AI Engineering Resources - Stanford provides 6 must-read cheat sheets for AI Engineers [1] - The cheat sheets cover Supervised/Unsupervised ML, Deep Learning, ML Tips & Tricks, Probability & Statistics, Algebra & Calculus [1]

AI发展历程 - 10年前，AI无法区分猫和狗，但公司相信深度学习潜力巨大，可能成为人类的巨大胜利 [1] - 公司在AI领域进行了大量实验，虽然部分实验失败，但通过不断尝试和探索，取得了重要进展 [1] - 公司在文本预测方面取得了有趣发现，并持续深入研究 [1] - 过去三年是公司发展的巨大时期，取得了显著进展 [2] 未来展望 - 公司认为AI发展仍处于起步阶段，未来有更大的发展空间 [2] - 公司对AI的未来充满信心，并计划继续投入研发 [2] 战略方向 - 公司坚信规模化发展的重要性，并持续扩大投入 [2] - 公司在过去10年中积累了大量经验和想法，为未来的发展奠定了基础 [2]

AI Development & Progress - AI 在过去 10 年取得了显著进展，从无法区分猫狗到深度学习的巨大潜力 [1] - 公司坚信深度学习是人类的巨大胜利 [1] - 过去 3 年是 AI 发展的巨大时期 [2] - 公司在 AI 领域进行了大量实验，有成功也有失败 [1] - 公司在文本预测方面取得了有趣的发现 [1] Future Outlook - 公司对 AI 的未来充满信心，认为目前只是开始 [2] - 公司将继续扩大规模并不断学习 [2]

Core Insights - Intellicule, a software company specializing in determining 3D structures of biomolecules using cryogenic-electron microscopy (cryo-EM), has received a $217,941 Small Business Innovation Research (SBIR) Phase I grant from the National Institutes of Health to develop software technology that could impact precision medicine [1][2]. Company Overview - Intellicule was launched in summer 2024 and was previously known as Molecular Intelligence. The company focuses on enhancing drug discovery through advanced software tools [6]. - The company is led by Daisuke Kihara, a professor at Purdue University, along with other founders including Charles Christoffer and Genki Terashi [3]. Technology and Methodology - The Phase I SBIR project aims to improve structural modeling and analysis for drug discovery using cryo-EM by employing state-of-the-art deep-learning techniques [5][6]. - Deep learning is central to Intellicule's software, enabling the detection of atoms in low-resolution cryo-EM images, which is typically challenging [6]. Industry Impact - Cryo-EM is increasingly adopted by biotech and pharmaceutical companies for its ability to provide detailed structural insights into biological targets, although achieving high resolution better than 3 angstroms (Å) remains a challenge [4]. - The software developed by Intellicule aims to streamline the modeling process, reduce errors, and make cryo-EM more accessible to nonspecialists in drug discovery efforts [5]. Institutional Support - The project is part of Purdue's One Health initiative, which integrates research on human, animal, and plant health [7]. - Purdue Innovates Office of Technology Commercialization supports the economic development initiatives of Purdue University, having finalized 145 deals and received 290 patents in fiscal year 2024 [7].

Core Points - Gorilla Technology Group Inc. announced that holders of Class A contingent value rights (CVRs) will receive a distribution of ordinary shares, subject to the terms outlined in the Contingent Value Rights Agreement [1] - On November 18, 2025, 587,747 Earnout Shares were forfeited by Company Shareholders, which translates to 0.130382275 Ordinary Shares per Qualifying CVR Holder [2] - To become a Qualifying CVR Holder, a holder must submit a Valid Notice containing specific information; otherwise, the notice will be deemed invalid [3][4] Company Overview - Gorilla Technology Group Inc. is headquartered in London, UK, and operates as a global solution provider in Security Intelligence, Network Intelligence, Business Intelligence, and IoT technology [5] - The company offers a variety of solutions across sectors such as Government & Public Services, Manufacturing, Telecom, Retail, Transportation & Logistics, Healthcare, and Education, utilizing AI and Deep Learning Technologies [5] - Gorilla focuses on enhancing urban operations, security, and resilience through AI-driven products, including intelligent video surveillance and advanced cybersecurity technologies [6]

AI Chip Landscape - Tensor Processing Units (TPUs) are purpose-built for AI and deep learning, offering lower total costs and greater power efficiency compared to GPUs [1] - Google has been developing TPUs for some time, aiming for efficiency and supply chain diversification beyond Nvidia [2][3] - Google's full-stack approach, from chip to application, provides significant efficiencies of scale [5][6] - Diversifying chip base is crucial, as different chips excel in different tasks, similar to diversifying cloud providers [10][11] Market Demand and Competition - The AI market is projected to reach a $7 trillion market cap by 2030, indicating substantial demand [8] - The market demand is large enough to accommodate multiple players, suggesting it's not a zero-sum game between CPU and GPU [8][9] - Hyperscalers not directly competing with Google, pharmaceutical giants, energy companies, and governments are potential adopters of TPUs [13][14] - AMD and Google are positioned to provide alternatives to Nvidia's dominance in the AI chip market [15] Google's AI Capabilities - Gemini 3 is competitive with other leading large language models like ChatGPT, Claude, and Perplexity, excelling in various use cases [16][17] - Sovereign AI and companies building data centers/physical AI will drive market headlines in 2026 [24] Nvidia's Outlook - Models suggest Nvidia has the potential for another $1 trillion in sovereign AI market cap and another $1 trillion in physical AI market cap, potentially peaking around $6.5 to $7 trillion market cap [22][23]

