" demand="" demanded="" demands.="" demands="" demis="" demo="" democrat="" democratize="" democrats="" demonstrate="" demonstrated="" demonstration="" demos="" denmark="" denotes="" dense="" denser="" density="" dental="" dentifying="" department="" departments="" departure="" depend="" depended="" dependence:ai:="" dependence:biotech:="" dependence:personalized="" dependence="" dependency.="" dependent="" depends="" depletion="" deploy="" deployed="" deploying="" deployment.="" deployment="" deployments.="" deployments="" deportation.="" deportation="" deportations="" depression="" depth.="" depth.proposed="" depth="" der="" deregulates="" deregulation.="" deregulation="" derivation="" derive="" derived="" descent="" describe="" described="" describes="" describing="" description="" descriptor:instead="" descriptor="" desert="" design.="" design:="" design:semiconductors:="" design="" designed="" designer="" designers="" designing="" designs.="" designs:="" designs="" designsthe="" designsyou="" desire="" despite="" destroy="" destroying="" destruction.="" destruction="" detached="" detail="" detailed="" details.="" details:the="" details:what="" details="" detect="" detecting="" detection.="" detection:="" detection="" detective="" detector="" detects="" deter="" determinism.="" determinism="" deterministic="" deterring="" develop="" developed="" developing="" development.="" development="" developments="" develops="" devices.="" devices="" devil="" devote="" devoting="" devotion="" dgx="" dhabi="" dhows="" dhs.="" dhs="" diabetes="" diabetic="" diagnostic="" diagnostics.="" diagnostics:="" diagnostics="" dialogue.="" dialogue="" dialysis="" diarrhea="" diarylnonanoid="" diarylnonanoids="" diaspora="" dice="" dictator="" dictionaries="" did.="" did="" didn="" die="" died="" difference="" differences:="" different="" differential="" differing="" differs="" difficicult="" difficult="" diffusion="" dig="" digestion="" digestive="" digit="" digital="" digitally="" digitata="" digotally="" dilation.="" dilation="" dilink="" dilute="" diluted="" dilutes="" diminishing="" dinner="" dip0lomacy="" diplomacy-relevant="" diplomacy.="" diplomacy.quiz:what="" diplomacy.setup:access:="" diplomacy.what="" diplomacy:="" diplomacy:amsterdam="" diplomacy:balachandran="" diplomacy:bloomberg="" diplomacy:follow="" diplomacy:history:1978="" diplomacy:hopkins="" diplomacy:nairobi="" diplomacy:siddhant="" diplomacy:track="" diplomacy="" diplomacythe="" diplomat="" diplomatic="" diplomats.="" diplomats:="" diplomats="" diplomatsto="" dirac="" direct.="" direct="" directed="" directionally="" directly.="" directly="" director="" directs="" disabilities="" disability="" disarmament="" disciplinary="" disciplines.="" disciplines="" disconnect:="" disconnect:einstein="" disconnect:newton="" disconnect="" disconnectyou="" discourages="" discourse.="" discourse="" discoveries="" discovering="" discovery.="" discovery:="" discovery="" discretion="" discrimination.="" discuss:="" discuss="" discussed.="" discussed:focus:="" discussed="" discusses="" discussing.="" discussing="" discussion.current="" discussion:="" discussion:cic="" discussion:turing="" discussion:von="" discussion="" discussions.="" discussions="" discussionyour="" disease="" diseases.="" diseases="" disengagement:="" disengagement="" dishes="" disillusioned="" dismantle="" dismantling="" dismissing="" disney-="" disney="" disparities.="" disparities:="" disparities="" displaced="" display="" displays:="" displays="" disputes="" disquisitiones="" disrupting="" disruption:="" disrupts="" dissolution:the="" dissolution="" dissolved="" distance="" distanced="" distillation="" distilleries="" distinct="" distinction.="" distinction="" distinguishes="" distinguishing="" distribution:="" distribution:focus:="" distribution="" distributions="" distrust="" dit="" dive="" diverged:einstein:="" diverged="" diverse="" diversifies="" diversify="" diversity:="" diversity="" divert="" diverting="" divide-and-conquer="" divide:="" divide="" divides.="" divides="" division="" dna="" do.="" do:="" do="" doable="" dock="" doctor="" doctors="" document="" documentation="" documented="" documents="" does:="" does="" doesn="" doesnt="" doge="" dogmatic="" doha="" doing="" dolar="" dollar="" dollars.="" dollars="" domain="" domestic="" dominance.="" dominance="" dominate.="" dominate="" dominated="" dominates="" dominating="" don="" donald="" done="" dont="" doors="" doses="" double-check="" double-checked="" double="" doublechech="" doudna="" doug="" down.newton="" down.where="" down="" downgraded="" download="" downloading="" downloads="" downplay="" downtown="" draft="" drain.="" drain="" draw="" drawing="" drawn="" dream:="" dream="" dried="" drive:="" drive="" driveai="" driven.="" driven="" driver.="" driver.evaluating="" driver="" driverless="" drivers.="" drivers="" drives="" driving.details:company:="" driving="" drone-based="" drone="" drones.="" drones:="" drones="" dropped="" drought-resistant="" drought-tolerant="" drought="" droughts="" drove="" drug.="" drug="" drugs.="" drugs="" drunk="" dry="" dsfederal.com="" dsfederal="" dth="" dubai="" due="" duer="" during="" dutch="" dvanced="" dvice="" dwarfed="" dyeing="" dying="" dynamic="" dynamics.="" dynamics.for="" dynamics:="" dynamics="" dynamicsmisunderstanding:issue:="" dynasty="" e-commerce="" e.g.="" e:="" e="" each="" eal-time="" earlier="" early.="" early="" earned="" earning="" earth.="" earth:="" earth:you="" earth="" easier="" east="" easy-to-read="" easy="" eb:0="" eb:10="" eb:12="" eb:14="" eb:15="" eb:16="" eb:17="" eb:18="" eb:19="" eb:1="" eb:20="" eb:21="" eb:22="" eb:23="" eb:25="" eb:2="" eb:3="" eb:4="" eb:5="" eb:6:="" eb:6="" eb:7="" eb:8="" eb:9="" eb:ai="" eb="" ebruary="" ec="" ecap="" ecember="" ecent="" ech="" echat="" echecked="" echo="" echoed="" echoes="" echoing="" echoingturing="" economic="" economics.="" economics="" economy.="" economy="" ecosystem.="" ecosystem:="" ecosystem:cwi="" ecosystem="" ecosystems.="" ecosystems:="" ecosystems:attend="" ecosystems:join="" ecosystems="" ecosystemsyou="" ecretary="" ecun:="" eczema="" ederal:="" ederal="" edex="" edge.="" edge:="" edge:edge="" edge="" edit="" editing:="" editing="" edits="" edt.="" edt="" edtech="" educate="" education.="" education.why="" education:="" education:active="" education:ai="" education:bookbot="" education:born="" education="" educational="" educator="" educators.="" educators="" educed="" edvac="" edward="" ee="" eens:="" eens="" eensyoutube:="" eep-maths:strength:="" eep-maths="" eep="" eepmind="" eepseek="" effect:="" effect="" effective="" effectively="" effects.="" effects:graduation="" effects="" efficacy="" efficiency.="" efficiency:="" efficiency:your="" efficiency="" efficient="" efficiently="" effort="" efforts.="" efforts.unicef="" efforts:the="" efforts="" efinition:="" efore="" eg="" egcg="" egypt="" ehr="" eibo="" eight="" eijing="" eines="" einstein-ai="" einstein-de="" einstein-inspired="" einstein-turing="" einstein.="" einstein:="" einstein:conceptual="" einstein:switzerland="" einstein="" einsteins="" einsytein="" eio-cmu="" either="" el="" elativistic="" elativity="" elderly="" elected="" electric="" electrical="" electricity="" electrified="" electrodynamics="" electrolux="" electromagnetism.="" electromagnetism="" electron="" electronic="" electronics.="" electronics:="" electronics="" electrotechnical="" elegance="" elements:conceptual="" elements="" elenbranswell="" elephant="" elesat="" elf-driving="" eliminate="" eliminating="" elite="" ellis="" elon="" elonmusk="" elow="" else="" elsewhere--="" elsewhere="" email="" embassies:="" embassies="" embassy="" embodied.="" embodied="" embodies="" embody="" embrace="" embraced.="" embraced="" embroiled="" embryonic="" emerged.the="" emerged="" emergencies.="" emergency="" emerges="" emerging="" emiconductors="" emigrate="" emigrated="" emigration:="" emigration="" emil="" emission="" emissions="" emocrat="" emollient="" emotional="" emperor="" emphasis="" emphasize="" emphasized.="" emphasized="" emphasizes="" emphasizing:unseen="" emphasizing="" emphis="" empires="" empirical="" employees="" employs="" empower="" empowered="" empowering="" empowerment.="" empowerment:lord="" empowers="" empowersyoungdiplomatstocodediagnostictools="" empty="" en.unesco.org="" en="" enable="" enabled="" enablement="" enablers="" enables="" enabling:genomic="" enabling="" encompassed="" encompasses="" encourage="" encouraged="" encouragement="" encourages="" end="" ended="" ending="" endorsed.="" endorsements="" ene="" enegal="" energieinhalt="" energises="" energize="" energized="" energy-efficient="" energy-intensive="" energy-mass="" energy-tech="" energy.2.="" energy.="" energy:="" energy="" enesco="" enetics="" enforce="" enforcement="" enforcing="" engage:="" engage="" engaged="" engagement.="" engagement:="" engagement="" engages="" engaging.="" engaging:="" engaging:look="" engaging="" engine="" engineer="" engineering-driven="" engineering-focused="" engineering.="" engineering.summaryhuang="" engineering:einstein="" engineering:newton="" engineering:tsmc="" engineering="" engineerings="" engineeringyou="" engineers.="" engineers="" engines:what="" engines="" england="" english-language="" english.="" english="" englishfor="" enhance="" enhanced="" enhancement.="" enhancement="" enhances="" enhancing="" eniac="" enigma="" enmark="" ennessee="" enough="" enriches="" enroll="" enrolling="" enrollment="" enrolls="" ensen="" ensors:="" ensure="" ensured="" ensures="" ensuring="" entanglement.="" entente="" entering="" enterprise="" enthusiasm="" enthusiasts="" entire="" entirely="" entities="" entity.="" entity="" entral="" entry="" entscheidungsproblem="" environment="" environmental="" environmentalists="" environments.="" envision="" envisioned.="" envisioned="" enya="" enzymes="" eo="" eoverse="" episode.="" episode="" episodes.="" episodes="" epistemology:="" epistemology="" epr="" eptember="" epublican="" equality="" equally="" equals="" equation="" equations.="" equations:="" equations="" equipment="" equipping="" equitable="" equity-focused="" equity.="" equity.summary:="" equity:="" equity="" equivalence.="" equivalence:="" equivalence="" equivalent="" equivalents="" era.="" era.neural="" era:="" era="" eraly="" eras="" erck="" ergodic="" eric="" eritasium="" ernst="" erode="" eroding="" erosion="" error="" errors="" es="" escalates="" escription:="" eshaun="" esla="" especcially="" especially="" essay="" essee:="" essential.="" essential:qualification:="" essential="" est="" established="" establishing="" establishment="" estate="" et="" eta="" etails:="" etc.="" etc="" eterministic:="" ethical="" ethics.4.="" ethics.="" ethics:="" ethics:unesco="" ethics="" ethnopharmacology="" ethos.2.="" ethos.="" ethos="" eu-africa="" eu-asean="" eu-china="" eu-style="" eu-taiwan="" eu-u.s.="" eu.="" eu:="" eu:the="" eu="" euclid="" euclidean="" eugenol="" eural="" euralink="" europe.="" europe="" european="" eutelsat="" ev-focused="" ev="" evaluate:cic="" evaluate:einstein="" evaluate:mathematical="" evaluate:unesco="" evaluate="" evaluating="" evaluation:strengths="" even="" event.what="" event="" events.="" events:="" events="" ever="" every="" everyone.="" everyone="" everything="" everywhere="" evidence:="" evidence:no="" evidence:reuters="" evidence="" evident="" evised="" evoke="" evolution="" evolve="" evolved="" evolves="" evolving="" evs="" ews="" ewton="" ex-prodigy="" ex-prot="" exabyte-scale="" exabytes="" exacerbate="" exacerbated="" exacerbates="" exacerbating="" exact="" exagamglogene="" exaggerate="" examination="" examinationwhile="" examine:atomic="" examine="" examining="" example.="" example:="" example:f="ma" example:in="" example:nanotechnology="" example:newton="" example:waymo="" example="" examples:="" examples="" excellence.="" excelling="" excels="" exchange.="" exchange="" exchanges="" excited="" exciting="" excluded="" excluding="" exclusivity="" exec="" executing="" execution.="" execution="" executive="" exhibit="" exhibition="" exhibitors="" exhibits="" exisiting="" exist="" existed="" existing="" exists="" exit="" expand="" expanded:="" expanded="" expanding="" expands="" expansion.