老子的"道"：宇宙的底层代码 我第一次读《道德经》时，刚读一句就想放弃，因为老子在《道德经》中开篇就上了强度，他说："道 可道，非常道。" 多年后我才明白，这句话说的是：真正的"道"，无法用语言描述，凡是可以言说的"道"，都只是表象， 无法触及那个永恒、普遍、无形无相的"道"。 换句话说，任何终极的实在（道）永远不会成为可推理、可论证的知识，我们也永远无法用语言来适当 地描述它，因为它超出了感觉和理智的范畴，而我们的言辞和概念都是从感觉和理智中得来的。 当今的物理学，也常常让人感到"道"的不可言说。 你有没有想象过光的波粒二象性？ 就连爱因斯坦这样的科学巨匠，也曾对量子物理的诡异之"道"感到困惑。他与玻尔进行了长达数十年的 著名辩论，质疑量子世界的随机性和不确定性，甚至说出"上帝不掷骰子"这样的话。 爱因斯坦无法接受量子物理那种超越直觉的"道"，但实验结果却一次次证明了量子物理的正确性。 我们从小接触的世界，物体要么是粒子（有确定位置、质量），要么是波（弥散开来、有波长频率）。 一个东西能同时是这两者，或者在不同情况下表现出这两种截然不同的性质，这完全违背我们的经验。 你无法想象一个棒球既能像一个坚硬的球一样 ...

Group 1 - Geoffrey Hinton, the AI pioneer, recently sat down comfortably in Shanghai, marking a significant moment in his life after nearly 18 years of discomfort that prevented him from sitting for extended periods [1][6][30] - Hinton's journey in AI began in 1972 when he chose to pursue neural networks, a path that was largely dismissed by his peers at the time [12][20] - His persistence in the field led to breakthroughs in deep learning, particularly during the ImageNet competition in 2012, where his team achieved a remarkable error rate of 15.3% [30][31][32] Group 2 - Hinton's contributions to AI were recognized with the Turing Award in 2019, which he received while standing, reflecting his long-standing discomfort with sitting [59][63] - Following his resignation from Google in May 2023, Hinton expressed concerns about the risks associated with AI, stating that he regretted his role in its development [67][68] - In recent interviews, Hinton has been able to sit for longer periods, indicating a potential improvement in his health, and he has been vocal about the dangers of AI, suggesting a 10%-20% chance of human extinction due to AI in the next 30 years [70][76]

Core Insights - The conversation between Lex Fridman and Demis Hassabis focuses on the future of artificial intelligence (AI), particularly the potential for achieving Artificial General Intelligence (AGI) within the next five years, with a 50% probability of success [3][4] - Hassabis emphasizes the ability of classical machine learning algorithms to model and discover patterns in nature, suggesting that all evolutionary patterns can be effectively modeled [5][10] - The discussion also highlights the transformative impact of AI on video games, envisioning a future where players can co-create personalized, dynamic open worlds [3][28] Group 1: AI and AGI - Demis Hassabis predicts a 50% chance of achieving AGI in the next five years, asserting that all patterns in nature can be modeled by classical learning algorithms [3][4] - The conversation explores the idea that natural systems have structure shaped by evolutionary processes, which can be learned and modeled by AI [9][12] - Hassabis believes that building AGI will help scientists answer fundamental questions about the nature of reality [3][4] Group 2: AI in Gaming - The future of video games is discussed, with Hassabis expressing a desire to create games that allow for dynamic storytelling and player co-creation [28][32] - He envisions AI systems that can generate content in real-time, leading to truly open-world experiences where every player's journey is unique [32][33] - The potential for AI to revolutionize game design is highlighted, with Hassabis reflecting on his early experiences in game development and the advancements in AI technology [38][39] Group 3: Computational Complexity - The conversation touches on the P vs NP problem, with Hassabis suggesting that many complex problems can be modeled efficiently using classical systems [15][17] - He believes that understanding the dynamics of systems can lead to efficient solutions for complex challenges, such as protein folding and game strategies [19][20] - The discussion emphasizes the importance of information as a fundamental unit of the universe, which relates to the P vs NP question [16][17] Group 4: AI and Scientific Discovery - Hassabis discusses the potential of AI systems to assist in scientific discovery by combining evolutionary algorithms with large language models (LLMs) [49][51] - He highlights the importance of creativity in science, suggesting that AI may struggle to propose novel hypotheses, which is a critical aspect of scientific advancement [59][60] - The conversation emphasizes the need for AI to not only solve problems but also to generate new ideas and directions for research [60][62] Group 5: Future Aspirations - Hassabis expresses a long-standing ambition to simulate a biological cell, viewing it as a significant challenge that could lead to breakthroughs in understanding life [64][65] - He reflects on the importance of breaking down grand scientific ambitions into manageable steps to achieve meaningful progress [64][65] - The conversation concludes with a vision for the future of AI, where it can contribute to both gaming and scientific exploration, merging creativity with computational power [39][64]