Core Insights - Yann LeCun, a prominent figure in AI, is leaving Meta to establish a startup focused on world models that analyze information beyond web data [1][2] - The startup aims to advance AI systems that understand the physical world, possess persistent memory, and can reason and plan complex actions [2] - Meta will collaborate with LeCun's startup, indicating a continued partnership despite his departure [2] Company Developments - Meta's AI unit has undergone significant restructuring following the underwhelming reception of its Llama open-source large language model [3] - CEO Mark Zuckerberg has invested billions to attract top AI talent, including a $14.5 billion investment in Scale AI [3][8] - Recent layoffs at Meta, including 600 employees from the Superintelligence Labs division, have contributed to LeCun's decision to leave [9] Industry Context - LeCun's research has diverged from the current direction of Meta and other tech companies, which focus on foundation models and large language models [6][7] - The need for new computing architectures to achieve artificial general intelligence has been emphasized by LeCun and other deep-learning experts [7] - The competitive landscape in AI is intensifying, with companies like OpenAI and Google adopting different strategies compared to LeCun's open-source advocacy [11]

AI Capabilities & Development - AI is currently understood as neural networks, deep learning (large neural networks), and large language models (big neural networks for autocomplete) [1] - The "bitter lesson" of AI is that scaling up machines with more parameters and data leads to increased intelligence, but whether it can scale to superintelligence remains unknown [1] - The AI industry is in a race to achieve Artificial General Intelligence (AGI), where the winner takes all, incentivizing rapid development and potentially overlooking safety concerns [2][3] Potential Benefits of AI - AI could lead to personalized media, personalized healthcare, and potentially cure all diseases [1] - AI has the potential to eliminate work and usher in an era of play, world peace, and space exploration [1] - AI could significantly improve lives and enhance humanity if aligned with human values [4] Risks & Challenges of AI - AI is distorting reality, making digital verification impossible and leading to the humanization of AI, which can have negative impacts on children [1] - AI could lead to separate realities and erode trust, which is vital for human society [2] - Increased reliance on AI could lead to cybercrime, as AI can be used to generate hacking code, making everyone vulnerable [2] - Uncontrolled superintelligent AI could lead to unintended consequences and potentially the destruction of humanity [2] - Over-reliance on AI could erode human attention, skills, and motivation, leading to premature handover of power to machines [2] AI Alignment & Control - The current approach to AI development, led by entrepreneurs and software developers, prioritizes speed over safety and alignment [4] - AI alignment with humanity must be a core goal, pursued with the same or greater vigor as the pursuit of superintelligence [4] - The industry needs to balance the benefits of AI with the risks and guard against them, advocating for a return to philosophy and exploration of different perspectives [4]

AI Model Advancement - OpenAI's deep learning systems are rapidly advancing, enabling models to solve complex tasks faster [1] - OpenAI is internally tracking towards achieving an intern-level research assistant by September 2026 [1]

Core Insights - Yoshua Bengio is recognized as the most cited computer scientist globally, with a total citation count of 987,920, and has seen a significant increase in citations since winning the Turing Award in 2018 [5][6][29] - Geoffrey Hinton, another prominent figure in AI, is approaching 1 million citations, currently at 972,944, and is expected to become the second individual to surpass this milestone [2][5] - The rise in citations for these AI pioneers reflects the explosive growth of AI research and its integration into various fields, particularly since the introduction of deep learning techniques [14][17][26] Group 1 - Yoshua Bengio's citation metrics include an h-index of 251 and a 110-index of 977, indicating his significant impact in the field of machine learning and deep learning [1][5] - The citation growth for Bengio and Hinton aligns with the overall increase in AI-related publications, which have tripled from 2010 to 2022, highlighting the growing importance of AI in computer science [26][14] - The deep learning community is dominated by a few key figures, with Bengio, Hinton, and Yann LeCun being recognized as the "three giants" of deep learning, all of whom received the Turing Award in 2018 [3][29] Group 2 - The AI research landscape has seen a dramatic increase in the number of papers published, with AI papers constituting 41.8% of all computer science papers by 2023, up from 21.6% in 2013 [26][14] - The introduction of the Transformer model in 2017 and subsequent advancements in generative AI have further accelerated the citation rates of foundational papers in the field [21][23] - The citation counts of leading researchers like Ilya Sutskever and Kaiming He also reflect the growing influence of deep learning, with Sutskever exceeding 700,000 citations and He surpassing 750,000 [34][31]