RT Pushmeet Kohli (@pushmeet)Our AlphaProof paper is in this week’s issue of @Nature!In 2024, @GoogleDeepMind's proof agents AlphaProof & AlphaGeometry together made a substantial leap in AI by achieving the silver-medal standard in solving IMO problems.The Nature paper describes the technical innovations required—in particular, the RL loop bridging natural language & symbolic rigor—that made AlphaProof possible. ...

Core Insights - David Silver, a prominent researcher in reinforcement learning, has left DeepMind after 15 years to establish his own AI company, Ineffable Intelligence [1][5]. Company Formation - Ineffable Intelligence was quietly founded in November 2025, with Silver officially appointed as a director on January 16, 2026 [2]. - The company is headquartered in London and is actively recruiting AI research talent while seeking venture capital [3]. Contributions at DeepMind - Silver was a key figure in the development of DeepMind's "Alpha series," leading or significantly contributing to major projects such as AlphaGo, AlphaZero, MuZero, and AlphaStar [7][9]. - His work on AlphaGo, which defeated world champion Lee Sedol in 2016, marked a significant milestone in AI history [9]. - Silver has received multiple accolades, including the ACM Prize in Computing in 2019 and the Royal Academy of Engineering Silver Medal in 2017 [10]. Academic and Research Impact - Silver is one of the most published authors among DeepMind employees, with over 280,000 citations and an h-index of 104 according to Google Scholar [11]. - His research has focused on advancing AI capabilities beyond human knowledge, advocating for a new "Age of Experience" where AI learns from its own experiences [17][19]. Vision for AI - Silver aims to tackle the challenge of creating superintelligent AI that can learn independently from first principles, moving away from reliance on human knowledge [17][19].

Core Viewpoint - David Silver, a prominent AI researcher from Google DeepMind, has left the company to establish a new startup named Ineffable Intelligence, focusing on solving complex AI challenges and pursuing superintelligence [1][3][4]. Group 1: Company Formation and Background - Ineffable Intelligence is being founded in London, with active recruitment for AI researchers and seeking venture capital [3]. - Silver was a key figure at Google DeepMind, contributing to significant achievements such as AlphaGo, AlphaStar, and AlphaZero, which demonstrated the capabilities of AI in complex games [9][12][14]. - The company was officially registered in November 2025, with Silver appointed as a director in January 2026 [4]. Group 2: Silver's Contributions and Vision - Silver's work includes developing AI systems that surpassed human capabilities in games, showcasing the potential of AI to learn and adapt [12][14]. - He emphasizes the need for AI to explore and discover knowledge independently, moving beyond human limitations and biases [18][23]. - The vision for Ineffable Intelligence is to create a self-learning superintelligence that can autonomously uncover foundational knowledge [23]. Group 3: Industry Context and Trends - Silver's departure follows a trend where notable AI researchers are leaving established labs to pursue startups focused on superintelligence, with significant funding being raised in the sector [15]. - Other notable figures, such as Ilya Sutskever and Yann LeCun, are also venturing into similar domains, indicating a growing interest in the pursuit of advanced AI capabilities [15][16].

Core Viewpoint - David Silver, a prominent figure in reinforcement learning and a key researcher at DeepMind for 15 years, has left the company to establish his own AI startup, Ineffable Intelligence, aiming to tackle the challenges of achieving superintelligence in AI [2][21]. Group 1: Departure from DeepMind - David Silver has officially left DeepMind and has been appointed as the director of his new company, Ineffable Intelligence, which was quietly established in November 2025 [3][2]. - Silver had been on leave for several months prior to his departure from DeepMind [4]. - Google DeepMind confirmed Silver's departure and expressed gratitude for his contributions during his tenure [9]. Group 2: Achievements at DeepMind - Silver was instrumental in the development of several landmark AI projects at DeepMind, including AlphaGo, which defeated world champion Lee Sedol in 2016, marking a significant milestone in AI history [14]. - He also led the development of AlphaZero, which achieved superhuman performance in Go, chess, and shogi without relying on human game data [14]. - Silver contributed to the creation of MuZero, which learns to play games without being informed of the rules, and AlphaStar, which defeated top players in StarCraft II [15][16]. - He has received numerous accolades, including the ACM Prize in Computing in 2019 and the Royal Academy of Engineering Silver Medal in 2017 [18]. Group 3: Vision for the Future - Silver's motivation for founding Ineffable Intelligence is to return to the awe and wonder of solving the most challenging problems in AI, with a focus on creating a superintelligent AI that can learn endlessly [21]. - He advocates for a new "Age of Experience" in AI, where systems learn from experiences through reinforcement learning, moving beyond reliance on human knowledge [24]. - Silver believes that achieving true superintelligence requires AI to learn from first principles, independent of human intuition and knowledge [25].