="" expansion="" expelling="" expels="" expensive="" expereintial="" experiemced.="" experience.="" experience="" experienced.="" experiential="" experiment="" experimenting="" experiments="" expert="" expertise.="" expertise="" experts="" explain="" explainable="" explained:="" explained="" explaining="" explains="" explanation:="" explanation="" explanations:orbital="" explanations:web:0="" explanations="" explian="" explicitly="" expliocity="" exploding="" exploitation="" exploited="" exploration="" explore:neural="" explore:taiwan="" explore="" explored="" explorer="" explores="" exploring:="" exploring="" explosive="" expo.scsp.ai="" expo="" expoenential="" expoenetial="" expoenetially="" exponential="" exponentially="" exponentials.="" exponentials:="" exponentials="" export="" exporting="" exports.="" exports="" exposed="" exposure="" express="" expressed="" expressing="" expulsion="" expulsions:einstein="" expulsions="" extend="" extended="" extending="" extends="" extennt="" extensive="" extent="" extract="" extracted="" extraction="" extracts="" extraordinary="" extricate="" extricated="" extrirdinary="" ey="" eye="" eyes="" f-1="" f="" fab="" fabricates="" fabrication="" face="" facebook.com="" facebook:="" facebook="" faced="" faces="" facilitate="" facilitated="" facilitating="" facilitative="" facilitator="" facilities="" facility="" facing="" fact:="" fact="" factiries="" factor.="" factor="" factorial:="" factories.="" factories="" factors.="" factors="" factory.="" factory="" factorythe="" facts="" faculty="" fail="" failed="" failing="" fails="" failure="" failures.="" failures="" fair.="" fair="" fairness="" fairtrade="" fall="" falls="" false="" falsifiable="" falsification="" faltered="" fame.="" fame="" familiar.="" familiar="" family="" famous="" fantastic="" fao="" far="" farm="" farma.="" farmer="" farmers="" farming.="" farming:="" farming="" farms.="" farms="" fascinating="" fascist="" faso-eu="" faso="" fast-charging="" fast.="" fast="" faster.="" faster="" fastest="" fatigue="" favor="" favored="" favoring="" favorites="" favors="" fda="" fear.="" fear="" fears.="" fears.i="" fears:="" fears="" feasible.="" feasible="" feat="" feature.="" feature="" features:="" features:speed:="" features="" featuring="" feb="" february="" fed="" federal="" federalist="" federally="" feed="" feedback-driven="" feedback="" feeding="" feeds="" feel="" feels="" fei-fei="" fell="" fertility="" fertilizer="" fewer="" fiber="" fictional="" field="" fields.="" fields.i="" fields="" fifth="" fight="" fighting="" figure="" figurehead="" figures.="" figures:john="" figures="" file="" filed="" fill="" filling="" films="" final="" finally="" finance.="" finance="" financial="" finches="" find="" finding="" findreams="" finds="" fine="" finite="" finland="" finshed="" fintech="" fiona="" fir="" firm="" firms.="" firms="" first-mover="" first-principles="" first-term="" first.="" first="" fission="" fit:="" fit="" fits="" fitted="" fitting="" five-year="" five="" fix="" fixation="" fixes.="" fixes="" fixing="" fizer="" flair="" flavonoids="" flaws="" fled="" fleeing="" fleets="" flexner="" flies="" flips="" flooding="" florida.="" flourishes.="" flow="" flows.="" flows="" flu="" fluvaccines="" flying="" focus.1.="" focus.="" focus:="" focus:cover="" focus="" focused="" focuses="" focusing="" folding:="" folding="" folklore="" folks="" follow-up="" follow:="" follow:demis="" follow:siddhant="" follow="" followed="" followers.="" followers="" followi="" following="" food="" foods="" footage="" for.="" for:biotech:="" for:communication:="" for:relativistic="" for="" forbes.="" forbes.english-language="" forbes="" force.="" force="" forced="" forces="" forcing="" ford="" forecast="" forecasting="" forecasts.="" forecasts="" foreign="" foresee="" foreshadowing="" foreshadows="" form="" formal="" formalization="" formalized="" formalizing="" formats="" formed="" former="" forms="" formula="" formulation="" formulations="" fortunately="" fortune="" forum.="" forum="" forums.="" forums:="" forums="" forward-looking="" forward.="" forward.summarywill="" forward="" forwardarm="" fossil="" foster="" fostered="" fostering="" fosters="" found="" foundation.3.="" foundation="" foundational.="" foundational="" foundations.="" foundations:theoretical:="" foundations="" founded:acorn="" founded="" founding="" four="" fox="" foxconn="" fractal="" fractures="" fragrances="" fragrans="" frame="" framed="" frames.="" frames="" framework.="" framework:="" framework:classical="" framework:newton="" framework:u.s.="" framework="" frameworkoverview:="" frameworks.="" frameworks="" framing="" france="" francisco="" francophone="" frank="" frankfurt="" fraud="" free.="" free="" freedom="" french:="" french="" frequency="" frequently="" frica:="" frica="" frican="" friedmann="" friend="" friendly="" friends="" frined="" from="" frontiers="" fruit="" fruitful="" fruiting="" frustrated="" frustrating="" fsd="" ft="" fu="" fuel="" fueled="" fueling="" fuels="" fugaku="" fuld="" fulfill="" fulfilling="" fulfills="" full="" fully="" fun="" function.="" function="" functional="" functions="" fund="" fundamental="" fundamentally="" funding.="" funding:="" funding="" funds="" furber="" further.="" further="" fusarium="" fusion="" future-proof="" future:="" future="" futures.summarycities:="" futures="" fyllo="" g20="" g42="" g7="" g="" gac="" gadget="" gadgets="" gag="" gain="" gaining="" gains="" gallons="" game-changer.="" game-changer="" game-changers="" game.="" game="" games="" gamified="" gaming="" gap.="" gap:="" gap="" gaps.="" gaps:="" gaps="" garners="" gas="" gateway="" gateways="" gauss="" gavi="" gaza="" gb200="" gb="" gbetgo="" gbps="" gdp:="" gdp="" gdpr="" ge-appropriate:15-year-olds:="" geely="" geforce="" gen="" gender="" gene="" genentech="" general="" generalization="" generate="" generated="" generates="" generating="" generation="" generations:="" generations="" generative="" generic="" genes.="" genes="" genetic="" genetics.="" genetics="" geneva-based="" geneva.="" geneva="" genius="" genome="" genomes.="" genomes="" genomic="" genomics.="" genomics="" geo="" geoffrey="" geographic="" geographical="" geography="" geometry.="" geometry="" geopolitical="" geopolitics="" george="" geostationary="" gerd="" german="" germany="" get="" gets="" getting="" ghana="" gheit="" giant="" giants="" gilbert="" gimletmedia.com="" girl="" girlfriend="" github="" give="" given="" gives="" giving="" global-local="" global.="" global="" globalization="" globalized="" globally.="" globally="" globe.="" gloves="" glucose="" glucosinolate="" glucosinolates="" glue.="" glue:="" glue="" gm="" gmo="" go.="" go="" goal="" goals.="" goals:="" goals="" god="" goes="" going="" gold="" golden="" goldmine="" gonzague="" good.="" good="" goods="" google.="" google:="" google="" googling="" got="" gov.="" gov="" governance.="" governance="" governed="" governing="" government-driven="" government-run="" government="" governments="" governor="" governs="" govtrack.us="" gow="" gps="" gpt-4o="" gpu-driven="" gpu="" gpus.="" gpus="" grace="" grad="" gradient="" gradual="" graduate="" graduated="" graduating="" graduation="" graininess="" grandfather="" grandfathers:="" grandfathers:turing="" grandfathers="" grant="" granting="" grants="" graph-based="" graph="" graphical="" graphics.="" graphics="" grasp="" gravitation="" gravitational="" gravity="" great.="" great="" greater="" greatest="" greatness.="" greatness:="" green="" greener="" greenhouses.="" greenhouses:="" greenhouses="" greitens="" grenada="" grenade="" grew="" greywater="" gricultural="" griculture="" grid="" grids.="" grids="" grinding="" gritech="" gritechglobal:matches:="" gritechglobal="" grok-powered="" grok.="" grok3="" grok="" grossmann="" ground="" groundbreaking="" grounded="" grounding="" groundnut:="" groundnut="" groundwork="" group="" groupdetails:="" groups="" grow:computing:="" grow="" growing.="" growing="" grows="" growth-focused="" growth-oriented="" growth.="" growth.for="" growth:="" growth:why="" growth="" gruender="" gsat="" gsk="" gslv="" gtu="" guangzhou="" guarantee.="" guardian="" guardrails.="" gubernatorial="" guess="" guesses="" guidance="" guide="" guided="" guidelines="" guides.="" guides:="" guides="" guiding="" guido="" guis="" gunmar="" gunnar="" gym="" gynandra="" gynandropsis="" h-1b="" h100="" h100s="" h200="" h200s="" h5n1="" ha="" haas="" hacking="" had="" hahn="" haldeman="" half="" hall.="" hall="" hallenges:="" halley="" hallucinations="" hamiltonian="" hammer="" han="" hanaway="" handle="" handled="" handles="" handling="" hands-on:="" hands="" handwringing="" hanoi.="" hanoi="" hansepepper="" happened="" happeneed="" happenings="" happy="" harass="" harbor="" hard:="" hard="" harder="" hardware.="" hardware:="" hardware="" harness="" harry="" harvard-educated="" harvard.="" harvard="" harvesters.="" harvesters="" harvesting.="" harvesting="" has="" hase="" hasen="" hasn="" hassabis.="" hassabis:="" hassabis="" hat="" hathaway="" hauser="" have="" having="" hbb="" hbm3e="" he:directed="" he="" head="" heading="" headquartered="" headquarters:="" headquarters="" headrick="" headset="" heaisummit="" healing="" health.="" health="" healthcare.="" healthcare:="" healthcare="" healthy="" hear="" hearing="" heart="" heat="" heavy="" heisenberg="" held="" helen="" hello="" help:memphis:issue:="" help="" helped="" helper="" helping="" helps="" helsinki="" hemagglutinin="" hemoglobin.="" henri="" hensley="" henzhen="" heory="" hepatotoxicity.="" her.="" her="" herbal="" here.="" here="" heres="" heritage="" hermann="" hero="" heroes="" heuristic="" hevea="" hey="" hi-trust="" hich="" hidden="" high-capacity="" high-dimensional="" high-energy="" high-level="" high-paying="" high-potential="" high-risk="" high-speed="" high-tech="" high-trust="" high-visibility="" high-yield="" high.="" high="" higher="" highest.="" highest="" highlight="" highlighted.="" highlighted="" highlighting="" highlights="" highly="" hilbert="" hilosophical="" him="" himself="" hina-taiwan="" hina:="" hina="" hinascience="" hinder="" hindered="" hindering="" hinders="" hindi="" hine="" hinese="" hinton="" hiring="" hiroshima="" his="" historians="" historical="" historically="" histories="" history:1978="" history:="" history="" histry="" hit="" hite="" hitler="" hiv="" hiwever="" ho="" hobby="" hodges="" holdings="" holdingsarm="" hole="" holes="" holidays="" holistic="" hollywood="" holmes:why:="" holmes="" home="" homeland="" homes="" hometown="" homework="" homg="" honest="" hong="" honing="" hook="" hookup="" hoots:why:="" hoots="" hope="" hoped="" hopes="" hopfield="" hopkins="" horizon="" hort="" hospital="" hospitalizations="" hospitals.="" hospitals="" host="" hosted="" hosting="" hosts="" hotonics="" hotspot="" hotspots="" hours="" house="" housed="" household="" how:what="" how="" howard="" however="" hplc="" hq="" hr="" hrarard="" hree="" hrl="" hsinchu="" http:="" https:="" huang-nvidia="" huang-tsmc="" huang.="" huang:="" huang:why:="" huang="" huawei="" hub.="" hub="" hubble="" hubs.="" hubs.summary45="" hubs="" huge="" hughes="" hughesnet="" huimo="" human-centered="" human-centric="" human-like="" human-rights-based="" human-robot="" human="" humana="" humanism.="" humanism="" humanistic="" humanitarian="" humanity="" humanized="" humanizing="" humanoid="" humans.="" humans="" humbled="" humor.="" hunags="" hundreds="" hungarian-jewish="" hungary="" hunger="" hungry="" hunt="" hunts="" hurdles="" hy:="" hy="" hybrid="" hydration="" hydrodynamics="" hydrogels="" hydrogen="" hydroponics="" hype="" hyped:="" hypotheses.="" hypotheses="" hypothesis.="" hypothesis="" hypothesise.="" hypothesize.="" hypothesize="" hypothesized="" hypothesizing="" hysical="" hyundai="" i.="" i.e.="" i="" ia="" ias-campus="" ias:proximity:="" ias="" iashan="" ibm="" icious="" icivics="" icon.="" iconic.="" iconic="" icrogrids="" ict="" id.="" id:="" id="" idar:="" idea:="" idea="" ideal="" ideals.="" ideas.="" ideas="" identification="" identified="" identifies="" identify="" identifying="" identity.if="" ideological="" ideologies.="" ideologies="" ideology.