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To navigate this new era of scientific discovery, policymakers and funders must focus on four urgent priorities: 1️⃣ Ensure widespread access to agents 2️⃣ Make national data assets agent-ready 3️⃣ Tackle the validation bottleneck 4️⃣ Empower peer reviewers with agents Read the full article here: https://deepmind.google/public-policy/conjecture-machines-ai-agents-and-the-new-validation-bottleneck-in-science/
Excited to share my very first article on AI policy & scientific discovery! 🧬🔬 At @GoogleDeepMind, we are building powerful "conjecture machines"—agentic AI that generates ideas at scale. But as AI accelerates, we face a critical new challenge: the Validation Bottleneck. 🧵👇 Ideas are becoming abundant, but testing them in the physical world remains slow and costly. For policymakers, funders, and researchers to truly unlock superhuman scientific discovery, we must understand and address this growing gap between generation and verification. These thoughts build upon our works in AI for Math over the past years—from neuro-symbolic systems such as AlphaGeometry and AlphaProof, to thinking models such as Gemini Deep Think, and most recently Aletheia. My belief is advancements in AI technology for addressing the verification problems in maths will give great insights for other fields! 📐💡🔍
From proposing hypotheses to designing experiments, AI agents are starting to reshape scientific discovery. But the hardest part is testing these ideas in the real world. Our essay explores the growing validation bottleneck and outlines four priorities for policymakers and funders. → https://goo.gle/4poACUT
Excited to share my very first article on AI policy & scientific discovery! 🧬🔬 At @GoogleDeepMind, we are building powerful "conjecture machines"—agentic AI that generates ideas at scale. But as AI accelerates, we face a critical new challenge: the Validation Bottleneck. 🧵👇 Ideas are becoming abundant, but testing them in the physical world remains slow and costly. For policymakers, funders, and researchers to truly unlock superhuman scientific discovery, we must understand and address this growing gap between generation and verification. These thoughts build upon our works in AI for Math over the past years—from neuro-symbolic systems such as AlphaGeometry and AlphaProof, to thinking models such as Gemini Deep Think, and most recently Aletheia. My belief is advancements in AI technology for addressing the verification problems in maths will give great insights for other fields! 📐💡🔍
From proposing hypotheses to designing experiments, AI agents are starting to reshape scientific discovery. But the hardest part is testing these ideas in the real world. Our essay explores the growing validation bottleneck and outlines four priorities for policymakers and funders. → https://goo.gle/4poACUT