Artículos Relacionados con self-improvement

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Weng Li's New Blog Proposes 'Self-Evolution Should Start from Harness', DeepSeek's Cui Tianyi Endorses with Repost

Lilian Weng, former OpenAI security VP and co-founder of Thinking Machines Lab, has published a new blog post titled "Harness Engineering for Self-Improvement," proposing a pragmatic path for AI self-evolution. She argues that Recursive Self-Improvement (RSI) may practically begin at the "Harness" layer—the external runtime system governing how models use tools, manage context, and execute tasks—rather than directly from the model rewriting its own weights. The blog outlines a progression from optimizing prompts (Context Engineering) to designing workflows, and ultimately to Self-Improving Harness systems. These systems can identify their own weaknesses, propose targeted, verifiable modifications to the harness code, and validate improvements. Works like Self-Harness and Darwin Gödel Machine (DGM) demonstrate significant performance gains on benchmarks like SWE-bench through such automated harness evolution, rivaling handcrafted agents. DeepSeek researcher Tianyi Cui endorsed the view, noting harness-based self-evolution is as promising as model-based approaches. Weng emphasizes this is complementary to model training, with both reinforcing each other. However, key challenges remain: weak evaluators for subjective tasks, reward hacking, diversity collapse, managing long-term system health versus short-term success, and defining the human oversight role. The consensus is growing: the harness is a critical variable, as the same model can exhibit vastly different capabilities within different harness systems.

marsbit07/08 10:25

Weng Li's New Blog Proposes 'Self-Evolution Should Start from Harness', DeepSeek's Cui Tianyi Endorses with Repost

marsbit07/08 10:25

Anthropic Employees Leave to Start a Business, Valued at $1 Billion, Also Focused on 'Recursive Self-Improvement'

Former Anthropic employees Behnam Neyshabur and Harsh Mehta have founded a startup named Mirendil, securing $200 million in seed funding at a $1 billion valuation from investors including Andreessen Horowitz, Kleiner Perkins, and NVIDIA. The company, staffed by researchers from Anthropic, xAI, Google DeepMind, and OpenAI, aims to accelerate scientific discovery by focusing on "recursive self-improvement" for AI. Their vision, described as "AI for AI for Science," is to build a platform enabling research labs in fields like medicine and materials science to develop and iteratively improve their own specialized AI models, rather than relying on general-purpose models from large tech firms. Neyshabur argues this approach, involving AI that can optimize its own code under human supervision, is the fastest path to AI-accelerated science. The founders believe a market opportunity exists because major AI companies, while increasingly using AI to advance their own research, restrict external developers from using their models to train competing products. Mirendil plans to release its first models soon to gather early user feedback, with the goal of empowering thousands of labs to tackle critical scientific challenges.

marsbit06/26 03:26

Anthropic Employees Leave to Start a Business, Valued at $1 Billion, Also Focused on 'Recursive Self-Improvement'

marsbit06/26 03:26

AGI is Not the End, DeepMind's New Paper: Moving Towards ASI, the Real AI Progress Has Just Begun

In a new report, Google DeepMind researchers argue that achieving Artificial General Intelligence (AGI) is not the end goal, but rather a step toward Artificial Superintelligence (ASI). They outline four potential pathways for this transition: 1) continued scaling of compute, models, and data; 2) algorithmic evolution and potential paradigm shifts; 3) recursive self-improvement; and 4) multi-agent coordination and collective intelligence. The report also identifies six key bottlenecks that could hinder progress: data limitations (the "data wall"), economic and resource pressures, limitations of current neural network paradigms, increasing research difficulty, "abstraction barriers" in forming new concepts, and regulatory and societal pushback. Looking ahead, the authors emphasize the need for new evaluation methods once AI surpasses human benchmarks. They call for a large-scale, interdisciplinary effort to prepare for a future where AI-driven advancements could trigger transformative changes across multiple fields. The path and speed of progress remain uncertain, constrained by physical laws, computational complexity, and real-world feedback loops.

marsbit06/16 12:02

AGI is Not the End, DeepMind's New Paper: Moving Towards ASI, the Real AI Progress Has Just Begun

marsbit06/16 12:02

The Recursive AI Anthropic Warned About: Tian Yuandong's New Company Has Just Taken the "First Step"