Core Insights - Google DeepMind's AlphaFold has significantly impacted protein structure prediction, driving advancements in scientific research over the past five years [1][4] - AI is reshaping scientific research, particularly in life sciences and biomedicine, due to rich data availability and urgent societal needs [1][3] Group 1: AI in Scientific Research - AI models and tools have achieved breakthroughs in basic research, including protein structure prediction and the discovery of new biological pathways [1][3] - The paradigm of "foundation models + research agents + autonomous laboratories" is emerging in AI-driven scientific research [3][13] Group 2: Advancements in Biology - DeepMind's AlphaFold has solved the protein structure prediction problem, earning the 2024 Nobel Prize in Chemistry and establishing itself as a digital infrastructure for modern biology [4] - The C2S-Scale model, developed by Google and Yale University, has generated new hypotheses about cancer cell behavior, showcasing AI's potential in formulating original scientific hypotheses [8] Group 3: AI in Drug Development - AI-assisted pathology detection has expanded to new disease scenarios, with the DeepGEM model achieving a prediction accuracy of 78% to 99% for lung cancer gene mutations [10] - The AI-optimized drug MTS-004 has completed Phase III clinical trials, marking a significant milestone in AI-driven drug discovery [10] Group 4: AI in Other Scientific Fields - AI applications in materials science are gaining momentum, with startups like Periodic Labs and CuspAI focusing on discovering new materials [11] - DeepMind's WeatherNext 2 model has surpassed traditional physical models in accuracy and efficiency for weather predictions [5] Group 5: Future of AI in Science - The evolution of scientific intelligence technologies is expected to accelerate, with AI foundational models and robotics enhancing research efficiency [19] - The integration of AI into scientific discovery is anticipated to lead to significant breakthroughs, with predictions of achieving near-relativistic level discoveries by 2028 [19]

Core Insights - Google DeepMind has publicly released the complete technology and training methods behind its new model, AlphaProof, which is designed for mathematical proofs [2][4] - The model utilizes a 3 billion parameter encoder-decoder transformer architecture and incorporates a reinforcement learning environment based on the Lean theorem prover [8][7] Development Process - The AlphaProof team was relatively small, with around 10 core members, and was led by IMO gold medalist Miklós Horváth, who developed a method for creating various problem variants for training [4][5] - Over the course of a year, the team explored various research ideas, integrating successful approaches into the AlphaProof system [5] Training Methodology - AlphaProof transforms the mathematical proof process into a game-like environment, where each mathematical proposition serves as a new game level [7] - The model was pre-trained on approximately 300 billion tokens of code and mathematical text, followed by fine-tuning with around 300,000 manually crafted proofs from the Mathlib library [9][10] - A significant breakthrough was achieved through an automated formalization process that generated about 80 million formalized problems from 1 million natural language math questions [10] Performance at IMO 2024 - AlphaProof demonstrated impressive performance at the 2024 IMO, solving three problems, including the most difficult one, P6, which only 5 out of 609 participants solved completely [15][16] - The model utilized a testing-time reinforcement learning mechanism to generate around 400,000 related problem variants for particularly challenging questions [13][15] Future Directions - Following its success, DeepMind has opened access to AlphaProof for researchers, allowing them to explore its capabilities [19] - While AlphaProof excels in identifying counterexamples and formalizing statements, it faces challenges with custom definitions and relies heavily on the Lean theorem prover [20] - The model's dependency on Lean's evolving environment and the limited availability of unique mathematical problems present ongoing challenges for its broader applicability [20]

Core Insights - DeepMind's AlphaProof achieved a silver medal at the International Mathematical Olympiad (IMO), scoring 28 points, just one point shy of gold, marking a significant milestone in AI's mathematical problem-solving capabilities [3][4][20]. Group 1: AlphaProof's Performance - AlphaProof is the first AI system to earn a medal-level score in a prestigious competition like the IMO, demonstrating a leap in AI's ability to tackle complex mathematical challenges [4][20]. - In the 2024 IMO, AlphaProof solved 4 out of 6 problems, including the most difficult problem, showcasing its advanced problem-solving skills [18][20]. - The performance of AlphaProof is comparable to that of a highly trained international high school student, with only about 10% of human participants achieving gold status [18][20]. Group 2: Technical Mechanisms - AlphaProof combines large language models' intuitive reasoning with reinforcement learning, allowing it to learn from a vast dataset of nearly one million mathematical problems [8][10]. - The system utilizes the Lean formal language for mathematical proofs, ensuring that each step of reasoning is verifiable and free from errors typical of natural language models [6][7][10]. - AlphaProof employs a strategy similar to Monte Carlo tree search, breaking down complex problems into manageable sub-goals, enhancing its problem-solving efficiency [11][17]. Group 3: Limitations and Future Directions - Despite its achievements, AlphaProof's efficiency is limited, taking nearly three days to solve problems that human competitors complete in 4.5 hours, indicating room for improvement in speed and resource utilization [21]. - The AI struggles with certain types of problems, particularly those requiring innovative thinking, highlighting the need for enhanced adaptability and generalization capabilities [21][23]. - Future developments aim to enable AlphaProof to understand natural language problems directly, eliminating the need for manual translation into formal expressions [23][24].

