Tagged articles

self‑improving AI

7 articles · Page 1 of 1

Machine Learning Algorithms & Natural Language Processing

Jul 13, 2026 · Artificial Intelligence

Inside Tang Jie’s Two‑Year Push Toward ASI: The Bold AGI Roadmap

Founder Tang Jie’s internal letter reveals a two‑year, four‑engine plan to overcome memory, continual‑learning and self‑evaluation hurdles, accelerate AI‑self‑improvement, and push Zhipu AI toward artificial general intelligence and eventually artificial superintelligence, citing DeepMind’s compute‑growth analysis.

AGIAI roadmapAI safety

0 likes · 9 min read

Inside Tang Jie’s Two‑Year Push Toward ASI: The Bold AGI Roadmap

Machine Heart

Jul 9, 2026 · Artificial Intelligence

Can Transformers Lead to AGI? Sergey Brin Says Yes—But They're Already Evolving

In an unscripted DeepMind Build Day interview, Sergey Brin discusses the convergence of AI models across domains, the surprising transfer effects of training, the limits of superintelligence, evolving definitions of AGI, Gemini's strengths and weaknesses, and why Google feels it fell behind in code‑centric AI development.

AGIAI convergenceDeepMind

0 likes · 8 min read

Can Transformers Lead to AGI? Sergey Brin Says Yes—But They're Already Evolving

PaperAgent

Jul 8, 2026 · Artificial Intelligence

Why Agent Skills Need Self‑Evolution: A Survey of 19 Frameworks and 10 Benchmarks

This survey from Rutgers and UNC Charlotte systematically reviews 19 agent‑skill evolution methods and 10 evaluation benchmarks, revealing critical gaps such as the lack of longitudinal tracking, binary pass/fail metrics, and one‑time security checks, and highlighting how separating diagnosis from rewrite improves cross‑task performance.

AgentEvaluationReinforcement Learning

0 likes · 9 min read

Why Agent Skills Need Self‑Evolution: A Survey of 19 Frameworks and 10 Benchmarks

AI Engineering

Mar 27, 2026 · Artificial Intelligence

How Hyperagents Enable AI to Self‑Improve Its Own Improvement Process

Meta's Hyperagents paper introduces a self‑modifying AI architecture that lets task agents and meta‑agents coexist, allowing the system to not only solve tasks but also evolve the very mechanisms of its own improvement across multiple domains.

DGM-HHyperagentscross-domain transfer

0 likes · 8 min read

How Hyperagents Enable AI to Self‑Improve Its Own Improvement Process

AI Engineering

Mar 15, 2026 · Artificial Intelligence

Why Static Skills Fail and How Cognee Enables AI to Self‑Repair Its Prompts

The article explains silent drift in static AI skills, outlines Cognee’s five‑step loop—Skill Ingestion, Observe, Inspect, Amend, and Evaluate—to let agents automatically detect, analyze, and fix degrading prompts, and discusses community reactions and related self‑help projects.

Agent SkillsKnowledge Graphcognee

0 likes · 6 min read

Why Static Skills Fail and How Cognee Enables AI to Self‑Repair Its Prompts

Machine Learning Algorithms & Natural Language Processing

Mar 5, 2026 · Artificial Intelligence

Can AI Self‑Improve? Inside a Stanford PhD Defense on Continually Self‑Improving AI

Zitong Yang’s Stanford PhD defense introduced “continually self‑improving AI,” a system that autonomously refines its own parameters, generates synthetic training data, and even designs its own learning algorithms, with experiments on synthetic continual training, synthetic‑bootstrap pre‑training, and AI‑design‑AI demonstrating measurable gains over static baselines.

AI researchPretrainingcontinual learning

0 likes · 35 min read

Can AI Self‑Improve? Inside a Stanford PhD Defense on Continually Self‑Improving AI

21CTO

Feb 27, 2023 · Artificial Intelligence

What’s Next for Large Language Models? Emerging Trends Shaping AI

The article explores three emerging directions for next‑generation large language models—self‑generated training data, built‑in verification with external retrieval, and massive sparse‑expert architectures—highlighting recent research, practical challenges, and their potential to reshape AI development.

AI researchGenerative AIlarge language models

0 likes · 17 min read

What’s Next for Large Language Models? Emerging Trends Shaping AI