="" ideology="" ids="" idu="" ie="" iec:="" iec="" ieee="" ietnam="" if="" ig="" igeria="" ight="" igital="" igna="" ignal="" ignificance="" ignore="" ignoring="" igyanshala="" ii.="" ii="" iii="" iita="" iita_cgiar="" ilibili="" ilicon="" ilimo="" ilk="" illegal="" illiteracy="" illness="" illumina="" imact="" image="" imagenet="" imagery="" images="" imagination.="" imagination="" imaginative="" imagine="" imaging="" immediacy:="" immediate="" immense="" immersed="" immigrant-driven="" immigrant-friendly="" immigrant="" immigrants.="" immigrants="" immigration="" imminent="" immune="" immunity="" impact.="" impact.details:company:="" impact:="" impact:international="" impact:mass-energy="" impact="" impacted="" impacteuropean="" impactful.="" impactful="" impacting="" impacts.1.="" impacts.web:1="" impacts="" impactyou="" imperial="" imperialist="" implants="" implementation.="" implementation="" implication:direct="" implications.="" implications.autonomous="" implications:="" implications="" implicationsyour="" implied="" implies="" implosion="" imply="" implying="" important.="" important="" importing="" imports="" imposes="" impossibility:="" impossible="" improbable.="" improbable.von="" improve="" improved="" improvement="" improvements="" improves="" improving="" in-car="" in-depth="" in-house="" in-space="" in:chabad="" in:presidential="" in="" inability="" inaccessible="" inaccuracies.clarifying="" inaccurate="" inadvertently="" inagintaions="" inaugurated="" inayobadilisha="" inc.="" incapable="" ince="" incense.="" inception:="" inception="" incidents="" include:="" include:accessible="" include:kurt="" include:new="" include:nuclear="" include:precision="" include:ted="" include="" included.="" included.evaluating="" included.interpreting="" included.the="" included.understanding="" included.why="" included="" includes="" including:ford="" including="" inclusion="" inclusive="" inclusivity.="" inclusivity:="" inclusivity:chinese="" inclusivity:cover="" inclusivity:swahili="" inclusivity="" incompatible="" incomplete="" incompleteness="" inconclusive="" incorporate="" incorporating="" increase="" increases="" increasing="" increasingly="" indable="" inded="" indeed="" independence="" independent:="" independent="" independently.="" independently="" index="" indi="" india-africa="" india-eu="" india.="" india:="" india="" indiaai.="" indiaai.gov.in:why:="" indiaai="" indian="" indicates="" indices="" indigenous="" indirect.="" indirect="" indirectly="" individual.="" individual="" individuals="" indonesia-eu="" indonesia="" indonesian-dutch="" indonesian="" inducing="" industrial="" industrialized="" industries="" industry-academia="" industry-backed="" industry-wide="" industry.="" industry="" inear="" ineffectiveness="" inefficiencies="" inequity="" inertia="" inertial="" infancy="" infants.="" infants="" infectious="" infer="" inference.="" inference="" inferred="" inflammation="" inflated="" inflows.="" influence.="" influence:="" influence="" influenced="" influences="" influencing="" influential.="" influential="" influenza="" info.="" info.ownership="" info.what="" info="" infographic="" infographics.="" infographics="" inform="" informal="" informally="" informatica="" information.="" information.recheck:="" information.why="" information="" informed="" informing="" informs="" infrared="" infrastructure-free="" infrastructure.="" infrastructure="" ingredients="" inherited="" inheriting="" inhibition="" inhua="" initial="" initially="" initiative="" initiatives:recommendation="" initiatives="" injunction="" innamomum="" innovate.="" innovate="" innovation-driven="" innovation.1.="" innovation.="" innovation.key="" innovation.recursion="" innovation:="" innovation:musk="" innovation="" innovationcore="" innovations="" innovative="" innovators.="" innterest="" input="" inputs="" inquire="" inquiry="" insecurity="" inside="" insight:="" insight="" insightful="" insights.="" insights:="" insights:what="" insights="" insilico="" insists="" inspiration="" inspirations="" inspire="" inspired.i="" inspired="" inspires="" inspiring.="" inspiring="" instability.="" instability="" instagram="" instance:energy="" instance="" instantly.="" instantly="" instead="" instein="" instinct="" institute.="" institute="" institutes="" institutional="" institutions="" instruction="" instrumental="" insufficient="" insular="" insulin-like="" integral="" integrate="" integrated="" integrates="" integrating="" integration.="" integration="" integrationeinstein="" integrity="" integrtation="" intel="" inteligent="" intellect="" intellectual="" intellectually:study="" intellectually:use="" intellectuals="" intelleigencve="" intelligence.1.="" intelligence.="" intelligence.einstein="" intelligence:="" intelligence:taiwan="" intelligence="" intelligennt="" intelligent="" intelligentran="" intenational="" intended="" intending="" intensifying="" intentional="" inter-satellite="" interact="" interacting="" interaction:ias="" interaction="" interactions.="" interactions="" interactive="" interchangeable="" interclub="" interconnected:="" interconnected="" interconnects="" interconnecxt="" interconvertible="" intercoonnected="" interdependent.="" interdisciplinary="" interest..="" interest.="" interest:="" interest="" interested="" interests.1.="" interests.="" interests25x="" interests:african="" interests:comparison="" interests="" interestsafrican="" interestscwi="" interestsyour="" interfaces="" internal="" internally="" international="" internationalist="" internationl="" internet.="" internet="" interoperability.="" interoperable="" interporet="" interpret="" interpretation:="" interpretation:you="" interpretation:your="" interpretation="" interpretations.="" interpretations="" interpreted="" interpreting="" intersected="" intersections="" intersects="" intervals="" interviews.="" interviews="" interwar="" into="" intricate="" introduce="" introduced="" introduces="" introducing="" introductions.="" intuition="" intuitive="" inuteearth="" invalidating="" invariant="" invented="" inventing="" invested="" investigate:what="" investigate="" investigating="" investigation="" investment.="" investment="" investments:="" investments="" investor="" investors.="" investors:="" investors="" investorsdetails:="" invisible="" invited="" involve="" involved:="" involved="" involvement.connecting="" involvement:="" involvement="" involves:lorentz="" involves="" involving="" iography="" iology="" ios="" iot-based="" iot-driven="" iot="" iotech:="" iotech="" iotechai="" ip="" iper="" iphone="" iplomatic="" ipo="" ipod="" ir="" iran="" irectionally="" iron="" ironically="" irrelevant="" irrigation="" irst="" irtuous="" is:="" is="" isa="" isaac="" isaacson="" iscover="" ision:="" ision="" islands="" isn="" isney="" isoflavones="" isolated="" isolates="" isolating="" isolation="" isomorphics="" isplays:="" israel="" isro="" issue="" issued="" issues.="" issues:="" issues:qualification:="" issues="" istanbul="" istorical="" istory="" istral="" istron:="" it.="" it.evaluating="" it:="" it:for="" it:go="" it:join="" it:search="" it="" italy.="" italy="" itellaria="" iter="" iterate="" iterates="" iterating="" iteration:engineering:="" iteration="" iterations.="" iterations="" iterative="" iteratively="" itler="" its="" itself.addressing="" itself="" itu="" itunes="" ity:="" itys="" iven="" j-1="" j.="" jaguar="" jakarta="" jam="" jan="" january="" japan-africa="" japan:="" japan="" japanese="" jargon-heavy="" jargon.="" jargon="" jarvis="" jatropha.="" jd="" jense="" jensen="" jept="" jersey.="" jesaitis-="" jesaitis.what="" jesaitis:="" jesaitis="" jesaitisbased="" jesaitisprepare:="" jesaitisrole:="" jesaitisto="" jetson="" jewish="" jews="" jiashan="" jindal="" jio="" job="" jobs.="" jobs="" johannesburg="" john="" join="" joined="" joining="" joint="" josh="" joshua="" joules="" journal="" journalism="" journalistic="" journals:="" journals="" journey="" joy="" judge="" judicial="" juice="" julian="" july="" jump="" june="" junior:why:="" junior="" junk:="" junk="" junks="" just="" justice="" justices="" justification:="" k-12="" k="" ka="" karik="" karoline="" kavanaugh="" keen="" keep="" keeping="" keeps="" kemp="" kendra="" kennedy="" kenya-uganda-eu="" kenya-uganda="" kenya="" kept="" keshaun="" kexueribao:why:="" key.="" key="" keynotes.="" keys="" keywords.="" keywords:="" keywords="" kg="9×10¹⁶" khan="" khanacademy.org:why:="" khanacademy:linear="" khanacademy:tutorials="" khanacademy:why:="" khoisan="" kid="" kidney="" kids="" kilimo="" kills:="" kills="" kilometers="" kind="" kinetic="" king="" kleene="" km="" knew="" knighted="" know:arm="" know="" knowing="" knowledge-sharing="" knowledge.="" knowledge="" known="" kok-saghyz="" kong="" korea="" krishi="" kristi="" kuala="" kuiper:="" kuiper="" kunlun="" kurt="" kurzgesagt:matches:="" kurzgesagt:why:="" kurzgesagt="" l="" lab="" labor-intensive="" labor:="" labor="" laboratory="" labs.="" labs:="" labs="" lacebelow="" lack="" lacked="" lacking="" lacks="" lackwell="" lad="" lag.="" lag="" lagged="" lagos.="" lagos="" lags="" laid="" lambda="" lament="" lancet="" land.="" land="" landed="" landing="" landlord="" landmark.="" landmark="" lands="" landsat="" landscape="" langiages="" langone="" language:="" language="" languages:english:="" languages="" languagesai="" lanka-eu="" lanka="" laptop="" laptops="" lara="" large-scale="" large="" largely="" larger="" largest="" larifying="" laser="" lasers:="" lasers="" last="" lasting="" late-stage="" late="" latency.="" latency:="" latency="" later.="" later="" latest="" latex.="" latex="" latin="" lattice="" launch="" launched="" launcher="" launches.="" launches="" launching="" laureate.="" laureate="" law="" lawrence:why:="" lawrence="" laws.="" laws="" lawsuit="" lawyer.="" lawyer="" lay="" layered="" laying="" le="" lead.="" lead="" leader.="" leader="" leaders.="" leaders="" leadership.="" leadership:="" leadership:china:="" leadership:wechat="" leadership="" leading="" leads:="" leads="" leaf="" leafy="" league.="" league="" leanings="" leap:="" leap="" leapfrogged="" leapfrogs="" leaps:="" leaps:arm1="" leaps="" leapsbelow="" leapsyou="" leapt="" learn:="" learn="" learners="" learning.="" learning:="" learning:mathematics:="" learning="" lease.="" leased="" leases="" leasing="" least="" leave="" leaves="" leaving="" leavitt="" lectricity="" lectrolux:="" lecture="" lectures="" lecun.="" lecun:="" lecun="" led:structural="" led="" leds="" lee.="" lee="" left="" legacies.="" legacies.summary="" legacies="" legacy.="" legacy.summarye="mc²" legacy:="" legacy:e="mc²" legacy="" legacyyou="" legal="" legislation="" legos="" legume="" leibniz="" lenet="" length:="" length="" lens:="" lens="" lenses="" leo="" les="" less="" lesser-known="" lesser="" lesson="" lessons.="" lessons="" let="" lets="" letter.="" letter:="" letter="" letters="" letting="" leveillula="" level.="" level="" leverage="" leveraged="" leverages="" leveraging="" leyen="" lfp="" lhc="" li.="" li="" liberal="" library="" license="" licensing="" lidar="" lie="" lies="" life-saving="" life.="" life:born="" life="" lifelong="" lifetime="" lift="" lifting="" light-neuron="" light-powered="" light.="" light="" lighting="" lightning-fast="" lightning="" lights="" lightspeed="" like:nuclear="" like="" likelihood:="" likelihood="" likely="" likes="" likle="" liklihood="" limbo:="" limit.="" limit:="" limit="" limitations:="" limitations:crispr:="" limitations:newton="" limitations="" limitationsnewton="" limited.="" limited="" limiting="" limits.="" limits:="" limits="" limonene="" line="" lineage="" lineages="" linear:="" linear="" linearis="" linearly="" link.switzerland="" link:="" link="" linked="" linkedin="" linking="" linkingtoafricanhubs="" links.="" links="" liquid="" lisa="" lisp="" list="" listed="" listen:="" listeners="" listing:arm="" lists="" lit="" literacy="" literature="" literaure="" lithium="" little="" live="" lived="" livelihoods.="" livelihoods="" lives.="" lives="" livestream="" living="" ll="" llama="" llc="" llicium="" llm="" llms="" lnked="" lobal="" local="" localized="" locals="" locate="" located="" location:="" location="" locationyou="" logged="" logic.