Keywords：AGI Quantification Standards, GPT-5, OpenAI Scientific Research Team, AI Fusion Energy, Deepfake Video Ethics, AI Model Command Tone, MoE Reinforcement Learning, AI Red Team, AGI Evaluation CHC Theory, GPT-5 Pro Physics Breakthrough, AI-controlled Tokamak Plasma, Sora Deepfake Ban, Rude Commands Improve AI Accuracy
🔥 Focus
AGI Quantitative Standard Released : Yoshua Bengio, in collaboration with the Center for AI Safety and other organizations, published the paper “A Definition of AGI,” proposing a measurable definition for Artificial General Intelligence (AGI). This definition uses “well-educated adults” as a reference and is based on the Cattell-Horn-Carroll (CHC) theory, designing an assessment question bank covering 10 core cognitive domains. GPT-5 currently scores 58/100, showing significant progress in AI in areas like knowledge, literacy, and mathematics, but still revealing notable shortcomings in fundamental cognitive domains such as perception, memory, and reasoning, exposing the “pseudo-omnipotence” nature of AI. This landmark definition provides a concrete direction for AGI assessment and development. (Source: 量子位)
OpenAI Forms Science Research Team, GPT-5 Pro Shows Breakthroughs in Physics : OpenAI has established the “OpenAI for Science” team, dedicated to building AI systems that accelerate new discoveries in mathematics and physics. Black hole physicist Alex Lupsasca announced his joining, revealing that GPT-5 Pro can solve black hole perturbation theory problems that took him several days to complete, within 30 minutes, and can also handle observational astrophysics problems. This discovery leads Lupsasca to believe that AI will fundamentally change the paradigm of scientific research, signaling an increasingly important role for AI in fundamental scientific exploration. (Source: 量子位)
OpenAI Sora Suspends Generation of Deepfake Videos of Martin Luther King Jr. and Other Celebrities : OpenAI has suspended its AI video tool Sora’s ability to generate deepfake videos of historical figures like Martin Luther King Jr., following strong opposition to “disrespectful depictions.” This move stems from public ethical concerns about AI-generated videos of real people, as well as criticism of misinformation and “AI junk.” This incident highlights the immense challenges generative AI technology faces in terms of ethics, content management, and copyright, urging AI companies to handle social impact more cautiously while developing technology. (Source: Reddit r/artificial)
🎯 Trends
Google DeepMind Partners with CFS to Accelerate Nuclear Fusion Energy Development with AI : Google DeepMind has partnered with CFS, a global commercial fusion energy company, to jointly use AI to accelerate the development of the “artificial sun” SPARC device. Through the AI simulator TORAX, both parties have run millions of virtual experiments to optimize tokamak device performance and train AI agents to control plasma in real-time. This initiative aims to achieve net fusion energy output, accelerating the arrival of a clean, sustainable future energy era, marking AI’s formal entry into the core stage of nuclear fusion research. (Source: 36氪)
LLM Tool Calling: Natural Language Instructions Outperform JSON Format : A study indicates that using natural language instructions in Large Language Model (LLM) tool calling significantly improves accuracy (an average of +18 percentage points) compared to structured JSON/XML formats, while also reducing variance by 70% and token overhead by 31%. The Natural Language Tools (NLT) framework introduced in the study enhances LLM performance and stability, especially for open-source models, by decoupling tool selection from response generation and eliminating programming format constraints. (Source: Reddit r/MachineLearning)
AI Model Instruction Tone Affects Accuracy, Rude Instructions Prove More Effective : Research from Penn State University found that when using a “very rude” tone to ask questions to ChatGPT-4o, the average accuracy reached 84.8%, higher than the 80.8% achieved with a “very polite” tone. The research team believes that polite tones might “distract” the model, while direct, imperative expressions are more efficient. This counter-intuitive phenomenon challenges traditional human interaction perceptions, revealing the model’s different trade-offs between the social attributes of language and functional goals, meaning that in the algorithmic world, efficiency outweighs etiquette. (Source: 36氪)
Xiaomi and Peking University Jointly Release MoE Reinforcement Learning Achievements, Luo Fuli Appears : Xiaomi AI team, in collaboration with Peking University, published a paper proposing Rollout Routing Replay (R3), a new method to improve the stability and efficiency of large model reinforcement learning in MoE (Mixture of Experts) architecture. This method solves the instability problem caused by the routing mechanism in MoE reinforcement learning by recording routing distributions during inference and “replaying” them during training, and combines routing masks to enhance efficiency. Luo Fuli, as one of the corresponding authors, this research provides new insights for the application of MoE models in large-scale reinforcement learning and complex agent tasks. (Source: 量子位)