Core Insights - The discussion revolves around the potential of AI to reach a transformative point akin to AlphaGo's "move 37," suggesting that AI may be approaching a critical technological shift [1][30] - Demis Hassabis warns of the risks associated with AI advancements, emphasizing the need for cautious optimism [1][30] Group 1: AI and Natural Systems - Hassabis believes that all natural models can be efficiently modeled through classical learning algorithms, particularly in fields like biology, chemistry, and physics [4][5] - The probability of achieving Artificial General Intelligence (AGI) by 2030 is estimated at around 50%, with benchmarks including the ability to propose new scientific hypotheses [4][30] - AI systems like AlphaGo and AlphaFold demonstrate the capability to solve complex problems through intelligent guided searches [4][5] Group 2: AI's Understanding of Reality - The Veo 3 model showcases an impressive ability to generate realistic videos and demonstrates a form of "intuitive physics" understanding [7][8] - Hassabis expresses surprise at Veo 3's ability to learn from video observation without physical interaction, challenging previous assumptions about AI's need for embodiment to understand the physical world [9][10] Group 3: Future of Gaming with AI - Future gaming experiences may be revolutionized by AI, allowing for dynamic story generation based on player decisions, creating a more immersive experience [12][13] - Hassabis envisions a future where AI can create truly open-world games that respond in real-time to player choices, enhancing the gaming experience [12][13] Group 4: Evolutionary Algorithms and AI Innovation - The recently released AlphaEvolve system utilizes evolutionary algorithms to explore new solution spaces, combining large language models with evolutionary computation [18][19] - Hassabis believes that understanding the underlying dynamics of systems is crucial for discovering new solutions and that evolutionary computation can lead to significant breakthroughs [18][19] Group 5: AI's Role in Scientific Research - Hassabis discusses the concept of "research taste," suggesting that while AI can solve complex problems, it currently lacks the ability to propose profound scientific hypotheses [22][23] - The challenge lies in AI's ability to discern the right questions and hypotheses, which is a critical aspect of scientific research [23][24] Group 6: Future Energy Sources - Hassabis predicts that nuclear fusion and solar energy will become the primary energy sources in the future, addressing energy challenges and potentially leading to a Kardashev Type I civilization [43][44] - The development of efficient solar materials and nuclear reactors could enable humanity to harness abundant, clean energy [43][44] Group 7: Competition in AI Development - Hassabis emphasizes the importance of collaboration in AI research, stating that the goal is to safely bring technology to the world for the benefit of humanity [47][48] - The competition for talent in AI is intensifying, with companies like Meta employing aggressive strategies to attract top researchers [51]

AI for Science & AlphaFold Overview - AI systems can accelerate scientific discovery and enable new breakthroughs, particularly in healthcare [1] - AlphaFold, a system developed for protein structure prediction, has been cited approximately 35,000 times, demonstrating its impact on scientific research [1] - The speaker's guiding principle is to build tools that enable scientists to make discoveries [1] Protein Structure Prediction & Biological Significance - Proteins, numbering around 20,000 different types in humans, perform nearly every function in a cell [1] - Determining protein structure is exceptionally difficult, often requiring years of effort and significant resources, costing around $100,000 [2] - There are approximately 200,000 known protein structures, with roughly 12,000 new structures being added annually [2] - Protein sequence discovery is happening approximately 3,000 times faster than protein structure determination [2] AlphaFold Development & Key Factors - AlphaFold's success was driven by data (200,000 protein structures), compute (128 TPU V3 cores for two weeks), and, most importantly, research and innovative ideas [2] - Research and novel ideas were approximately 100 times more valuable than the data used in training AlphaFold [3] - Mid-scale ideas, rather than just scaling transformers, are crucial for building transformative AI systems [2][3] Impact & Applications of AlphaFold - AlphaFold has enabled scientists to make discoveries in areas like vaccine and drug development, and understanding how the body works [1] - The release of the AlphaFold database, containing approximately 200 million protein structure predictions, significantly increased its adoption and impact [3] - Researchers are using AlphaFold in unexpected ways, such as predicting protein interactions and engineering proteins for targeted drug delivery [5][6] - AlphaFold is estimated to have accelerated the field of structural biology by approximately 5-10% [9]

Core Viewpoint - Isomorphic Labs is preparing to initiate human trials for AI-designed drugs, marking a significant milestone in its drug development journey [1][2][3]. Group 1: Company Overview - Isomorphic Labs, a spin-off from DeepMind, was established in 2021 and is focused on utilizing AI for drug discovery [3][6]. - The company has raised $600 million in its first round of external financing, led by Thrive Capital [1][8]. Group 2: Technological Advancements - The company leverages AlphaFold, an AI system capable of accurately predicting protein structures, to enhance drug design processes [3][4][6]. - AlphaFold's capabilities have evolved to simulate interactions between proteins, DNA, and drugs, facilitating more precise drug development [4][5]. Group 3: Collaborative Efforts - In 2024, Isomorphic Labs formed significant research collaborations with major pharmaceutical companies, Novartis and Eli Lilly [7]. - These partnerships aim to create a "world-class drug design engine" that integrates machine learning researchers with experienced pharmaceutical professionals [9]. Group 4: Drug Development Strategy - Isomorphic Labs is not only supporting existing drug projects but also independently designing internal drug candidates in fields like oncology and immunology [10]. - The company identifies unmet medical needs and initiates drug design projects, aiming to advance them to human clinical trial stages [11]. Group 5: Market Impact and Vision - Traditional pharmaceutical companies often spend millions to bring a drug to market, with a success probability of only 10% even after trials begin [12]. - Isomorphic Labs aims to significantly improve these success rates by accelerating development speed and reducing costs, with a vision of generating treatment designs at the click of a button using advanced AI tools [12].

