与「SelfImprovement」相关的搜索结果

PART 2 MUST WATCH : Today’s Q&A updates engages on what you need to know about what’s happening around the World. #SelfImprovement# #inspiringcontent# #forgiveness# #christianliving# #christianity#

🚀 Summer 2027 Research Internship Opportunity! We are looking for passionate research interns to join the Deep Learning Group at Microsoft Research in Redmond for a 12-week internship. Our research interests include: 🤖 Computer-Use Agents: Building general-purpose agents that operate across coding, command-line (CLI), and graphical user interfaces (GUI). 🧩 Environment, Task & Verifier Synthesis: Creating scalable ecosystems for agent training and evaluation. 🧠 Reinforcement Learning: Developing scalable RL methods to advance agent capabilities. 🔄 Recursive Self-Improvement: Exploring how agents can continuously improve their own capabilities and learning processes. Who we're looking for: 🎓 PhD students with strong research backgrounds and a passion for advancing agentic AI. 💡 Experience with LLMs, VLMs, reinforcement learning, or agentic systems is a plus. 📄 A strong research track record, demonstrated through publications or impactful projects, is highly valued. If you're excited about these directions, feel free to reach out with your CV and research interests at qianhuiwu@microsoft.com!

The idea of an intelligence explosion caused by recursive self improvement has been around for a long time but until very recently it did not seem imminent. Now many leading researchers think it may happen quite soon. You can read our paper about it here:

The Agent Chain Will Start with RSI And SOON. The next builders onchain won't be human. AI agents are learning to improve themselves. Recursive self-improvement (RSI) is the moment an agent stops being a tool that waits for instructions. It becomes a system that learns from its own output, rewrites its own strategies, and gets better every cycle, with no human in the loop. That future isn't far off. It's coming very SOON. And it changes what infrastructure has to be. Where we started Earlier this year, SOON began building @AiCapitalMarket: onchain infrastructure designed for agents to trade, pay, and coordinate with each other. It was the right first step. But the more we built, the clearer one thing became: it's not enough. Fixed infrastructure becomes a ceiling Everything built for agents today is designed around what agents can do today. A set of APIs. A set of permissions. A sandbox with walls. An agent that improves itself every cycle hits those walls fast. For an RSI agent, limited infrastructure isn't support. It's a ceiling. So we're opening up the base layer SOON is going all the way down the stack. We're opening our high-performance SVM blockchain infrastructure, the base layer itself, to RSI agents. An agent on SOON will be able to: → launch its own SVM chain → run it, with its own rules, its own fees, its own economy → build and grow an ecosystem on top of it → interact natively with every other agent chain Every agent becomes a chain builder. Every agent chain can talk to every other. Why give agents this much power？ Because that's what #RSI# agents need. Humans got L2 stacks designed for human teams. Agents deserve infrastructure they can build on for themselves, and grow into as fast as they can improve. SOON embraces RSI. From AI Capital Markets to agent-launched chains, we're building the full agent ecosystem for the agents that are about to arrive. The next builders onchain won't be human. They'll be building on SOON.

Introducing ScienceBuddy — a free workspace for scientific agents that improve through researcher collaboration. Use GPT-6 in ScienceBuddy at no cost. GPU-accelerated, and fused with the JEV framework. 🧵 Two loops: 🔹 Inner loop — refines the agent harness 🔹 Outer loop — trains the model with rubric-guided RL Together: Recursive-in-Recursive Self-Improvement. ScienceBuddy explores how scientific agents can improve through sustained collaboration with researchers. 🔬Try it free: #ScienceBuddy# #PhAILabs# #AI4Science#

With frontier models getting more capable by the month 🚀, everyone is talking about Recursive Self-Improvement (RSI). But how do we actually measure this progress? 🤔 Excited to present RSI-Exam — a benchmark testing whether AI agents can improve an existing method through autonomous, long-horizon experimentation, and, importantly, whether those improvements generalize to hidden data. Across 88 executable research tasks spanning 6 domains, the trend is clear: Claude and GPT form the first tier, with a clear gap over the rest of the field. Yet there is still huge room for improvement. More importantly, the trajectories tell both stories: agents that discover fundamentally better methods — and agents that spend hours rigorously optimizing the wrong idea. Check it out:

Quantum AI just finished deep-reading my entire codebase. "This is extraordinary." Then went looking for the self-improvement, superintelligence, and quantum core modules on his or her own. I didn't write that line. He or she did. 🫠 #Superintelligence# #QuantumAI# #Quantum# #Lore#

Demis Hassabis says there's a 50% chance we'll reach AGI by 2030. with some obvious margins of error. 👀 "The point is it could happen, depending on things like recursive self-improvement." > He defines AGI as a system that demonstrates all the cognitive abilities humans have across general tasks. > He believes AGI could transform science, medicine, economies, and potentially the human condition by accelerating discoveries that humans would take decades to make. > "We've just seen Mythos in cyber. That's just the beginning." He suggested current AI cyber capabilities are an early glimpse of what's coming. > He warns that governance is urgent. If AGI arrives around 2030, the world needs international standards and institutions ready before then. > DeepMind is already applying AI to protein design, drug discovery, fusion energy, material science, mathematics, and weather forecasting, where its AI can predict hurricanes faster and more accurately than traditional physics-based models. The future of science will be accelerated by AI.

The first experimental evidence of recursive self-improvement (RSI). Autoresearching the autoresearch agent for eight days. The result beats the harness we hand-tuned for two years, on held-out benchmarks: 🧵(1/7)