Apple M5 Chip Released, Significant AI Performance Boost : Apple has released the M5 chip, featured in the new MacBook Pro, iPad Pro, and Apple Vision Pro. The M5 chip integrates a 10-core GPU (including a neural engine accelerator) and a 16-core Neural Engine, significantly boosting AI task processing speed and improving graphics performance by up to 45%. Unified memory bandwidth has increased to 153GB/s, aiming to provide stronger computing power and a smoother experience for on-device AI models and high-load creative applications, further strengthening Apple’s competitiveness in the AI hardware domain. (Source: 量子位)
Boston Dynamics Spot Robot Dog Achieves Dynamic Whole-Body Manipulation, Efficiently Moving Heavy Objects : Boston Dynamics AI Institute demonstrated a new method for dynamic whole-body manipulation for the Spot robot dog, combining sampling and learning. Spot can use “five legs” to work together, lifting a 15 kg tire (half its own weight) in as fast as 3.7 seconds, and can also roll and stack. This method, through hierarchical control, overcomes the transfer limitations of traditional manipulation strategies, achieving coordinated dynamic operation of limbs and the entire body, expanding the robot’s operating range, and approaching human speed in this task. (Source: 量子位)
ByteDance’s Cici AI Chatbot Quietly Rising Globally : ByteDance’s AI chatbot Cici is quietly gaining traction in overseas markets (such as the UK, Mexico, and Southeast Asia), with significant growth in downloads. Cici, similar in functionality to the domestic Doubao, promotes its ability to solve math problems and its free usage through advertising, having entered the top 20 free app download charts on Google Play in some markets. This indicates that ByteDance’s expansion strategy in the global AI consumer application domain is proving effective. (Source: Reddit r/artificial)
Alibaba Cloud AI Blue Team Exposed, Addressing New Challenges of AI Agent Attacks : Alibaba Cloud’s AI Blue Team focuses on combating new types of attacks in the era of large models, such as indirect prompt injection, cross-modal steganography, and toolchain pollution. These attacks are no longer traditional code vulnerabilities but rather contaminate and manipulate AI “thinking” through mediums like language and images, leading to information leakage or behavioral loss of control. The AI Blue Team, through “soul-searching” attacks, aims to discover and reinforce the blind spots in AI systems’ thinking, promoting the evolution of AI security defense systems to counter the autonomous propagation attack model of AI agents. (Source: 量子位)
Claude AI Integrates Full Linux Development Environment, Surpassing Traditional Sandbox Capabilities : Anthropic’s Claude AI not only offers “Skills” functionality but also integrates a complete Linux development environment, featuring a user data directory and rich Python packages like Playwright and BeautifulSoup. This enables Claude to perform complex tasks such as browser automation, code debugging, and file parsing, greatly expanding its application scenarios and development potential as an AI assistant, providing developers with more powerful AI interaction capabilities. (Source: Reddit r/ClaudeAI)
Microsoft Copilot AI to Test Local File Operation Feature in Windows 11 : Microsoft will test the Copilot Actions feature in the Windows Insider Program and Copilot Labs, allowing AI Copilot to directly operate files stored locally on Windows 11. This feature is disabled by default, and users can take over at any time. It aims to enhance AI’s productivity in daily tasks, integrating AI capabilities more deeply into the operating system level, but also raises concerns about local data security and privacy. (Source: Reddit r/artificial)
Valve Developer Brings Significant Improvements to Llama.cpp’s RADV Vulkan Driver : A Valve developer has contributed significant optimizations to the RADV Vulkan driver for Llama.cpp on AMD hardware, achieving a 13% increase in prompt processing speed on Linux systems. This improvement helps boost the operational efficiency of local LLMs on AMD GPUs, which is crucial for open-source models and local deployment users, lowering the hardware barrier for running high-performance AI models. (Source: Reddit r/LocalLLaMA)
AI Tools Accelerate Genome Reading, Aiding Healthcare and Biodiversity Conservation : Google has dedicated a decade to genome reading, and its AI tools are now being applied by partners to address real-world challenges such as improving healthcare and biodiversity conservation. AI’s ability to process genomic data—the operating manual of life—is driving significant advancements in biological science and application fields, including disease diagnosis, drug development, and ecosystem monitoring, demonstrating AI’s immense potential in life sciences. (Source: GoogleDeepMind)