Core Insights - AI is rapidly evolving and is being integrated into various fields, including education and research, enhancing capabilities and expanding possibilities [1][3][4] - The introduction of AI tools like AlphaFold and AlphaGenome is revolutionizing biological research by allowing scientists to approach problems from new angles, fundamentally changing research methodologies [3][4] Group 1: AI in Research - AI is being utilized daily by researchers to select topics and improve research efficiency [3] - The emergence of AlphaFold has transformed the approach to biological research, enabling a reverse methodology that allows for the exploration of biological functions from structural data [3][4] Group 2: Collaboration and Interdisciplinary Approaches - There is a strong emphasis on the need for collaboration among researchers across different disciplines to foster innovative thinking [4] - AI is seen as a tool that can enhance cognitive processes, encouraging researchers to leverage its capabilities for better outcomes [4]

Core Insights - The rapid development of AI is significantly impacting life sciences research, emphasizing the need for critical thinking and foundational research skills among students [1][3]. Group 1: AI in Research - AI tools like AlphaFold enable precise predictions of protein structures, allowing researchers to explore vast numbers of homologous structures and their genetic sequences [3][4]. - The breakthrough in protein structure prediction by AlphaFold allows a shift in biological research methodologies, enabling a reverse approach from biophysics to biological functions, thus uncovering new phenomena and mechanisms [4]. Group 2: Educational Recommendations - Students, regardless of their research focus, should prioritize foundational training in scientific logic and critical thinking to effectively utilize AI advancements [3][4]. - Collaboration among students, principal investigators (PIs), and interdisciplinary exchanges is encouraged to enhance research outcomes and leverage AI as a supportive tool [4].

Group 1 - Google AI's Isomorphic Labs is set to begin its first human trials for AI-designed cancer drugs, leveraging the advancements from the AlphaFold model [1] - Alibaba Cloud has officially open-sourced its web intelligence agent WebSailor, which has shown superior performance compared to other models in both open-source and closed-source categories [1] - Tencent's Hunyuan3D-PolyGen is the first art-grade 3D generation model aimed at improving modeling efficiency for artists by addressing challenges in 3D asset generation [2] Group 2 - Zhiyuan has launched the Lingxi X2-N humanoid robot, featuring a dual-mode design that allows it to switch between wheeled and bipedal movement, enhancing its ability to navigate complex terrains [3] - The "New Generation Black Panther 2.0" has set a new world record for robotic dogs, achieving a speed of 10.3 meters per second, surpassing the previous record held by Boston Dynamics [4] Group 3 - ByteDance has denied reports of selling TikTok's U.S. operations to a consortium led by Oracle, emphasizing that the information is false [5] - Apple has appealed against a €500 million fine imposed by the EU for violating the Digital Markets Act, arguing that the penalty exceeds legal boundaries [6] Group 4 - A report by AlixPartners predicts that only 15 out of 129 electric vehicle brands in China will achieve financial sustainability by 2030 due to intense competition leading to market consolidation [7] - Changxin Storage has initiated its listing guidance, with major financial institutions involved, focusing on the development and production of DRAM products [8] Group 5 - The U.S. has announced new tariffs on 14 countries, with rates ranging from 25% to 40%, set to take effect on August 1 [8]

Group 1 - Prominent investor Mark Cuban predicts that AI will create the world's first trillionaire, likely not from traditional wealthy backgrounds [2] - Cuban emphasizes that the impact of AI is comparable to the advent of the internet or cloud computing, suggesting that those who can integrate AI into everyday life will reap significant rewards [2] - Isomorphic Labs, a company spun off from Google DeepMind, is preparing to start its first human trials for AI-designed cancer drugs [3] Group 2 - Isomorphic Labs was established in 2021 and is leveraging AI technology to assist in the development of cancer treatments, building on DeepMind's breakthrough research with AlphaFold [3] - AlphaFold is recognized for its ability to predict protein structures with unprecedented accuracy, and Isomorphic has signed significant research collaboration agreements with major pharmaceutical companies like Novartis and Eli Lilly [3] - Alibaba Cloud has officially open-sourced its web intelligence agent, WebSailor, which has shown superior performance compared to other open-source models and is second only to closed-source models like OpenAI's DeepResearch [4] Group 3 - The robotics company Star Era has completed nearly 500 million yuan in Series A financing, led by Dinghui VGC and Haier Capital, with participation from several notable financial and industrial investors [5] - Star Era has developed service-oriented wheeled humanoid robots and full-sized bipedal robots for industrial applications [5] - Capgemini has announced a $3.3 billion acquisition of business process management company WNS to enhance its AI capabilities, with a final agreement reached at $76.50 per share [6]