or is the question: did AI depend on training on enormous amounts of human knowledge?
MACHINE LEARNING
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Study: Perfect AI-human value alignment mathematically impossible
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Perfect alignment between #AI and human values is mathematically impossible, study says
by PNAS Nexus @TechXplore_com Learn more: https://
bit.ly/4e7EiqJ #MachineLearning #ArtificialIntelligence #ML -
AI is transforming mathematics, says Nature article
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‘It is incredible’: How #AI is transforming mathematics
by @dcastelvecchi @Nature Learn more: https://
buff.ly/NDPsRy2 #LLM #ArtificialIntelligence #MachineLearning #DeepLearning -
When extra test-time compute helps model convergence
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The key distinction: More test-time compute is not automatically useful. It becomes useful when the model has learned an internal landscape where extra iterations move the latent state toward solution-aligned attractors rather than spurious ones. That is why the convergence
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New paper introduces Equilibrium Reasoners for latent AI reasoning
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The next clue in AI reasoning: answers may be attractors. A new paper from Benhao Huang, Zhengyang Geng, and Zico Kolter introduces Equilibrium Reasoners (EqR) — a sharp mechanistic view of test-time scaling in latent reasoning models. The core idea is simple, but deep:
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Test-Time Compute and Solution-Aligned Attractors
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The key distinction: More test-time compute is not automatically useful. It becomes useful when the model has learned an internal landscape where extra iterations move the latent state toward solution-aligned attractors rather than spurious ones. That is why the convergence
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OpenAI and Anthropic’s contrasting AI launches in 2026 cinema
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OpenAI: carefully rolls out GPT-5.5-Cyber through Trusted Access for verified defenders Anthropic: “Claude Mythos is too powerful for public release” Also Anthropic: accidentally shows Mythos in the UI and immediately runs out of capacity 2026 AI launches are absolut cinema.
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Instruct codex to maintain a scratch-log for tracking refactor decisions
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Tell codex to maintain a scratch-log while it works on bigger refactors with decisions it had to make, tradeoffs, review fixes, so later on you can read through which tradeoffs the agent made, what you forgot to specify etc.
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Open-source AI wins through community and knowledge sharing
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I said Buy a GPU because I wanted as many indie researchers & devs working on RTX 3090s for the community (e.g. @pupposandro
, @no_stp_on_snek
) I started writing greentext posts on LLMs because I wanted to get people curious How Opensource AI wins? Community & knowledge sharing -
Anticipation of GPT-5.6 release, hopes for better vibe
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Looks like GPT-5.6 release is very close. Really looking forward to it. 5.5 already is an insanely good model. Hope it gets a bit better vibe tho