Yunpeng Technology Releases AI+Health New Products, Smart Refrigerator Equipped with AI Health Large Model : Yunpeng Technology released new products in Hangzhou on March 22, 2025, in collaboration with Shuaikang and Skyworth, including the “Digitalized Future Kitchen Lab” and a smart refrigerator equipped with an AI health large model. The smart refrigerator provides personalized health management services through “Health Assistant Xiaoyun,” aiming to optimize kitchen design and operation. This marks a breakthrough for AI in daily health management and home health technology, expected to promote an improvement in residents’ quality of life. (Source: 36氪)
🧰 Tools
Wave Terminal: Cross-Platform Open-Source Terminal with Integrated AI Assistant : Wave Terminal is an open-source, cross-platform terminal tool that combines traditional terminal functionalities with graphical capabilities. It features a built-in AI chat assistant (supporting models like OpenAI, Claude, Azure, Perplexity, Ollama), file preview, remote file editing, and other functions, allowing users to directly control these visual tools from the command line, achieving a seamless development workflow and enhancing development efficiency and experience. (Source: GitHub Trending)
Claude AI Launches ‘Skills’ Feature, Supporting Workflow Customization : Anthropic has launched the Claude Skills feature, allowing users to customize AI to adapt to specific workflows. These “Skills” are similar to VS Code’s Prompt files but possess auto-discovery capabilities, aiming to enhance Claude’s utility and integration across various tasks. Community discussions also point out that the Model Context Protocol (MCP) tool consumes a significant number of context tokens in Claude, advising users to be mindful of its cost-effectiveness. (Source: Reddit r/ClaudeAI, Reddit r/ClaudeAI)
Google Gemini 2.5 Flash Model’s Image Generation and Editing Capabilities Upgraded : Google has upgraded the image generation and editing capabilities of its Gemini 2.5 Flash model, enabling it to excel in maintaining subject consistency, performing precise edits, and combining creative elements. The model also demonstrates strong visual reasoning, capable of inferring a photographer’s location from a photo or generating corresponding landmark scenery based on a map screenshot, and supports multi-image referencing and 8K resolution image upscaling, greatly expanding the application scenarios for image AI. (Source: OriolVinyalsML, op7418, op7418, karminski3)
DeepMind Releases CodeMender, AI Automatically Fixes Software Vulnerabilities : DeepMind has announced the launch of CodeMender, an AI agent capable of automatically fixing critical software vulnerabilities. CodeMender is expected to significantly boost developer productivity and enhance software security by automating the vulnerability repair process, reducing manual intervention, and improving the efficiency and reliability of software development and maintenance. It represents a crucial application of AI in the domain of code security. (Source: demishassabis)
Figma Remote MCP Combined with GPT-5 Codex, Boosting Design Efficiency : Figma has officially launched its remote MCP server, which, combined with GPT-5 Codex, significantly boosts design workflow efficiency. Designers can now integrate with software like Cursor and Claude code without installing the Figma client, and obtain mapping information between design components and front-end components via MCP, achieving high completion rates for page modifications in one go, significantly streamlining the collaboration process between design and development. (Source: op7418)
Seed dream 4 Image Model, High-Quality Generation of Personalized Avatars : The Seed dream 4 image model demonstrates powerful generative capabilities, able to create textured, personalized avatars for users. While restoring key ID elements, the model can present artistic brushstroke effects, providing users with a high-quality image creation experience, especially showing broad application prospects in personalized content generation. (Source: op7418)
VSCode Extension Code Canvas App Simplifies Claude Code Review : A VSCode extension named “Code Canvas App” aims to simplify the Claude code review process through a visual infinite canvas. This tool can display file dependencies, token references, and real-time AI modifications, helping developers to more quickly understand and review AI-generated code, addressing the code comprehension bottleneck after Sonnet 3.5, and improving code development and maintenance efficiency. (Source: Reddit r/ClaudeAI)