="" logic="" logician="" logicians="" logistically="" logistics.="" logistics:why:="" logistics="" lon="" london.="" london="" lone="" long-term="" longa="" longer-lasting="" longer="" look="" looking="" loomberg="" looming="" loop.="" loop:="" loops.="" loops="" lord="" lords="" lorentz="" los="" lose="" losest="" losing="" loss.="" loss:="" loss="" losses="" lot="" lots="" love.="" love="" loves="" low-carbon="" low-income="" low-latency="" low-power="" low-resource="" low-speed="" low="" lower="" lowers="" lowing="" lowry="" lows="" loyalty-based="" loyalty="" lphabet="" lphafold="" lphago="" lse="" ltd.="" luck="" lue="" lumpur="" luna="" lung="" luxury="" lychee="" m1="" m="" mac="" mach="" machine.="" machine="" machinery="" machines-="" machines.="" machines="" machinesdefinition:="" macrae="" macroeconomic="" macroscopic="" macvhine="" made="" maff="" magazine="" magic:="" magic="" mail="" main="" mainframes="" mainly="" maintain="" maize="" major="" majors="" make="" maker="" makerere="" makes="" making="" malabaricone="" malaria.="" malaria="" malaysia="" mali="" malnutrition="" mammogram="" man="" manage="" management="" manages="" managing="" manchester="" mandate="" mandates.="" mandates="" maneuvers="" manhattan="" maniac="" manifesto="" manila="" manipulated="" mankind="" manual="" manufactiring="" manufacture="" manufactured="" manufacturer.="" manufacturer:="" manufacturer="" manufacturing.="" manufacturing:focus:="" manufacturing:why:="" manufacturing="" many="" maoming="" map="" mapconsequently="" mapped="" mapping:="" mapping="" maps.="" maps:="" maps="" mapsyou="" mar="" marcel="" march="" marco="" margin="" marginal="" marginalized="" marie="" maritime="" mark="" marked="" marker-assisted="" markers="" market-driven="" market.="" market:="" market="" marketed="" markets.="" markets="" markup="" mars="" marsha="" mart="" marter="" marths="" marula-based="" marula:="" marula="" marvin="" masayoshi="" maso="" mass-energy="" mass="" massachusetts="" massbio="" massive="" master="" masters="" match.="" match:="" match:scishow="" match="" matched="" matches="" matching.="" matching:="" matching="" materials.="" materials="" math-powered="" math.="" math="" mathematica="" mathematical.="" mathematical="" mathematically.="" mathematically="" mathematician="" mathematicians="" mathematics.="" mathematics:="" mathematics:no="" mathematics="" mathematicsnewton="" mathematicsyou="" maths.1.="" maths.="" maths:="" maths:linear="" maths:strength:="" maths="" mathstitle:="" mathsyou="" mathsyour="" matrices.="" matrices="" matrix="" matter="" matters:="" matters="" mature="" maturity.="" maturity="" max="" maximize="" maxwell="" may="" maybe="" mayer="" mayor="" mazon="" mba="" mbas="" mbed="" mbps="" mc="" mcaleer="" mcaller="" mccarthy="" mcculloch-pitts="" mcculloch="" mcgowen="" mckinsey="" mcmahon="" mcmurdo="" mdma-assisted="" mdma="" me:="" me="" mean="" meaning="" meaningful="" means:harvard="" means:wet="" means="" meant="" meanwhile="" measure="" measures.="" measures:="" mechanical="" mechanics.="" mechanics="" mechanism="" mechanisms="" medi="" media-driven="" media-facing="" media.="" media="" median="" mediate="" mediatek:="" mediatek="" mediates="" mediating="" mediation.for="" mediation="" mediator:="" mediator="" mediators="" medica="" medical="" medicinal="" medicine.="" medicine="" medicines.="" medicines="" medigen.="" meet="" meeting:="" meeting:geographical="" meeting="" meetingprepare:="" meetingrene="" meetings.="" meetings:="" meetings:context:="" meetings="" meetingvince="" meetingyou="" meets="" mega-ai="" mega-campus="" mega-efficient="" melanoma.="" member.="" member="" members="" membership.="" membership:="" memes="" memorandum="" memory="" memos="" memphis:="" memphis:digital="" memphis:role:="" memphis="" men="" mention="" mentioned="" mentioning="" mentor="" mentored="" mentorship.="" merchants="" merck-moderna="" mercury="" merge="" merger:nvidia="" merger="" mergers="" merica="" merican="" merit-driven="" message:="" message="" met.="" met="" meta="" metabolic="" metaphor:by="" meters="" methane="" method="" methodology="" methods.="" methods="" metrics.="" metrics:trial="" metrics="" mexico="" mi300x="" mia="" michael="" micro="" microbiome="" microbiomes="" microgrid="" microgrids.="" microgrids="" microleds="" microscopes="" microscopic="" microsoft="" mid-1920s.="" mid-semester="" middle="" might="" migr="" migration="" mile="" mileage="" miles="" militarism="" militarization="" militarized="" military="" millennia.="" millennial-led="" millennial="" millennials="" million="" millions="" mimic="" mimicking="" mimics="" mimo:="" min="" mind-blowing="" mind="" minds="" mine="" minecraft="" minerals="" mingled="" minimal.="" minimal="" minimize="" minimizing="" minkowski="" minly="" minor="" minors="" minsky.="" minsky="" minuteearth:why:="" minuteearth="" minutephysics:why:="" minutephysics="" mirabilis="" miracle="" miracolus="" miraculous="" mirror="" mirrored="" mirroring="" mirrors="" misalignment.="" misalignment="" misinformation="" misinterpret="" misinterpretation:="" misinterpretation="" mislead="" misleading="" misremembering="" miss="" missed:="" missed="" misses="" missing="" mission.="" mission="" missions="" mississippi="" missouri="" misstatement="" mist="" mistake="" mistral.="" mistral="" misunderstanding:="" misunderstanding:problem:="" misunderstanding="" misunderstandingmisunderstanding:issue:="" misunderstandings:="" misuse="" mit="" mitigating="" mitigation="" mix-up="" mixed="" mixing="" mixtral="" mjfree="" mkulima="" mkulimamwanafunzi="" ml="" mlgw="" mmda="" mmigration:="" mobile="" mobility="" mod="" model.="" model.i="" model:software="" model="" modeled="" modeling.="" modeling:="" modeling="" models.="" models.ai="" models.starlink="" models:bill="" models="" moderate="" moderation="" modern="" moderna="" modifiability="" molecular="" molecules="" moluccas="" moment="" momentum="" monde.="" monde="" money.="" money:testing="" money="" monitir="" monitired="" monitor:="" monitor:baobab:="" monitor:traditional="" monitor="" monitored="" monitoring.="" monitoring:="" monitoring="" monoclonal="" monoclonals="" monoploy="" monopolies="" monopolized="" monopoly="" monte="" month.="" month="" monthly="" months.="" months="" monty="" monumental="" moore="" moral="" more.1.="" more.="" more="" morgenstern:="" morgenstern="" moringa.="" moringa="" moscow="" most="" mostly="" mosttop="" motion.="" motion="" motivated="" motivates="" motivating="" motivation:="" motivations="" motivationsis="" motives:="" motor="" mountain="" mouth="" move="" moved="" movements="" moves.="" moves:hassabis:="" moves="" movie="" movies="" moving="" mperial="" mpox="" mrna-1273.214="" mrna-1345="" mrna-4157="" mrna-based="" mrna.="" mrna="" ms.="" ms="" mskcc="" msterdam="" mt="" mu="" much="" mukesh="" multi-billion="" multi-body="" multi-gene="" multi-gigabit="" multi-orbit="" multi-virus="" multifaceted="" multilateral="" multilateralism:="" multilaterals="" multinational="" multiple-choice="" multiple="" multiplied="" mumbai="" mumbling="" murray.relevance="" murray="" musk-centric="" musk-deep-maths:strength:="" musk-deep-maths="" musk-owned.="" musk-owned="" musk:="" musk="" must="" mutation="" mutations="" mv="" mw="" mwanafunzi="" mwmphis="" my="" myanmar="" myristica="" myristicin="" mysteries="" myths="" n-1="" n:="" n="" nagasaki.="" nairobi-based="" nairobi-beijing="" nairobi-focused="" nairobi-led="" nairobi.="" nairobi.resources="" nairobi="" naked="" nalyze="" name="" named="" namibia="" naming="" nano-="" nano-level="" nano-maths="" nano-scale="" nano="" nanomaterial="" nanomaterials="" nanomedicine="" nanoparticle="" nanoparticles="" nanoscale="" nanotech="" nanotechnology="" narrated="" narration.="" narration.i="" narration="" narrative.="" narrative:="" narrative="" narratives.1.="" narratives.="" narratives="" narrow-bandwidth="" narrow="" narrower="" nasa="" nascent="" nasdaq="" nassau="" nathan="" nation="" national="" nationalism="" nations.="" nations:="" nations="" natires="" native="" natural="" naturally="" nature-inspired="" nature.="" nature.com="" nature:="" nature="" naturebiotech="" natures="" naturevideochannel="" natutre="" navic="" navigate.="" navigate="" navigates="" navigating="" navigation="" nazi="" nazism.="" nazism="" nbvidi="" nclusive="" nd="" nda-driven="" nda="" ndas="" ndependent="" ndia="" ndiaai="" ndian="" ndirectly="" ndonesia="" ndroid="" ne="" near-elliptical="" near-term="" near="" nearby="" nearer="" nearest="" necessary="" necessity="" need:="" need="" needed.="" needed="" needing="" needs.="" needs.addressing="" needs:="" needs="" needsno="" negative="" neglected="" negligible.="" negligible="" negotiate="" negotiated="" negotiations.="" neil="" nejm="" neoantigen="" neoantigens="" neologism="" neoverse.="" neoverse="" netherlands-indonesia="" netherlands.who:="" netherlands="" nets="" netwon="" network-building="" network.="" network:="" network="" networked="" networking="" networks.="" networks:="" networks:photonics="" networks="" networkx="" neumann-einstein-turing:="" neumann-einstein-turing="" neumann-einstein="" neumann-era="" neumann-like="" neumann.="" neumann:="" neumann:vision:="" neumann="" neumanncontrast:welcoming="" neumanns.="" neumannyou="" neural-inspired="" neural-like="" neural="" neuralink:="" neuralink="" neurodegenerative="" neuroimaging="" neurons="" neuroprotection="" neuroprotective="" neuroscience.="" neuroscience="" neuroscientists="" neutral="" neutrality="" never="" nevs="" new="" newly="" newman="" news.="" news:="" news:balachandran:="" news:follow="" news="" newton-raphson="" newton:="" newton:e="mc²," newton="" newtonian="" newtoon="" newtown="" next-gen="" next-generation="" next="" ngig="" nglish-language="" nglish="" ngo="" nhs="" niazimicin="" niche="" niels="" niger="" nigeria-eu="" nigeria-ghana-eu="" nigeria="" night="" nigrum="" nih.gov="" nih="" nimble="" nio="" nique="" nirsevimab="" nist="" nited="" nitrogen="" nj="" nlock="" nlp="" nm.="" nnalen="" nnovation="" nnus="" no1="" no="" noaa="" nobel="" node="" noem:="" noem="" noise="" noividia="" noland="" nom="" non-ai="" non-binding="" non-compliance="" non-compliant="" non-discrimination="" non-engagement="" non-euclidean="" non-experts.="" non-ideological="" non-industrialized="" non-interaction="" non-involvement:="" non-iterative="" non-linear="" non-medical="" non-sensitive="" non-standard="" non-western="" nondisclosure="" nonlinear="" nor="" norbert="" nordisk.="" norman="" norris="" north="" northwestern="" norway="" not="" notable="" notably="" notation="" note:="" note="" notebooks="" noted.="" noted="" notes.="" notes:sources:="" notes="" nothofagin="" noting="" notions="" novartis.="" novel="" november="" novo="" now="" npr.org="" npr="" nsa="" nstitute="" nteractive="" nternational="" nterprise="" ntibodies:="" ntibodies="" ntro="" ntu="" nuance.="" nuance="" nuanced="" nuclear-powered="" nuclear="" number="" numbered="" numberphile:why:="" numberphile="" numbers="" numerical="" nut="" nutmeg="" nutraceutical="" nutraceuticals:="" nutraceuticals:nutmeg="" nutraceuticals="" nutrient="" nutrients="" nutrition="" nuts="" nutshell="" nvidia-arm="" nvidia-tsmc="" nvidia.="" nvidia:role:="" nvidia="" nvidias="" nyu="" o="" obelscope="" obit="" object="" objects="" obligations="" obotics="" obscurity:="" obscurity="" observation.conclusion:="" observation="" obstructions="" obviously="" occasionally="" occupation="" occurs="" oceanic="" october="" ocus="" ocw.mit.edu:why:="" od="" oderna="" oderna_tx="" oding="" odrered="" oem="" oes="" of:="" of:fostering="" of="" off="" offer:one-paragraph="" offer="" offered="" offering="" offers="" office="" officer="" offices="" official="" officially="" officials:role:="" officials="" offiuce="" offline="" often="" ohn="" oil:="" oil="" oilar="" oils="" oin="" oine="" ok="" okyo="" olar="" old="" older="" olds="" ole:="" oleic="" oleifera="" olf="" olhysical="" ollect="" olossus:10="" olossus="" olut="" omb="" omicron="" omniverse:="" omniverse="" omparing="" omputability:="" omputex="" omputing:="" omputing="" on-board="" on-device="" on.