Core Insights - Google DeepMind has publicly released the complete technology and training methods behind its IMO gold medal model, AlphaProof, continuing its tradition of transparency in AI research [1][22]. Group 1: Development and Team Structure - The AlphaProof team was relatively small, typically consisting of about 10 members, with additional personnel joining closer to the IMO competition [3]. - The core breakthrough was attributed to IMO gold medalist Miklós Horváth, who developed a method to create various problem variants for training the AI [3][5]. Group 2: Technical Architecture - AlphaProof employs a 3 billion parameter encoder-decoder transformer model as its "brain," designed to understand the current proof state and output strategies and step estimates for completing proofs [8][9]. - The system transforms the mathematical proof process into a game-like environment, utilizing a reinforcement learning framework based on the Lean theorem prover [6]. Group 3: Training Methodology - The training faced challenges in sourcing sufficient mathematical problems, initially pre-training the model on approximately 300 billion tokens of code and math text [11]. - A specialized translation system was developed to convert natural language math problems into a formal language understood by Lean, generating around 80 million formalized problems from 1 million natural language questions [11][14]. Group 4: Performance and Achievements - AlphaProof demonstrated impressive performance at the 2024 IMO, successfully solving three problems, including the most difficult one, with a training time of 2-3 days per problem [19][20]. - The system's ability to generate related problem variants during testing significantly enhanced its problem-solving capabilities [19][17]. Group 5: Future Directions and Limitations - Following its success, DeepMind has opened access to AlphaProof for researchers, who have reported its strengths in identifying counterexamples and proving complex statements [22][23]. - However, limitations were noted when dealing with custom definitions, indicating a dependency on existing concepts within the Mathlib library [24]. - The reliance on the Lean theorem prover presents challenges due to its evolving nature, which may affect AlphaProof's performance in advanced mathematical fields [24].

Core Insights - Google DeepMind has publicly released the complete technology and training methods behind its IMO gold medal model, AlphaProof, continuing its tradition of transparency in AI research [1][30] - The model utilizes a 3 billion parameter encoder-decoder transformer architecture, which allows it to understand and generate mathematical proofs effectively [12][21] Development Process - The AlphaProof team was relatively small, consisting of about 10 members for most of the development period, with additional members joining closer to the IMO competition [3] - A key breakthrough came from team member Miklós Horváth, who developed a method to create various problem variants for training the AI [4][5] - Over a year, the team explored various research ideas, integrating successful approaches into the AlphaProof system [7] Training Methodology - AlphaProof transforms the mathematical proof process into a game-like environment, where each mathematical proposition serves as a new game level [8] - The system employs a reinforcement learning environment based on the Lean theorem prover, allowing it to suggest strategies and estimate the steps needed to complete proofs [13][14] - The training faced challenges in sourcing sufficient mathematical problems, initially using 300 billion tokens of code and math text for pre-training, followed by fine-tuning with 300,000 manually crafted proofs [16][21] - A significant innovation was the automatic formalization process, which translated natural language math problems into a format understandable by Lean, generating around 80 million formalized problems from 1 million natural language questions [16][21] Performance at IMO - AlphaProof's performance at the 2024 IMO was remarkable, successfully solving three problems, including the most difficult one, despite requiring 2-3 days of computation for each problem [26][28] - The system's ability to generate related problem variants during the competition was crucial for its success [26][27] Future Directions - Following its success, DeepMind has opened AlphaProof's capabilities to the scientific community, allowing researchers to apply for access [30] - Researchers have noted AlphaProof's strength in identifying counterexamples and its limitations when faced with custom definitions in proofs [31][33] - The reliance on the Lean theorem prover presents challenges due to its evolving nature, which can affect AlphaProof's performance in more mature mathematical domains [35] - The limited availability of unique mathematical problems poses a challenge for the AI's generalization capabilities, highlighting the need for further development in generating its own training problems [36]

Core Insights - DeepMind, a subsidiary of Google, has developed an AI system named AlphaProof that can prove complex mathematical theories, enhancing the process of mathematical problem-solving [1][2] - AlphaProof demonstrated its capabilities by solving 4 out of 6 problems in the International Mathematical Olympiad, achieving a score equivalent to a silver medal [2] Group 1: AI System Development - The AI system, AlphaProof, is designed to generate verifiable proofs in a formal mathematical software environment, addressing challenges faced by traditional language models [1] - The system utilizes reinforcement learning to formalize and find proof methods for 80 million propositions, outperforming previous advanced AI systems in mathematical competitions [1] Group 2: Performance and Limitations - In the International Mathematical Olympiad, AlphaProof, in collaboration with another system called AlphaGeometry, successfully solved a significant portion of the competition's complex problems [2] - Despite its impressive performance, experts noted that AlphaProof has limitations in solving other forms of difficult problems, suggesting this as a future research direction [2]