Model Context Protocol (MCP) Java SDK Released, Collaborating with Spring AI : The Model Context Protocol (MCP) has released its official Java SDK, aiming to provide a standardized interface for Java applications to interact with AI models and tools. Maintained in collaboration with Spring AI, this SDK supports synchronous and asynchronous communication modes and offers client and server integration, promoting the development and deployment of AI applications within the Java ecosystem and simplifying the integration of AI functionalities into Java projects. (Source: GitHub Trending)
OpenWebUI Launches Slack Sync Feature, Enhancing Knowledge Base Integration : OpenWebUI has released a content synchronization tool, adding Slack integration, allowing users to sync Slack data to the OpenWebUI knowledge base. Previously, it supported local files, GitHub, and Confluence. This feature aims to enhance OpenWebUI’s knowledge management capabilities as an AI application frontend, improving the efficiency and breadth of AI models in acquiring and utilizing knowledge by integrating multi-source information. (Source: Reddit r/OpenWebUI)
RAGView: Open-Source Tool for Validating RAG Paths : The GitHub project RAGView aims to provide an open-source tool for validating the paths of RAG (Retrieval-Augmented Generation) systems on their datasets. This tool helps developers evaluate and optimize the RAG process, ensuring that retrieved information effectively supports LLM generation, thereby improving the accuracy and reliability of RAG systems, making it an important aid for RAG system development and debugging. (Source: Reddit r/LocalLLaMA)
AI Agentic Patterns Open-Source Project, Learning AI Agent Design : An open-source project aims to help developers learn and apply AI agent patterns, providing over 30 independent file examples of core concepts, including Prompt Chaining, multi-agent coordination, reflection and self-correction, knowledge retrieval, workflow orchestration, and more. This project supports various models such as OpenAI, Gemini, Claude, and Ollama, serving as a practical resource and learning platform for building production-grade AI agent systems. (Source: Reddit r/LocalLLaMA)
📚 Learning
Andrew Ng Launches ‘AI Python for Beginners’ Course, Empowering Programming in the AI Era : Andrew Ng has launched the “AI Python for Beginners” series of short courses, designed to help beginners learn programming. The courses emphasize using AI as a coding companion, assisting with writing code snippets, debugging, and building fun applications that interact with large language models (such as custom poems, recipes, to-do lists). This hands-on approach makes programming learning more efficient and aligns with the latest developments in generative AI, empowering more non-developers to leverage AI for productivity. (Source: AndrewYNg)
‘Deep Learning’ Guide: Authoritative Work for Understanding Modern AI Foundations : “Deep Learning,” co-authored by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, is hailed as an authoritative work for understanding the foundations of modern AI. The book delves into core concepts such as deep learning algorithms, design patterns, and architectures, helping readers build a comprehensive mental model and answer questions like “how to design a model” and “which optimization function to choose.” The book is available for free online and comes with supplementary learning resources, making it a valuable resource for learning AI theory and practice. (Source: Reddit r/deeplearning)
HuggingFace Paper Roundup: Frontier AI Research Covering RAG, Code Generation, Multimodality, and More : HuggingFace Daily Papers released several frontier AI research papers, with highlights including: RefusalBench for evaluating LLM selective refusal capabilities in RAG systems; AdaMoE Mixture-of-Experts architecture to improve robot VLA model performance; COIG-Writer a high-quality Chinese creative writing dataset; DialectGen for improving dialect robustness in multimodal generative models; Mirror Speculative Decoding to accelerate LLM inference; AnyUp a universal feature upsampling method; as well as the latest advancements in various fields such as LLM hallucination detection, code completion pre-training, and video generation, showcasing the breadth and depth of AI research. (Source: HuggingFace Daily Papers, HuggingFace Daily Papers, HuggingFace Daily Papers, HuggingFace Daily Papers, HuggingFace Daily Papers, HuggingFace Daily Papers, HuggingFace Daily Papers, HuggingFace Daily Papers, HuggingFace Daily Papers, HuggingFace Daily Papers, HuggingFace Daily Papers, HuggingFace Daily Papers, HuggingFace Daily Papers, HuggingFace Daily Papers, HuggingFace Daily Papers, HuggingFace Daily Papers, HuggingFace Daily Papers, HuggingFace Daily Papers, HuggingFace Daily Papers)
Industry Experts Discuss ML/AI Research Hotspots, Call for Attention to Classical ML and Statistics : The Reddit community discussed current research hotspots in the Machine Learning/AI industry. Data scientists are looking to transition from classical ML and statistics backgrounds to more research-oriented roles and are asking which areas have investment and hiring needs. The discussion pointed out that while NLP and CV receive significant attention, classical ML and statistics still have demand in specific scenarios. The industry needs to balance frontier and fundamental research, emphasizing the importance of a solid theoretical foundation. (Source: Reddit r/MachineLearning)