="" on:biology:="" on:linear="" on="" once="" ondon="" one-paragraph="" one="" ones="" oneweb:="" oneweb="" onfirmation:="" ongoing="" online="" only="" onnection="" onset="" ontent:="" ontext:="" ontext="" onward="" oogle="" open-ended="" open-source="" open.="" open="" opencourseware="" opened="" openly.="" openness="" operate="" operated="" operates="" operating="" operation="" operational="" operations.web:1="" operations:="" operations:amazon="" operations="" operationsyou="" operationsyour="" operator="" opinion="" oppenheimer="" opportunities.="" opportunities="" opportunity:="" oppose="" opposing="" opposition="" optical="" optics.="" optics:="" optics="" optimal="" optimising="" optimism.="" optimism="" optimistic="" optimization.="" optimization:math:="" optimization="" optimize:="" optimize:hardware:="" optimize:run="" optimize="" optimized="" optimizes="" optimizing="" optimus="" option="" optional="" optoelectronic="" opular="" or="" oranges="" orbit.="" orbit="" orbital="" orbits="" orchestrated="" ord="" order.="" order="" ordering="" orders.="" orders.web:1="" orders:="" orders:e="mc²" orders="" ore="" oremaths.="" oremaths="" organ="" organic="" organisation="" organisations="" organisms="" organization="" organizations.="" organizations="" organized="" organs.="" origin="" original="" originated="" origins:unicef="" orin:="" orin="" oringa="" orld="" oronto="" orphan="" orr="" orthodox="" os="" oseltamivir="" oskar="" oslo="" ost:0="" ost:1="" ost:4="" ost:5="" ost:7="" ostering="" oston="" osts="" ot="" otential="" other="" others.="" others="" othersx-deep-maths="" othersxp-deep-maths:strength:="" otherworld="" otto="" ou="" ould="" our="" out="" outcome:="" outcome="" outcomes.="" outcomes="" outgrew="" outh="" outlets:ap="" outlets="" outline="" outlined="" outlook="" outpace="" outpaces="" outpacing="" outperforming="" outperforms="" output="" outputs.="" outputs="" outrage="" outright="" outshine="" outside="" outstrip="" outube="" outweigh="" ov="" oval="" ovember="" ovens="" over="" overcome="" overcoming="" overcrowding="" overemphasize="" overhype="" overhyped="" overhypes="" overhyping="" overlap.="" overlap:="" overlap="" overlapping="" overlaps="" overly="" overseeing="" overshadowed="" overshadowing="" overshadows="" oversight.="" oversight="" oversimplification:="" oversimplified="" oversimplifies="" oversimplify="" overstate="" overstated="" overstates="" overtaking="" overtook="" overturned="" overview="" overwhelming.="" ow2ned="" ow="" own="" owne="" owned="" owner:="" owner="" owners="" ownership:="" ownership:electrolux="" ownership="" owning="" owns="" oxalate="" oxford="" oyota="" p-deep-maths="" p="" pace="" pacifism.="" pacifism="" pacifist="" packaging="" packed="" pages="" pain="" paints="" palivizumab="" pam="" panacea="" pancreatic="" pandemic-ready="" pandemic="" pandemics="" panels="" paper.="" paper:="" paper:candidate:="" paper="" papers:="" papers:unified="" papers="" papert="" paradigm-shifting="" paradigm.="" paradigm="" paradigms.="" paradigms="" paradox.="" paradox="" paradoxa="" paragraphs="" parallel="" paralleled="" paralleling="" parallels="" parameters="" paramount.="" parent="" parents="" paris.="" paris="" park.="" park="" parkinson="" part="" partially="" participant.="" particle="" particles="" particularly="" partisan="" partisanship.="" partly="" partner:="" partner="" partnered="" partners.around="" partners="" partnership.="" partnership="" partnerships.="" partnerships:="" partnerships:cwi="" partnerships="" parts="" party="" pasadena.="" passing="" passion="" past="" path:="" path:pre-2007:="" path="" pathogen="" paths.="" paths:crispr="" patient-specific="" patient="" patients="" pattern="" patterns.="" patterns="" paul="" paulo="" paving="" paying="" pc="" pcs="" pdated="" pdf="" peace-through-communication="" peace.="" peace:="" peace="" peaceful="" peak.="" peak6="" peak:="" peak:peak="" peak="" peanut.="" pearson="" peculiar="" peculiarly="" peer-reviewed="" pen-and-paper="" penetration="" penn="" pentagon="" people.="" people="" peoples="" pepper.="" pepper="" per="" perceived="" perception:="" perception="" perceptions.="" perceptron="" perceptrons="" perfect="" perform="" performance.="" performance:="" performance="" performed="" perfumes="" perhaps="" period="" periodic="" peripheral="" permanent="" permiited="" permit="" permits="" persecution="" persist="" persisted="" persists.="" persona="" personal="" personalities="" personalization.="" personalization="" personalized="" perspective:="" perspective:millennials="" perspective="" perspectiveestablishment="" perspectivenoem="" perspectives.1.="" perspectives.="" perspectives="" perspectivestrengths:="" perturbation="" perturbations.="" pest="" petaflops="" pfizer="" ph.d.="" ph.d.s="" pharma.="" pharma:="" pharma="" pharmacare.="" pharmaceutical="" pharmaceuticals="" pharmaceutics="" pharmacology="" phased="" phenolic="" phenomena.="" phenomena="" phenylpropene="" philippines="" philips.="" philips="" phillips="" philosopher.="" philosopher:philosophical="" philosopher="" philosophers="" philosophical="" philosophically="" philosophy.="" philosophy="" phoenix-owned="" phoenix="" phone="" phones="" phosphate="" photoelectric="" photonic="" photonics-ai="" photonics:="" photonics="" photonicsthe="" photons="" physical="" physically="" physicist="" physicists="" physics-centric="" physics-computing="" physics-focused="" physics-informed="" physics.="" physics:="" physics="" physik:photoelectric="" physik="" phytate="" phytochemical="" phytochemicals="" phytomedicine="" phytosterols="" pice="" pick="" picking="" picture="" pictures="" pider="" pidgeon="" piece="" pig-to-human="" pig="" pika="" pilot="" piloted="" pilotingof="" pinene="" pinpoint="" pioint="" pioneer="" pioneered="" pioneers.="" pioneers:einstein="" pioneers="" pipeline="" pipelines="" piperine="" pipettes="" pippa="" pity="" pivot="" pivotal="" pixar="" pixel="" pixels="" place="" placed="" places:="" places="" plague="" plain="" plan="" planet="" planetary="" planned="" planner="" planning.="" planning="" plans.="" plans="" plant.="" plant:="" plant="" planting="" plants="" platform-centric="" platform.="" platform.for="" platform:="" platform:a="" platform:posts="" platform="" platforms:clara:="" platforms:drive="" platforms:icivics="" platforms="" platformsfor="" platformslet="" platformsthere="" plausible="" plausibly="" play="" played="" player="" players.="" players:musk:="" players:musk="" players="" playerselon="" playground="" playing="" plc="" please="" plotenetially="" plug-in="" plugged="" plus="" plutonium="" plwase="" pm="" pod="" podcasts:="" podcasts="" podcastsfor="" podolsky="" pods="" poentially="" poincar="" point.="" point="" points.="" points:unesco="" points="" pointsyour="" pointyour="" poised="" polarizing="" policies.="" policies.i="" policies.u.s.="" policies:="" policies:propose="" policies="" policiesyou="" policy-focused="" policy-heavy="" policy-investment="" policy-oriented="" policy-tech="" policy.="" policy:="" policy="" political="" politically="" politicization="" politico="" politics.="" politics="" pollution.="" pollution="" polyisoprene="" polymath="" polymer="" polyphenol="" polyphenols="" ponential="" pony.ai="" pool="" poor="" pop-science="" pop="" popular.="" popular="" popularity.="" popularization="" populated="" population.="" population="" populations="" port="" portuguese="" posed="" position.="" position="" positioned="" positioning:math:="" positioning="" positions="" positive="" positives="" possible:in="" possible="" possibly="" post-1905="" post-1915="" post-1940s="" post-1945="" post-1955="" post-1969.="" post-2002="" post-2020="" post-atomic="" post-brexit="" post-covid="" post-e="mc²" post-graduation="" post-harvest="" post-newtonian="" post-q="" post-quiz="" post-war:="" post-war="" post-world="" post:="" post="" postdocs="" posted="" posters="" posts="" postulates:light="" potency="" potential.="" potential:="" potential:cosmetics:="" potential:pharma:="" potential="" potentially="" potentialnutmeg="" potentialthe="" potify="" potions="" pour="" poverty="" powder="" power-efficient="" power-hungry="" power-up="" power.="" power:="" power="" powered="" powerful="" powering="" powers.="" powers="" powerwall="" pple="" pr="" practical.="" practical:="" practical="" practically="" practice:="" practice="" practicing="" pragmatic="" pragmatism:="" pragmatism="" praise="" praised="" praises="" praising="" pre-1900="" pre-1905="" pre-1945:="" pre-1945="" pre-2020.="" pre-2020s="" pre-ai="" pre-wwii="" precedence.5.="" precedence="" precession="" precise="" precision="" precisionag="" precursor="" precursors.="" precursors="" predated="" predates="" predator-prey="" predict="" predictable="" predicted="" predicting="" prediction="" predictions="" predictive="" predicts="" prefer="" preference="" prefering="" preferred="" preferring="" prefigure="" prefix="" pregnant="" premium="" preoccupied="" prep.="" prep="" prepare="" prepared="" preparedness="" prepares="" preparing="" presence.="" presence.web:0="" presence="" present.="" present="" presented="" presenting="" preservation="" preserving="" president="" presidential="" press="" pretoria="" preussischen="" prevails="" prevent="" preventing="" prevention="" prevents="" previous="" previously.="" previously="" price="" priced="" pril="" primarily="" primary.="" primary="" prime="" princeton="" princetonfirst="" principa.="" principal="" principia.="" principia:="" principia:strengths:unified="" principia="" principiato="" principle:="" principle="" principles.="" principles:="" principles="" prior="" priorities.="" priorities.biographyearly="" priorities:="" priorities:advanced="" priorities:ai="" priorities="" prioritization="" prioritize="" prioritized="" prioritizes="" prioritizestop="" prioritizing="" priority.="" priority:="" privacy="" private-sector="" private="" prize.="" prize="" pro-business="" pro-palestinian="" pro-terrorist="" probabilistic="" probabilities="" probability="" probably="" problem-solving="" problem.="" problem="" problems.="" problems="" process="" processes.="" processes="" processing.="" processing:="" processing:genomics:="" processing="" processor="" processors="" prodigee="" prodigious="" produce="" produced="" producer="" produces="" producing="" production.="" production="" productivity:="" productivity="" products="" prof="" professionals="" professor="" professordaveexplains:why:="" profile.="" profile:="" profile="" profilekey="" profiles="" profit="" profited="" profits="" profound.="" profound="" program="" programmable="" programmer="" programming.="" programming:="" programming="" programs.="" programs="" progress.="" progress:="" progress="" prohibitive="" project.="" project:="" project="" projected="" projections="" projects.="" projects:10="" projects:="" projects="" prominence.="" prominence="" prominent="" promise="" promising="" promote="" promoted="" promotes="" promoting="" promotion="" prompting="" prompts="" proof="" propaganda="" properties.="" properties="" property="" proportional="" proportionally="" propose="" proposed="" proposes="" proposing="" proposition.="" proprietary="" pros="" prospects.="" prostate="" protect="" protected="" protecting="" protection="" protects="" protein-rich="" protein="" proteins.="" proteins="" proteinsnvidia="" protest="" protesters="" protests.="" proved="" proven="" provide="" provided="" provides="" providing="" proving="" prowess.="" prowess="" proximity="" prussian="" pslv="" psoriasis="" psychedelic="" psychoactive="" psychotherapy="" ptimus="" ptional="" ptsd.="" ptsd:="" ptsd="" public-private="" public="" publication="" publications="" publicationsin="" publicly="" publish="" published.="" published="" publishers="" publishes="" publishing="" puente="" pugwash="" puibclication="" pulau="" pulp="" punjab="" purchased="" pure="" purely="" purification="" purpose:="" purpose="" pursue="" pursued="" pursuing="" pursuit.="" pursuits.="" pursuits="" push="" pushes="" pushing="" put="" puzzle-maker="" puzzles.="" puzzles:e="mc²" puzzles="" pydata="" python-based="" python.="" python:="" python="" pytorch="" q10.