Core Insights - The AI industry is transitioning from a "Scaling Era" back to a "Research Era," emphasizing fundamental innovation over mere model size expansion [4][7][40]. - Current AI models exhibit high performance in evaluations but lack true generalization capabilities, akin to students who excel in tests without deep understanding [10][25]. - SSI's strategy focuses on developing safe superintelligence without commercial pressures, aiming for a more profound understanding of AI's alignment with human values [15][16]. Group 1: Transition from Scaling to Research - The period from 2012 to 2020 was characterized as a "Research Era," while 2020 to 2025 is seen as a "Scaling Era," with a return to research now that computational power has significantly increased [4][7][40]. - Ilya Sutskever argues that simply scaling models will not yield further breakthroughs, as the data and resources are finite, necessitating new learning paradigms [7][39]. Group 2: Limitations of Current Models - Current models are compared to students who have practiced extensively but lack the intuitive understanding of true experts, leading to poor performance in novel situations [10][25]. - The reliance on pre-training and reinforcement learning has resulted in models that excel in benchmarks but struggle with real-world complexities, often introducing new errors while attempting to fix existing ones [20][21]. Group 3: Pursuit of Superintelligence - SSI aims to avoid the "rat race" of commercial competition, focusing instead on building a safe superintelligence that can care for sentient life [15][16]. - Ilya emphasizes the importance of a value function in AI, akin to human emotions, which guides decision-making and learning efficiency [32][35]. Group 4: Future Directions and Economic Impact - The future of AI is predicted to be marked by explosive economic growth once continuous learning challenges are overcome, leading to a diverse ecosystem of specialized AI companies [16][18]. - Ilya suggests that human roles may evolve to integrate with AI, maintaining balance in a world dominated by superintelligent systems [16][18].

He was a rockstar MIT student, dazzling the world with his groundbreaking research. Now everyone is wondering if he just made it all up. https://t.co/HkWSIwGWCF ...

He was a rockstar MIT student, dazzling the world with his groundbreaking research. Now everyone is wondering if he just made it all up. https://t.co/hEXk4hCJDD ...

RT BSCN (@BSCNews)📣 LATEST ANALYSIS: $ICP - Get the latest research into @dfinity's native token... Read now https://t.co/sde22pdJDL ...

Actually if you want to know what the real ‘secret’ is 😀 it’s world-class research AND world-class engineering AND world-class infra all working closely together with relentless focus and intensity…Oriol Vinyals (@OriolVinyalsML):The secret behind Gemini 3?Simple: Improving pre-training & post-training 🤯Pre-training: Contra the popular belief that scaling is over—which we discussed in our NeurIPS '25 talk with @ilyasut and @quocleix—the team delivered a drastic jump. The delta between 2.5 and 3.0 is https:/ ...

Aidan Toner-Rodgers shot to academic fame in a field hungry for new insights and revelatory research. But a computer scientist thought something seemed off. https://t.co/9RBCcHlWRI ...

Model Performance - Gemini 3 demonstrates extremely strong performance across various benchmarks, achieving state-of-the-art results in multiple areas [1] - Gemini 3 excels in tasks relevant to building agents, particularly in long horizon planning (Vending bench 2), terminal-based coding (Terminal Bench 2), and real-world contextual tasks like customer support (Sierra Tow Squared Bench) [2][3] Deep Agent Harness & Tool Utilization - The Deep Agent harness, an open-source tool, is used to test Gemini 3's agent-building capabilities, featuring built-in tools for planning, sub-agent delegation, and file system manipulation [3][4] - Gemini 3 effectively utilizes native tools within the Deep Agent harness, including file manipulation, planning, and sub-agent delegation, for tasks like research [18] - The agent successfully plans tasks, writes files, initiates sub-agents, analyzes results, updates to-dos, and generates final reports with citations [7][8] Research Task & Workflow - A research task is implemented using Gemini 3 within the Deep Agent harness, demonstrating the model's ability to perform complex tasks [5] - The research agent workflow involves creating to-dos, writing the research request to a file, initiating a sub-agent for research, analyzing results, and writing a final report [6][7] - The agent effectively uses a custom research sub-agent to isolate context, conduct in-depth research, and return results to the parent agent [15] Implementation & Customization - Gemini 3 can be easily integrated into existing workflows using Langchain and the Deep Agent harness [12][13] - The Deep Agent harness allows for customization through custom tools, instructions, and sub-agents, enabling users to tailor the agent to specific use cases [4][11] - The provided quick start repository offers instructions and code for running Gemini 3 with the Deep Agent harness, facilitating experimentation and customization [9][10]

Britain has a strong research base in the field. Can it turn this into a lasting advantage? https://t.co/1P5vtehylS ...

These are rare books, so a lot of them are pretty obscure. >> If Atwood can see around corners, it's because her visions have historical precedent. They come rooted in actual events.At the Thomas Fischer Rear Book Library in Toronto at what is archived stacks of her research, that is the hundreds of news clippings that substantiate her plots. So, this is folder upon folder of your research >> for Handmaid's Tail. >> Oh, yeah.Lots of it. She writes by a strict rule. If it didn't happen somewhere at some time ...

Research Importance - Research makes life better massively [1] - The United States does research really well [1] University & Research - Universities research makes life better [1] - Attack on universities is a tragic blunder [1]