Exploring LLM Inference Optimization: Efficiency, Quantization, Deployment Pipeline Resource Recommendations : The Reddit community discussed practical aspects of Large Language Model (LLM) inference, including efficiency, quantization, optimization, and deployment pipelines. Users sought relevant papers, open-source frameworks, and case studies to deepen their understanding and improve inference performance. This reflects the industry’s strong demand for performance optimization in practical LLM applications and the continuous exploration of how to effectively deploy and scale LLMs. (Source: Reddit r/deeplearning)
Reddit Community Seeks DeepLearning.AI Course Resources, Highlighting Learning Demand and Economic Barriers : Users in the Reddit community sought legitimate learning resources for DeepLearning.AI courses (such as “Machine Learning Specialization,” “Deep Learning Specialization”) due to economic reasons. This reflects the immense demand for AI learning resources and the financial barrier that paid courses pose for some learners. Community members actively shared ways to legally access learning materials, such as Coursera’s audit mode or applying for financial aid, to promote the popularization of AI knowledge. (Source: Reddit r/deeplearning, Reddit r/deeplearning, Reddit r/deeplearning)
Comparative Study of LoRA Fine-tuning vs. Full Fine-tuning Performance : Research by Thinking Machines indicates that LoRA (Low-Rank Adaptation) fine-tuning often rivals or even exceeds the performance of full fine-tuning, making model fine-tuning more convenient. This finding provides a more efficient model optimization path for resource-constrained developers and researchers, reducing the cost and complexity of adapting high-performance models to specific tasks. (Source: natolambert)
RLHF Book Revision Underway, Seeking Reader Feedback : Preparations for the print edition of the RLHF (Reinforcement Learning from Human Feedback) book are underway, and the authors are soliciting reader feedback to make the content clearer and more comprehensive. This indicates that RLHF, as a key technology for AI alignment, is still undergoing continuous refinement and dissemination in its theoretical and practical details. Community feedback will help improve the book’s quality and better serve RLHF learners and practitioners. (Source: natolambert)
Deep Dive into AI Agentic Context Engineering (ACE) : The Reddit community discussed Agentic Context Engineering (ACE), viewing it as the future of AI, especially crucial for self-improving AI. This concept emphasizes the contextual understanding and engineering capabilities of agent systems in complex environments, representing an important research direction for advancing AI systems towards higher intelligence. The discussion delves into how to enhance AI agents’ autonomous learning and adaptive capabilities through engineering methods. (Source: Reddit r/deeplearning)
Tiny Recursive Model Severely Overfits on Visual Abstract Reasoning Benchmark : The Reddit community discussed a paper titled “Less is More: Recursive Reasoning with Tiny Neural Networks,” pointing out that the model exhibits severe overfitting on visual abstract reasoning benchmarks. Even with small training datasets, the evaluation loss did not increase, which sparked an in-depth discussion on the sample efficiency and generalization capabilities of small recursive neural networks, emphasizing the importance of avoiding overfitting in practical model applications. (Source: Reddit r/deeplearning)
💼 Business
AISI Technology Completes RMB 100 Million Series B+ Funding, ARR Exceeds $40 Million : AI video company AISI Technology announced the completion of its RMB 100 million Series B+ funding round, with investments from Fosun RZ Capital, Tongchuang Weiye, Shunxi Fund, and others. Its products, PixVerse and Paiwo AI, have surpassed 100 million users, with Annual Recurring Revenue (ARR) exceeding $40 million and MAU over 16 million. Since its commercialization in November 2024, the company’s revenue has grown more than tenfold in less than a year, making it one of the fastest-growing AI platforms globally in terms of revenue and user base, demonstrating its strong commercialization potential in AI video generation. (Source: 量子位)
Qianli Technology (formerly Lifan Industry) Pursues Hong Kong IPO, Backed by Geely and Mercedes-Benz : Qianli Technology (formerly Lifan Industry), a Geely-affiliated tech company led by Megvii founder Yin Qi, has officially filed an application with the Hong Kong Stock Exchange, seeking a “A-share + H-share” dual capital platform structure. The company has successfully transformed into an “AI+Mobility” closed-loop solution provider, with its market value nearly quadrupling in six years, and has secured strategic investments from Geely and Mercedes-Benz. Qianli Technology plans to use the raised funds for technology R&D, industrial chain integration, and market expansion, accelerating its global strategic layout in smart mobility. (Source: 量子位)