="" q1="" q2="" q4="" q5="" q6.="" q6="" q7.="" q7="" q8.="" q8="" q9.="" q9="" q:="" q="" qatar="" qin="" qinchuan="" qualcomm="" qualification:="" qualify="" qualifying="" quality="" quanta="" quantum-enhanced="" quantum-inspired="" quantum-mechanical="" quantum="" qubits="" quercetin="" queries.="" queries.clarifying="" queries="" queriesdandelions="" query.="" query="" quest:="" quest="" question.="" question.was="" question:="" question:turing="" question="" questioned="" questioning="" questions.="" questions:ai="" questions="" quickly.="" quite="" quiz:="" quiz="" quizzes="" quotas="" quoted="" qusoft="" r.="" r1="" r="" ra5ther="" race.="" race:="" race="" races="" radar.="" radiliogy="" radio="" radiologists="" radiology="" radiopharmaceuticals="" radioprotective="" radiotherapy="" rail="" rain:="" raise="" raised="" raises="" raising="" rallied="" ran="" rance="" range="" rank="" rankings="" ransational="" ransnational.="" ransnational="" ranswell="" raphics="" rapid="" rare="" rarely="" rates.="" rather="" ratinglikelihood:="" rational="" rationale:="" rats="" raw="" raymond="" rayong="" raytheon="" rcp="" rd-einstein="" rd="" rdma="" re:="" re="" reach.="" reach:="" reach:x="" reach="" reaches="" reaching="" react="" reaction:="" reactions:="" reactions="" reactor="" reactors="" read="" readability="" readers="" readiness="" ready-made="" ready="" real-time.="" real-time:the="" real-time="" real-world="" real="" realism.="" reality.="" reality="" really="" reason="" reasonably="" reasoning.="" reasoning="" reasons="" reassess="" reatermemphis:posts="" reatermemphis="" reatest="" rebranded="" rebuild="" rebuilding="" recap:="" recap="" receive="" recemtly="" recent="" rechecked="" rechecking="" recipe="" recipes="" reclusive="" recognition:="" recognition="" recognizable="" recognize="" recognized="" recognizing="" recommend="" recommendation:="" recommendation="" recommendations="" recommended="" reconfiguration="" reconnaissance="" reconstructed="" record="" recorded.="" recorded="" records.="" records="" recurrence.="" recurrence="" recursion.="" recursion:="" recursion="" recursiondefinition:="" recursive="" recursively.="" recursively="" recusrion="" recycling="" red="" redefined="" redefining="" redesign:="" redesign="" redesigned="" redesignhuman="" redesigning="" redirecting="" redoubled="" reduce="" reduced="" reduces="" reducing="" reduction="" ree="" reenegineering="" reengineer="" reenginered="" referee="" reference:="" reference="" referenced.="" referenced="" references="" referred="" referring="" refers="" refine="" refined="" refinement="" refines="" refining="" reflect="" reflected="" reflecting:exponential="" reflecting="" reflection="" reflective="" reflects="" reform.="" reform:="" reform="" reforms="" reframing="" refugee="" refugees="" refusing="" regarded="" regarding="" regimes.="" regional="" regions.="" regions="" register="" regression.="" regret="" regretted="" regular="" regularly="" regulates="" regulations="" regulators="" regulatory="" reichlin-melnick="" reinforced="" reinforcement="" reinforcing="" reinstates="" reiterating="" rejecting="" rejection="" rejoined="" rejoining="" relatable="" relate:baidu="" relate="" related="" relating="" relation="" relations.="" relations="" relationship.="" relationship:cic="" relationship="" relationships="" relatively="" relativistic="" relativity.="" relativity="" relativitymathematical="" relaxation.="" release="" released="" releases="" releasing="" relevance.1.="" relevance.="" relevance.e="mc²" relevance:="" relevance:correct="" relevance:e="mc²" relevance:nairobi="" relevance:nuclear="" relevance:vince="" relevance="" relevanceyou="" relevant.="" relevant:="" relevant="" reliability.="" reliability="" reliable="" reliance:="" reliance="" reliant="" relied="" relief="" relies="" religion="" relocating="" reluctance:="" reluctance="" reluctant="" rely="" relying="" remain="" remained="" remaining="" remains="" remark="" remind="" remote="" remove="" removing="" renaissance="" renamed="" rendering="" renders="" rene="" renee="" renewable="" renewables="" rentm_spacex="" reopening="" repeat="" repelling="" repels.="" repels="" replace="" replaces="" replacing="" replica="" replicated="" reply="" report="" reported="" reportedly="" reporting.="" reporting="" reports.="" reports:="" reports="" reproducible="" republican="" republicans="" repurposed="" reputable="" request="" requested:a="" requested="" requesting="" requests="" requestyou="" require="" required.="" required="" requirements:="" requirements="" requires="" requiring="" rescinds="" research-based="" research-focused.="" research-policy="" research.="" research="" researcher="" researchers.="" researchers="" researches="" resembling="" resent:="" reshaped="" residence:="" residence="" resident="" residents="" resigned="" resigning="" resilience.="" resilience.this="" resilience:="" resilience="" resilient="" resist="" resistance="" resisted="" resolutions.="" resolve.comparing="" resolve="" resonance:="" resonance:unseen="" resonance="" resonate="" resonates="" resonating="" resource-constrained="" resource="" resources.="" resources:="" resources:for="" resources:teens:="" resources:unesco="" resources:wechat:="" resources:what="" resources="" resourcesbelow="" resourcesfor="" resourcesgovernment="" resourcesto="" respectfulness.="" respiratory="" respond="" responded="" response:="" response:harvard="" response:what="" response="" responses.="" responses="" ress="" rest="" restore="" restraining="" restrict="" restricted="" restrictions:="" restrictions="" results.="" results="" retail="" retailing="" retained="" retains="" retaliation.="" retaliatory="" retreat="" return="" returning="" reuniting="" reusable="" reuters="" reveal="" revealed="" revealing="" reveals="" revenue="" reversing="" review="" reviews="" reviously="" revised="" revisit="" revisiting="" revival:="" revive="" revived="" revocation.="" revocation="" revocations="" revoked="" revokes="" revoking="" revolution.="" revolution="" revolutionary.="" revolutionary="" revolutionized="" revolutionizing="" revolutions.1.="" revolutions.="" revolutions="" rewwally="" reynold.="" rhetoric="" rib="" rice.="" rice="" rich="" richard="" richer="" richmond:role:="" richmond="" riddles="" rids="" riemann="" right="" rights.="" rights="" rigor.="" rigor="" rigorous="" rinceton="" rincipia="" risc="" rise="" rishi="" rishivigyankendra.="" rishivigyankendra.website:="" rishivigyankendra:why:="" rishivigyankendra="" rising="" risk-taking="" risk:="" risk="" risking="" risks.="" risks="" ritash="" riticalperspective-establishmentnarrative:sourceslikenationalgeographic="" ritika="" ritual="" rival="" rivaling="" rivalries="" rivals.="" rivals="" riven="" river="" riverport="" riyadh="" rm:="" rm:posts="" rm="" rna="" rnas.="" rnas="" road.="" road="" roadmaps="" roads="" robert="" robin="" robos="" robosense="" robot="" robotaxi="" robotaxis.="" robotaxis="" robotic="" robotics.="" robotics:="" robotics="" robots.="" robots:="" robots="" robotys="" robust="" roche="" rocket="" rockets="" rok="" rokcreated:why:="" rokcreated="" role.="" role:1939="" role:="" role:ai="" role:as="" role:bionemo="" role:cic="" role:e="mc²’s" role:itu:="" role:itu="" role:leads="" role:manhattan="" role:neumann-einstein-turing:balachandran="" role:neumann-einstein-turing="" role:scharf="" role:starlink:="" role:tsmc:="" role:von="" role="" roleai="" roles.5.="" roles.="" roles:joined="" roles="" rolesyou="" rolewill="" rollout.="" rollout="" rom="" roman="" rome="" romotes="" rontiersin="" rooibos:="" rooibos="" room="" roosevelt="" root-finding="" root="" rooted="" roots.="" roots.unesco="" roots="" rop="" ros:="" ros:musk="" ros:x="" rosen.="" rosen="" rosenblatt="" rossum="" rote="" rough="" rought-resistant="" round="" routes="" routing="" rover:role:="" rover="" rovers="" rowe="" royal="" rpers="" rsv.="" rsv="" rtecursion="" rtetail="" rubber.="" rubber="" rubio="" ruhr="" ruins="" rules.="" rules:nature="" rules:your="" rules="" ruling="" rump="" run="" running="" runs="" rural="" russell-einstein="" russell="" russia="" s.="" s:="" s="" sad="" safe:="" safe="" safeguards.="" safeguards="" safely="" safer="" safety-conscious="" safety.4.="" safety.="" safety:="" safety="" safrole:="" sahel="" sahelo-sudanian="" sai="" saic="" said="" sailors="" sais.="" sais:="" sais="" sale.="" sale:="" sale="" sales.="" sales="" salesforce="" same="" sample="" samsung="" san="" sanctions="" sand="" sanofi="" santa="" santiago="" sao="" sarah="" sars-cov-2="" satellite="" satellites.="" satellites:math:="" satellites="" satellitesnasa="" saudi="" save="" saves="" saving="" saw="" say.="" say="" saying="" scalability.="" scalability:="" scalability="" scalable="" scale.="" scale:="" scale="" scaled="" scales:="" scales="" scaling.="" scaling="" scan="" scarcity="" scavenger="" scd="" scenarios="" scharf-like="" scharf="" schmidt="" scholarly="" scholars.="" scholars="" school.="" school="" schools="" schwarf="" science-focused="" science-vs="" science.3.="" science.="" science:="" science:cwi="" science="" sciencenewsforstudents.org:why:="" sciences="" scientfic="" scientific="" scientists.="" scientists="" scieties="" scikit-learn="" scikit-learnforpancreaticcancer="" scishow:covers="" scishow:matches:="" scishow:why:="" scishow="" scistream:why:="" scistream="" scitable:why:="" scomputationalvisioncrisprtrialsinspirerecursivelearning="" scoop:what="" scoops.="" scoops="" scope.="" scope="" score="" scott="" screen="" screening="" screens="" script="" scripting.="" scroll="" scrolling="" scrutiny.="" scrutiny="" scsp="" sdg="" sdgs="" sdiplomacyrole="" sdo="" seagull="" seal="" seamless="" search:="" search:context:="" search:context="" search="" searches.="" searches="" searching="" seasonal="" secluded="" second-largest="" second="" seconds="" secrecy:="" secret="" secretarial="" secretary.="" secretary="" secretive="" secrets="" secrurity="" sectior="" sectir="" sectirs="" sector-specific="" sector:="" sector="" sectors.="" sectors:advanced="" sectors="" secturs="" secure="" secured="" secures="" securing="" security.="" security:="" security="" see="" seed="" seeds="" seeing="" seek="" seeking="" seeks="" seem="" seems="" seen="" sees="" seinem="" select="" selected="" selection.="" selection.safe="" selection="" selectusa="" self-calling="" self-driving.="" self-driving:="" self-driving="" self-improving="" self-paced="" self-reference="" self-referential="" self-reliance="" self-reproducing="" self="" sell="" selling="" semi-arid="" semi-e="" semiconductor="" semiconductors="" seminal="" seminars.="" seminars:="" seminars="" senator="" send="" sending="" sends="" senegal-south="" senegal="" senior="" sense.="" sense:parallel="" sense="" senses.="" senses="" sensing:="" sensing="" sensitive="" sensitized="" sensor="" sensors.="" sensors="" sent="" sentiment:="" sentiments="" seoul="" separate="" separately="" separating="" separation.="" separation:="" separation="" september="" sequenced="" sequencing.="" sequencing="" serach="" series="" serious="" serotonin="" serum="" serve="" served="" server="" servers="" service.="" service="" servicenow="" services.="" services="" serving="" sessions="" set="" setbacks="" sets="" setting="" settings="" settle="" settled="" setup="" seven="" several="" severe="" sevp-certified="" sevp="" seymour="" shaanxi="" shanghai="" shannon="" shape="" shaped="" shapes="" shaping="" share="" shared="" shareholders="" shares="" sharf="" sharing="" she="" shea.="" shea="" sheet="" sheets="" shelby="" shenzhen="" shield="" shift.="" shift:="" shift:the="" shift="" shifted="" shifting="" shifts:="" shifts="" shikimic="" shining="" shipping="" shock="" shop="" short-term="" short="" shortages.="" shortages="" shortest="" shortfall="" shortly="" shots.="" should:track="" should="" show="" showcase="" showcased="" showcases="" showed.="" showed="" showing="" shown="" shows="" shum="" sia:star="" sian="" sickle="" siddhant="" side="" sidelined="" sidelining="" signal="" signaling="" signals="" signed="" significance.="" significance:1905="" significance="" significant.="" significant="" significantly="" signing="" signings.conclusion:="" signings="" silico.