Nando de Freitas

Enhorabuena 🇪🇸 El mejor equipo ganó la copa ⚽️ 👏👏👏👏

I know everyone knows this, but what a beautiful visualisation x.com/themathflow/st…

Great point 3 by @RichardSocher —likely many bullshit jobs en.wikipedia.org/wiki/Bullshit_… won’t be impacted by AI either 🤔 x.com/richardsocher/…

What a game 🇦🇷🇦🇷🇦🇷🇦🇷🇦🇷🇦🇷🇦🇷

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I loved the analysis in the CANDI (text diffusion) paper. Wonderful to see new works in this direction. However, beyond perplexity, we urgently need to start seeing benchmark results after post-training, and comparisons to autoregressive LLMs. We must measure the performance gap to know whether we’re making progress.

Any predictions for when open source GLM or DeepSeek will match Claude Fable 5? x.com/anthropicai/st…

A model trained for next-token prediction is forced to build compressed representations of latent structure in text. Ilya Sutskever correctly refers to this phenomenon as understanding. Here, a model trained for next-step sensor prediction, with a robot that has proprioception and touch sensors but no vision, is forced to build compressed representations of latent structure in the physical world. The robot becomes aware of the shape of external objects. That is, it understands the physical properties of the external world that enable it to make better next-step sensor predictions. This research was previously done by a diverse team of expert engineers at DeepMind over a month - including stars like @notmisha and @yuvaltassa. Remarkably, this reproduction with a completely different robot took only a few hours to implement using Codex. The automatic creation of physical environments by AI will likely lead to huge advances in areas of science and engineering that use physical simulators or twin models. The paper and notebook are available at ❤️∀ t.co/z2N29JF1FY

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