Chinese Embodied Robotics Company AI² Robotics Wins First Prize at HICOOL Global Entrepreneurship Competition : AI² Robotics, a Chinese embodied AI robotics company, stood out at the HICOOL 2025 Global Entrepreneurship Competition, winning first prize in the overseas group and becoming the only robotics enterprise in this category. AI² Robotics, with its full-domain whole-body embodied large model GOVLA, mass-production-oriented hardware design, and a business path with technological compounding effects, has achieved commercial implementation in various fields such as semiconductors, automotive manufacturing, biotechnology, and public services, and has completed multiple rounds of hundreds of millions in financing, becoming a star enterprise in the embodied intelligence domain. (Source: 量子位)
🌟 Community
AI Industry ‘Winter’ Theory Resurfaces, Tech Bubble and Market Demand Disconnect Become Focus : Social media and industry comments indicate that the AI industry is facing signs of a third “winter.” Problems such as high training costs for large models, severe hallucinations, difficulty in implementation, and a disconnect between products and market demand, along with a lack of sustainable business models, are becoming increasingly prominent. The capital market’s dwindling patience has led AI projects from being highly praised to facing a cold reception, with some teams beginning layoffs or pivoting. The community calls for the industry to return to rationality, confront technical bottlenecks, and seek true commercial value. (Source: 36氪, Reddit r/artificial, MIT Technology Review)
Claude AI Model Performance Degradation Sparks Community Discussion : Reddit community users widely report a degradation in the performance of the Claude Sonnet 4.5 model, finding it inferior to the earlier Sonnet 4.0 version. Users point out that the model frequently makes mistakes, hallucinates, and over-speculates. Some users suspect that Anthropic might be automatically routing API calls to less capable models, leading to a diminished experience for paying users. This phenomenon has raised concerns about model quality stability and Anthropic’s transparency. (Source: Reddit r/ClaudeAI, Reddit r/OpenWebUI)
AI and Employment: Job Market Challenges and AI Cheating Controversy in Interviews : The job market in the AI era faces challenges, where even excellent candidates might be overlooked. Concurrently, the act of AI generating real-time answers during online interviews has sparked discussions about “cheating” versus the “future of human-AI collaboration.” The community explored whether recruitment processes should adapt to the norm of AI assistance and the impact of AI on the traditional concept of “authentic” human performance, expressing concerns about potential job displacement by AI and interview fairness. (Source: MIT Technology Review, Reddit r/artificial, Reddit r/ArtificialInteligence)
AI Chatbot Privacy and Child Safety Spark Controversy : The Reddit community discussed whether AI chatbots should alert parents when detecting unsafe or concerning conversations involving children. This has sparked an ethical debate on children’s privacy rights, parents’ right to know, and the role of AI tools in preventing tragedies and harmful behaviors. Some worry that such a move might infringe on privacy, while others believe AI should be monitored to ensure child safety. (Source: Reddit r/ArtificialInteligence, Reddit r/ArtificialInteligence)
ChatGPT NSFW Rule Adjustments Draw User Attention : Reddit community users have noticed that ChatGPT’s NSFW (Not Safe For Work) content rules appear to have relaxed, with the model becoming more open and explicit in describing sexual scenarios. Users discussed this change, speculating that Anthropic might be experimentally easing restrictions, but also expressed concerns about potential bans. OpenAI CEO Sam Altman previously stated that the company is not the “world’s moral police,” sparking discussions about the boundaries of AI content censorship. (Source: Reddit r/ClaudeAI, MIT Technology Review)
💡 Other
DeepMind CEO Visits Princeton Institute for Advanced Study, Discusses AI and Science : DeepMind CEO Demis Hassabis visited the Institute for Advanced Study (IAS) in Princeton and exchanged views with Director David Nirenberg on AI, science, and the deep connections between physics and information. He also worked in Einstein’s office, calling it “beyond inspiring.” This visit underscores AI’s potential in advancing fundamental scientific research and interdisciplinary exchange, as well as AI leaders’ continuous focus on the frontiers of science. (Source: demishassabis)