="" silico:="" silico:many="" silico:qualification:="" silico="" silicon="" silk="" sim:="" simcity="" similar="" similarly="" simple="" simpler="" simplest="" simplicity="" simplified="" simplify="" simply.="" simply="" simulate="" simulated="" simulates="" simulating="" simulation:="" simulation="" simulations.="" simulations="" simulator="" simultaneity="" simultaneously="" since="" sinensis="" singapore="" single-cell="" single-region="" single="" singular="" sinica="" site="" sites.="" sites="" sitter="" sitzungsberichte="" six="" size="" skeptical="" skepticism.="" skepticism="" sketch="" skew="" skewing="" skews="" skilled="" skills.="" skills:="" skills:turing="" skills="" skin="" skincare="" skipped="" skperplexity="" skyroot="" slicing:="" slicing="" slide="" slovakia="" slow="" slower="" slowing="" slowly.="" slows="" small-team="" small:="" small="" smaller="" smallholder-friendly.="" smallholder-friendly="" smallholders.="" smart.="" smart="" smarter="" smartereveryday:why:="" smartphone="" smartphones="" smog="" smoothf1="" smoothly.="" smoothly="" snap="" snapchat="" snippet="" snowflake="" so="" soace="" social="" socialist="" societal="" society.="" society="" softbank="" software:="" software="" soil-less="" soil="" soils="" solar="" solarcity="" sold="" sole="" solely="" solidarity="" solitary="" solitude="" solo="" solution.="" solution:="" solution:education:="" solution:raise="" solution="" solutions.="" solutions:von="" solutions="" solutionsproposed="" soluyions="" solvable="" solvay="" solve="" solved="" solves="" solving.="" solving="" some="" someone="" something="" sometime="" sometimes="" son.="" son="" song="" sony="" soon="" sophie="" sophisticated="" sophomore="" soros="" sorry="" sort="" sorting="" sought="" soul="" sounds="" source.="" source="" sourced="" sources.="" sources:education="" sources:web:6:="" sources:x:="" sources:x="" sources="" sourceyou="" sourcing="" south="" southern="" sovereignty.="" sovereignty:="" sovereignty:definition:="" sovereignty:taiwan:="" sovereignty="" sovereigntyyou="" soy="" soybeans="" space-time="" space.="" space:="" space="" spaces.="" spaces="" spacetime="" spacex="" spalathus="" span="" spanned="" spanning="" spark.="" spark="" sparked="" sparking="" sparks="" sparsely="" speakers-="" speakers="" speakershere="" speakersthese="" speaks="" spearheaded="" special="" specialists="" specialized.="" specialized="" specializes="" specializing="" species="" specific="" specifically="" specificity.="" specifics="" spectroscopy="" spectrum="" speculating="" speculative="" sped="" speech="" speeches="" speed-of-light="" speed.="" speed.if="" speed="" speeding="" speeds="" spend="" spends="" spent="" spice="" spices:turmeric="" spices="" spider="" spielberg="" spielnerg="" spike="" spikes="" spinning="" spirit.="" spirit="" spite="" split="" splits="" spot="" spots.="" spots="" spotting="" spraying="" spread="" spring="" springfield="" spun="" spurred="" sputnik="" spying="" spython="" sq="" squad="" square-foot="" squared="" sri="" srivastava:why:="" srivastava="" srivastia="" ssris="" sstudentexpulsions="" ssumed:="" st="" stabilizers.="" stable="" stach="k" stacks="" staff="" staffing="" stage="" stages="" stake="" stakeholder="" stakeholders="" stalentbarriersviastemvisas="" stalin.="" stalin="" stalled="" stalls="" stance.="" stance="" standard.="" standard="" standardized="" standards-setter="" standards-setting="" standards.="" standards.conclusion:="" standards:="" standards="" standrads="" stands="" stanford="" stanton="" star="" starkly="" starling="" starlink-ai="" starlink.="" starlink:="" starlink="" stars="" starshield="" start="" started="" starting="" starts="" startup="" startups-="" startups.="" startups="" stat="" state-aligned="" state-certified="" state-run="" state="" statement:="" statement="" statementi="" statements="" states="" static="" station="" stationary="" stations="" statistical="" statistics.="" statistics:="" statistics="" statnews.com="" stats="" status.="" status:="" status:original="" status="" stay.="" stay="" stayed="" staying="" steam="" steel:role:="" steel="" stellar="" stellenbosch="" stem.="" stem="" stemming="" stems="" step="" stephen="" stepped="" stepping="" steps="" steroids="" sterols="" steve="" stifle="" stifled="" stifles="" stifling="" stigma="" still="" stochastic="" stock="" stockholm="" stood="" stop="" stopped="" stops="" storage="" store="" stored-program="" stored="" stores="" stories.="" stories="" storms="" story-driven="" story="" strain.="" strains.="" strange="" strassmann="" strategic="" strategies="" strategy="" stream="" streaming="" streamlines="" streamlining="" streams="" strength.="" strength="" strengthening="" strengthens="" strengths:science:="" strengths="" strengthsstarlink="" strict="" strong="" stronger="" structure:einstein="" structure="" structured="" structures="" struggle="" struggles="" struggling="" student-friendly="" student.="" student="" students.="" students="" studentsfrom="" studentsscale:="" studentswhat="" studentsyou="" studied.="" studied="" studies="" study.="" study:biology="" study="" studybiology:="" studying="" studyto="" stuff="" style:known="" styles="" su:="" su="" sub-designfrom="" sub-saharan="" subject="" subjective="" subjects.="" subjects:="" subjects="" submit="" subproblems.="" subscribers.="" subscribers="" subsidiary="" subsidies="" subsidized="" substantial="" subterranea="" subunit="" subur="" succeed="" success.="" success="" successor="" such="" suffice.="" sufficed.="" sufficient="" sugar="" suggest.="" suggest="" suggested.="" suggested="" suggesting="" suggestion="" suggests.="" suggests="" suicide="" suit="" suitable="" suite="" suited="" summaries.="" summaries:="" summaries="" summarised="" summarize="" summarized.="" summarized="" summarizes="" summarizing="" summary.="" summary:="" summary="" summarycic="" summarycwi="" summarydigital="" summarye="mc²" summaryeinstein="" summaryelectrolux="" summarylargest="" summarystarlink="" summarytic="" summaryunesco="" summit:="" summit="" summits="" summoit="" super-cool="" super-detailed="" super-efficient="" super-fast="" super-smart="" super="" supercharges="" superchip="" supercomouter="" supercomputer.="" supercomputer:="" supercomputer="" supercomputers.="" supercomputers.for="" supercomputers:="" supercomputers="" supercomputing="" supercomuter="" supercoputer="" superfood="" superhero="" superheroes="" superhighway="" superhuman="" superpower="" superpowers.="" superpowers="" superstar:="" superstar="" superstars:="" superstars:moringa:why="" superstars="" supicion="" supoerstars-="" supplements="" supplier="" supplies="" supply.="" supply="" supplying="" support.="" support:="" support:photonics="" support="" supported.="" supported="" supporter="" supporterted="" supporting="" supportive.="" supportive="" supports="" supportto="" supreme="" sure="" surgeon="" surgeries="" surgery="" surgical="" surpass="" surpassed="" surpasses="" surpassing="" surprise="" surprised="" survival="" suspect="" suspended="" sustaimnable="" sustainability.="" sustainability.format:="" sustainability:="" sustainability:arm1="" sustainability:king="" sustainability="" sustainable="" sustainably="" sustained="" sustaining="" svms="" swahili:="" swahili:why:="" swahili="" swarm="" sway.="" sweden="" swedish="" switzerland.="" switzerland="" sxecretary="" sxsw="" sycamore="" sydney="" syllabi="" syllables="" symbol="" symbolic="" symbolizes="" symbolizing="" sympathies="" synaptic="" sync="" syncs="" synergies.1.="" synergies="" synergy.="" synergy:="" synthesis="" synthesized="" synthetic="" system.="" system="" systematically="" systemic="" systems.="" systems:="" systems:cwi="" systems:nuclear="" systems:virtuous:="" systems="" szil="" t-cell="" t-mobile="" t.="" t="" tachers="" tackle="" tackled="" tackles="" tackling="" tactics="" tags="" taifa="" tailor="" tailored="" tailoring="" taipei-1="" taipei="" taiwan-africa="" taiwan-centric="" taiwan-india="" taiwan-u.s.="" taiwan.3.="" taiwan.="" taiwan:="" taiwan:issue:="" taiwan:unesco="" taiwan="" taiwans="" taiwn="" take="" takeaway:="" takeda="" taken="" takes="" taking="" talent.="" talent:="" talent:immigration="" talent:who="" talent="" talented="" talents="" talk="" talked="" talks="" tamil="" tangible="" tanks="" taoyuan="" taraxacum="" target="" targeted="" targeting="" targets="" tariff="" tariffs="" tarlink="" tasks.="" tasks="" taste="" tatistics="" taught="" taurica="" tax="" taxes="" tb="" tcl="" tcm="" tea:="" tea="" teach:="" teach="" teacher-focused="" teacher="" teachers.="" teachers.i="" teachers:="" teachers:ai="" teachers:einstein="" teachers:facilitate:="" teachers:forbes="" teachers:integrate:="" teachers:integrate="" teachers:le="" teachers:nature="" teachers:not="" teachers:taifa="" teachers:teach:="" teachers:ted-ed="" teachers:the="" teachers:use="" teachers:website:="" teachers:wechat:="" teachers:youtube:="" teachers="" teachersbelow="" teachersfor="" teacherskhan="" teachersnvidia="" teachersstatement:="" teachersteens:engage:="" teachersteens:learn:="" teachersteens:start:="" teachersteens:take="" teachersthese="" teacherstitle:="" teachersto="" teacherswebsite:="" teacherswhat="" teaches="" teaching-focused="" teaching.="" teaching="" team="" teams="" teas="" tecahers="" tech-driven="" tech-focused="" tech-savvy="" tech.="" tech:="" tech:baidu="" tech:cwi="" tech:waymo="" tech="" technical="" technically="" techniques="" technocracy="" technocratic="" technological.="" technological="" technologies.="" technologies:jobs:="" technologies="" technologues="" technology.="" technology:="" technology="" ted-ed.="" ted-ed="" ted="" teded:matches:="" teded:why:="" teded="" tedx="" teen-appealing.="" teen-focused.="" teen-focused="" teen-friendly.="" teen-friendly="" teen="" teenager="" teens.="" teens:="" teens:start:="" teens:you="" teens="" teensyoutube:="" teh="" tel="" telecom="" telecommunications="" telecommuns="" telecoms="" telegraph="" telegraphy="" telemedicine="" telephone="" telephony="" telesat:="" telesat="" telescopes="" tell="" teller.="" telling="" tells="" temperature="" temporary="" tencent="" tennessee.="" tennessee="" tension.="" tensions.="" tensions:world="" tensions="" tensor="" tensorflow="" tensors="" terabytes="" term.="" term:="" term:why="" term="" terms:="" terms="" termsince="" termyou="" terpenes="" terrestrial="" tesla.="" tesla:="" tesla="" test="" tested="" testing="" tests="" teva="" texas.="" texas="" text-to-sign="" text="" textbooks="" texts="" tgain="" th-century="" th="" thailand="" thalassemia.="" thalassemia="" than="" thank="" thanked="" thanks="" that="" the="" theater="" their="" them.="" them="" theme="" themselves="" then="" thenakedscientists.com="" theorem="" theorems.="" theorems="" theoretical="" theoretically="" theories.="" theories="" theorm="" theory.="" theory="" therapeutics.="" therapeutics="" therapies.="" therapies="" therapy.="" therapy="" there="" thereof="" these="" they:are="" they="" thing.="" thing="" things="" think="" thinking="" third-party="" third="" this.="" this.biotech="" this.huang="" this.interpreting="" this:1905="" this="" those="" though="" thought-provoking="" thought.="" thought="" thoughtful:wet="" thoughtful="" thoughts.="" thoughts="" thoujsands="" thousands="" thre-="" thread:="" thread="" threads:einstein="" threads="" threatened="" threatening="" threatens="" threats="" three="" thrive.="" thrive="" thrived="" thrives="" through.="" through:global="" through:massive="" through:relativity="" through="" thye="" tianjin="" tiawan="" tic="" ticket="" tickets="" tie="" tied="" ties.="" ties.ownership:baidu="" ties:investment:="" ties="" tightening="" tik="" tiktok="" tilt="" tilts="" time.="" time:="" time="" timeline.="" timeline:1956="" timeline="" timely="" times:role:="" times="" timing:="" timing="" tip:="" tip="" tips="" tires="" tiring="" title="" titles="" tn="" tnecd="" to.web:0="" to:bridge="" to:build="" to:cultural="" to:implosion="" to:optimize="" to:simulate:="" to:technical="" to:translate="" to="" tobacco="" tocuch="" today.="" today:="" today:python="" today="" together.