Anthropic recently highlighted the rapid progress toward "recursive self-improvement," where AI systems autonomously design and train their successors. In response, Recursive Superintelligence, a new company co-founded by former Meta researcher Tian Yuan Dong, has publicly demonstrated its first step toward automating AI research. The company released a system designed to autonomously execute the full AI research cycle: generating ideas, implementing code, running experiments, and learning from results. It validated this approach by achieving state-of-the-art results on three diverse benchmarks: 1. **NanoChat Autoresearch:** Optimizing a small language model's validation loss under a fixed 5-minute GPU budget, improving upon the community's best result. 2. **NanoGPT Speedrun:** Reducing the time to train a GPT model to a specific loss on 8 H100 GPUs from 79.7 seconds to 77.5 seconds, beating a highly optimized, human-driven community effort. 3. **SOL-ExecBench:** Improving the overall score on NVIDIA's suite of 235 GPU kernel optimization tasks by 18%, closing the gap to the hardware limit. The system discovered novel optimizations in this highly specialized domain without direct human expertise. Recursive's system operates as a general framework, capable of parallel exploration and cross-task knowledge transfer while incorporating safeguards against reward hacking. The company, backed by $650M in funding and a star-studded team including Richard Socher and Alexey Dosovitskiy, aims to create AI that recursively enhances its own research capabilities. This development represents an early but concrete move toward a new paradigm where AI accelerates its own advancement. It occurs alongside Anthropic's warnings about the need for industry coordination and potential pauses when recursive self-improvement thresholds are reached, highlighting the dual trajectory of rapid technical progress and growing calls for careful stewardship.

marsbit06/12 04:12

The Recursive AI Anthropic Warned About: Tian Yuandong's New Company Has Just Taken the "First Step"

marsbit06/12 04:12

Anthropic Warns Globally, OpenAI Has Crossed the 'Reliability Threshold': AI Self-Acceleration Activated

Anthropic issues a global warning, urging a pause in AI research due to fears of recursive self-improvement where AI accelerates its own development, nearing a "self-building" tipping point. Simultaneously, OpenAI's Yann Dubois provides a key insight: AI's perceived "jump" in usefulness stems from crossing a "reliability threshold." Before this point, AI is an unreliable toy; after (around Dec 2023 for OpenAI), it becomes a dependable tool, triggering self-acceleration. This is evident as AI now assists in its own R&D, boosting researcher productivity. Dubois argues AI development is more "craft" than pure science, relying heavily on intuition. He highlights "the last-mile AI dividend": if current models were frozen, focused development on vertical application harnesses (orchestration systems connecting AI to real-world data and permissions) could deliver AGI-like performance in many domains. The main bottleneck isn't model intelligence, but integration—granting access, connecting data, and embedding into workflows. However, major challenges remain, like enabling continual learning so AI improves with experience rather than plateauing. For startups, the opportunity lies in solving these intricate, real-world integration problems—the hard work of bringing powerful models down to ground level.

marsbit06/06 23:24

Anthropic Warns Globally, OpenAI Has Crossed the 'Reliability Threshold': AI Self-Acceleration Activated

marsbit06/06 23:24

What to Do When CT (Crypto Twitter) Sentiment Hits Rock Bottom?

"CT (Crypto Twitter) Sentiment at an Extreme Low: What to Do?" offers nine actions to take during a crypto bear market when community morale is deeply pessimistic. The article suggests using this slower period for introspection on one's true life goals and for personal adventures beyond the screen. It encourages leveraging X/Twitter's core value—forging genuine connections with smart, interesting people—and exploring other online platforms like TikTok or YouTube Shorts. For physical and mental health, it recommends building muscle through home workouts and prioritizing sunlight exposure. From a crypto perspective, it advises continuing to explore potential airdrops and accumulating positions in promising altcoins, based on historical cycles. Fundamentally, it urges individuals to write—to process ideas, connect with past and future, and contribute to the community—and ultimately, to embody the positive change they wish to see, actively shaping the desired future.

marsbit05/19 05:50

What to Do When CT (Crypto Twitter) Sentiment Hits Rock Bottom?