="" together="" tok="" tokyo.advice="" tokyo.critical="" tokyo="" tolerance="" tolerate="" tolerates="" toll:="" tolman="" tomtom="" ton="" tone="" tonnes="" too.="" too="" took="" tool="" toolbox="" toolkit="" tools.="" tools="" top-selling="" top="" topic="" topics.="" topics="" topping="" tops="" toronto.="" toronto="" tory="" tosince="" total="" totalitarian="" totally="" touch="" touched="" touching="" tough="" tougher="" toured="" touted="" toward="" towards="" towers="" town="" townsend:="" townsend="" townsendrole:="" townsendted="" toxicity="" toyota="" tr="" trach="" track.i="" track="" tracked="" trackers="" tracking.recap:="" tracking="" traction="" tractors="" trade.="" trade="" traded="" trading="" tradition.="" traditional="" traditionally="" traffic="" trail="" trailing="" trails="" train="" trained="" trainers="" training.="" training="" traits:teens:="" trajectoiry="" trajectories="" trajectory="" transaction.="" transational="" transfer.="" transfer:why="" transfer="" transferred="" transferring="" transfers="" transfomration="" transforiming="" transform="" transformation.="" transformation.for="" transformation.i="" transformation:="" transformation:unesco="" transformation="" transformations.="" transformations:="" transformations="" transformative="" transformed="" transforming="" transforms="" transfusions="" transistors="" transition:cic="" transition:the="" transition="" transitions="" translate="" translated="" translates="" translating="" translation.="" translation="" transmission="" transparency="" transparent="" transplant="" transplantation="" transplanted="" transplants="" transport="" travel="" treason="" treasure="" treated="" treatment="" treatments.="" treats="" treaty="" tree="" trees="" trends.="" trends:brain="" trends:european="" trends="" trength:="" trhat="" trial-and-error="" trial="" trials.="" trials="" tricks="" tricky:="" trigger.="" triggering="" triggers="" trilateral="" trillion-dollar="" trillion-parameter="" trillion="" triple="" triterpene="" triterpenes="" triton="" tropical="" trucks.="" true="" trump:="" trump="" trumps="" trumptrump="" trust-building.="" trust-building:="" trust-building="" trust-challenged="" trust.="" trust:="" trust="" trusted.="" trusted="" trusts="" trustworthiness="" trustworthy="" try="" trying="" tsmc-amd="" tsmc-powered="" tsmc-u.s.="" tsmc="" ttorney="" tuition.="" tumor="" tuna="" turbines.="" turbines="" turbulence="" turing.="" turing.conclusion:="" turing:="" turing:e="mc²" turing:einstein="" turing:turing="" turing:vision:="" turing="" turingyou="" turkey="" turkovac="" turmeric.="" turmeric="" turmerones:="" turn="" turning="" tutorial="" tutorials.="" tutorials="" tutoring="" tv="" tva="" tweak="" tweaks="" tweets="" twin.="" twin:="" twin="" twinned="" twinning.="" twinning:="" twinning="" twinningdigital="" twinningyou="" twinningyour="" twins.="" twins:photonics="" twins="" twitch.tv="" twitch:="" twitch="" twitter.com="" twitter:="" twitter="" two="" tying="" type="" types="" typically="" typo="" u.s.-centric="" u.s.-controlled="" u.s.-developed="" u.s.-taiwan="" u.s..="" u.s.="" u="" uae.="" uae="" uang="" uanta="" uantamagazine:why:="" uantamagazine="" uantum="" uawei="" ubjects="" uclear="" ugaku="" uganda="" uild="" uiper="" uk-eu="" uk.="" uk="" ukesh="" ukraine="" ukusa="" ulane="" uld="" ules31415="" ultilateral="" ultimately="" ultural="" umanizing="" umanoids:="" umc="" ummarize="" ummitlondon2025="" un="" unattainable="" unaware="" unbiased="" uncertainties="" uncertainty.="" uncertainty:="" uncertainty="" unchecked="" unclear.="" unclear="" unconstitutional.="" unconventional="" under="" underexplored:="" underexplored="" underfunded="" underfunding="" undergoes="" undergraduate="" underlying="" undermine="" undermined="" undermines.="" undermines="" undermining="" underpin="" underpinned="" underpinning="" underpinnings="" underpins="" underreport="" underreporting="" underreports="" underrepresented="" underscore="" underscores="" underserved.="" underserved="" understand="" understanding="" understate="" understood.="" understood="" undervalue="" undervalued:="" undervalued="" undervalues="" undervaluing="" underweight="" undiminished="" une="" unencumbered="" unequal="" unesco.="" unesco:="" unesco:su="" unesco="" unescoyou="" uneven="" unevenly="" unexplored="" unfamiliar="" unhackable="" unicef.="" unicef:your="" unicef="" unified="" unify="" unifying="" unilateralism.="" unintended="" union="" unique="" uniquely="" unit="" unite.4.="" unite="" united="" uniting="" units="" unity.="" unity:="" unity="" universal="" universality="" universe="" universiteit="" universities="" university="" unix="" unknown-="" unknown="" unlawful="" unleashing="" unless="" unlike="" unlikely.="" unlikely="" unlock="" unmatched.="" unpack="" unparalleled="" unprecedented="" unpredictable="" unproductive.="" unpublished="" unrelated="" unresolved="" unsaponifiables="" unseen="" unsolvable="" unsuitable="" unsure:why="" unsure="" unsustainability="" unsustainabilitychallenging="" unsustainable="" unsustainableyou="" until="" up.="" up:="" up="" upcoming="" update="" updated="" updates.="" updates="" upgrades="" upgrading="" uploads="" upon="" upport="" upreme="" upset="" uranium="" urban-centric="" urban="" urcuma="" urgency:="" urgency:support="" urgency="" urgent="" urging="" uring="" urious="" urls="" urope="" urpose:="" ursula="" uruguay="" urzgesagt="" us-based="" us-china-taiwan="" us-china="" us-japan="" us.="" us="" usa.="" usa="" use.="" use:="" use="" used="" user-centric="" user-facing="" user="" users="" uses.1.="" uses.="" uses="" usg="" using:hardware:="" using="" usk-deep-maths="" usk="" ussian="" ussr="" ustin="" ustralia="" ustronesian="" usually="" ut="" utility="" utmeg:="" utonomous="" utterberry="" uturenow="" uv="" v2="" v3="" v="" vacant="" vaccine:="" vaccine="" vaccines.="" vaccines:="" vaccines="" vague="" vagueness:="" valid="" validating="" validation="" valley.2.="" valley.="" valley="" valuable="" valuating="" valuation="" value-added="" value.="" value="" valued="" values="" valuing="" van="" vance="" vanderbilt="" variability:="" variable="" variant="" variation="" varieties="" various="" vary="" varying="" ve="" vector="" vegan="" vegas="" vegetables="" vehicle="" vehicles.="" vehicles="" vein="" velocities.="" velocities:="" velocities="" velocity="" venice="" vent:="" venture="" ventures:starlink:="" ventures="" verifiable="" verification:="" verification="" verified.="" verified="" verify:evidence:amd="" verify="" verifying="" verigy="" veritasium:why:="" veritasium="" versailles="" versatile="" versatility.="" versatility="" version.="" version="" versus="" vertex="" vertical="" verum="" very="" via:hardware:="" via="" viable.="" viable="" vibe="" vibes="" vibration="" vice="" vicinity="" vicious:="" vicious="" viciously="" vidence:="" vidence="" video.="" video="" videos.="" videos="" vidia="" vie="" vietnam-eu="" vietnam.="" vietnam="" vietnamese="" view.50="" view.="" view:="" view:ethical="" view="" viewed="" viewers="" viewiews="" viewing="" viewpoint="" views.="" views:later="" views="" vigyan="" village="" villages="" villain="" vince.="" vince:="" vince="" vinod="" violates="" violations="" violence="" violent="" viral="" virology="" virtual="" virtually="" virtulisly="" virtuous="" viruses="" visa="" visas.="" visas="" visibility="" visible="" vision.="" vision:="" vision:ai="" vision:intellectual="" vision:unesco="" vision="" visionary="" visioneinstein="" visions="" visit.="" visit:="" visit="" visited="" visiting="" visitor="" visitors="" visits.="" visual="" visualization.="" visualize="" visualizes="" visuals="" vital:="" vital="" vitality.="" vitality:="" vitality="" vitamin="" vitamins="" vitellaria="" vlsi="" voc="" vocal="" vodafone:role:="" vodaphonre="" voice-activated="" voice="" void.="" void="" volkswagen.="" volkswagen="" voltage="" volume="" volution="" von="" vp="" vr="" vrije="" vs.="" vs="" vulnerability="" vulnerabilitytrump="" vulnerable="" waether="" wait="" walkable="" walter="" waned="" wang="" wanna="" want="" wanted="" war-torn="" war.="" war="" warehouse="" warehousing="" warfare="" warning:="" warning="" warns:="" warns="" wars.="" wars:="" wars:world="" wars="" warsyou="" wartime="" wary="" was="" washington="" washingtondc.jhu.edu:ai="" washingtondc.jhu.edu:resources="" wasn="" waste="" watch.="" watch:="" watch="" water="" watsonx="" wave="" waves:="" waves="" way="" waymo.="" waymo="" ways.="" ways="" we="" weakening="" weakens="" weaker="" weakness:="" weaknesses:limited="" weaknesses:published="" wealth="" weapon="" weapons="" weather.="" weather="" weaving="" web6="" web:0="" web:10="" web:11="" web:12="" web:14.="" web:14="" web:15.="" web:15:="" web:15="" web:17="" web:1="" web:21="" web:22="" web:23="" web:2="" web:3="" web:4="" web:5="" web:6.="" web:6="" web:="" web="" webinars.="" webinars="" website:="" website="" websites:="" websites:white="" websites="" websitesfor="" wechat.="" wechat="" weden="" week="" weekly="" weeks.="" weeks="" weibo.="" weibo.com="" weibo:="" weibo="" weight:e="mc²’s" weight="" welcome="" welfare.="" welfare="" well-known="" well="" wellness="" weltpartner="" wema="" went="" were:china="" were="" weren="" weride.="" weride="" west="" western-centric="" western-centrism.="" western="" wet="" weyl:="" weyl="" what="" whatever="" whats="" wheat="" wheels="" wheis="" when="" where:="" where="" whereas="" whether="" which="" while="" white="" whitehaven="" who-gavi="" who="" whoce="" whoich="" whole="" wholly-owned="" whose="" why:="" why:newton="" why:saves="" why="" wi-fi="" wide="" widely="" widen="" wider="" widespread="" wiener="" wikipedia="" wild="" wildlabs="" wildlife="" will="" willem="" willingness="" wilson="" win="" wind="" wing.="" winning="" winter="" wires="" wisconsin-based="" wishes="" wiskunde="" wissenschaften="" wistron="" with.="" with="" withamsterdam="" withdrawal="" withdrawing="" within="" without="" witter="" witzerland="" wizard="" wizards="" wolfram="" wolframalpha.com:why:="" women-led="" women="" won="" wondering="" word.="" word="" words="" work.="" work.recursion="" work:="" work="" worked="" workers.="" workflows="" workforce="" working="" worklkd="" workloads="" workout="" works:="" works="" workshops="" world.="" world.training="" world="" worlds.="" worlds="" worldwide.="" worldwide="" worry="" worsened="" worst="" worth="" would="" wouldn="" wound="" wow-in-the-world="" wow="" write="" writer="" writings="" written="" wrong="" wrote="" wuhan="" wuxi="" wwii.="" wwii="" www.aatf-africa.org:why:="" www.agri.cn:why:="" www.annenbergclassroom.org="" www.ans.org="" www.bbc.com="" www.cirad.fr:why:="" www.coa.gov.tw:why:="" www.cyol.com:why:="" www.federalregister.gov="" www.forbes.com:why:="" www.icivics.org="" www.kepuchina.cn:why:="" www.lemonde.fr:why:="" www.maff.go.jp:why:="" www.nature.com:why:="" www.precisionag.com:matches:="" www.science-et-vie.com="" www.taifaleo.nation.co.ke:why:="" www.theaisummit.com="" www.whitehouse.gov="" www.x.ai="" www.xinhuanet.com:why:="" www.xinhuanet.com="" x-deep-maths:strength:="" x-deep-maths="" x-linked="" x-mediated="" x-promoted="" x-ray="" x-shared="" x.="" x.com.="" x.com:discussions="" x.com:why:="" x.com="" x:="" x="" xai-branded="" xai-owned="" xai.="" xai:="" xai:why:="" xai="" xcited="" xcluding="" xeced="" xenotransplantation.="" xenotransplantation:="" xenotransplantation="" xenotransplants="" xi="" xinhua="" xor="" xp-deep-maths:strength:="" xp-deep-maths="" xpeng="" xponential="" xponentialfuture="" xponentially="" xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx="" y="" yale="" yan="" yann="" year-old="" year-olds="" year.="" year="" yearly.="" yearly="" years.="" years:why="" years="" yes:="" yes="" yesterday="" yet="" yhuge="" yield="" yields="" ynamic="" yo="" yoiung="" york="" you="" youi="" young="" younger="" your="" youth-friendly="" youth-relevant:="" youth-relevant="" youth.="" youth:="" youth="" youthful="" youtube.="" youtube.com="" youtube:="" youtube="" yrar="" ythinktank="" ython="" yummy="" yup="" ywinning="" yzygium="" z.="" z="" za="" zeekr="" zeneca="" zimbabwe="" zionism="" zip="" zones="" zoom="" zurich="">
