DeepMind Blog

IBM, Facebook, Nuance, and most importantly: Google Google Google. "Google Search Will Be Your Next Brain"[1] is a good start, plus it's follow-up[2]. Some lay-of-the-land, least-to-most cool IMO:

* Facebook's interest is in AI for social purposes (face recognition, NLP), rather than AGI (reason DeepMind turned them down). But they're still solidly in the field / a worthy target.

* IBM has been interested in AGI for a very long time, hence Watson. NLP, neural nets, machine learning, etc. Dig into Watson, lots of fun here.

* Nuance. Smaller, Dragon Speak. NLP & Markov Models primarily, but their creator Ray Kurzweil (now at Google) is the biggest AGI champion you'll see. He started this Singularity religion based on what AGI will bring to bear, see "The Singularity is Near" and "How to Create a Mind". Super fun stuff; inspiring follow, if not exaggerated. Both Nuance, and a ~similar company Wolfram Alpha (which Siri uses) would be really cool targets.

* Google. G's always been interested in AI for NLP for translating web queries & mobile speech queries; speech synthesis for play-back; computer vision, robotics, & machine-learning (neural nets) for self-driving cars; you name it. If it's AI, G's on it. Importantly, one Geoff Hinton recently tweaked an AI algorithm which unblocked a clog in the field, and now shit is _throwing down_, catalyzing Hinton & Jeff Dean's G-internal "Google Brain" project. Shortly after, G acquired DeepMind[3], and _this_ my friend... this is the…

Do not nerd snipe me!

There's tons of juice left on the CPU side. Again I recommend Metamath Zero (which is somewhat of an opposite direction as HoTT), as it's able to check the entire Metamath proof database in a few hundred milliseconds. This is orders of magnitude faster than most other proof systems.

If you did want to manipulate trees on the GPU, then my stack monoid work[1] might be a good basis for it.

Of course, a whole nother direction is to use AI to develop proofs, which is a fascinating and growing area (see Holophrasm[2] and DeepMind work[3]). That of course runs on the GPU.

[1]: https://arxiv.org/abs/2205.11659

[2]: https://arxiv.org/abs/1608.02644

[3]: https://www.deepmind.com/publications/proving-theorems-using-incremental-learning-and-hindsight-experience-replay

In academy for example, including the publicly funded institute mentioned in the article (European Molecular Biology Laboratory’s European Bioinformatics Institute). Also, there is a blog page on the deep mind website where they cite "millions of researcher" around the world using alphafold [1]. Some of those institutions might be happy to develop the new alphafold.

On the other hand, even in academy, scientific articles are not always the best examples of open and reproducible research [2,3]. And in computer science, it is common for articles to be more focused on algorithms than on implementations.

[1]: https://deepmind.google/impact/meet-the-scientists-using-alphafold/

[2]: https://en.wikipedia.org/wiki/Reproducibility

[3]: https://en.wikipedia.org/wiki/Replication_crisis

no matter your luck with hardware or your sysadmin skills, doing local inference for just yourself and/or to emulate typical usage (e.g. your coding workflow and deep research, etc.) is just very inefficient in current model architecture.

to me, this is a "truck" approach to city driving as a single person who does not do furniture hauling every weekend. the sense of privacy and freedom is nice but online inference is more "economical" as multi-user load is more effectively served than going solo.

maybe new architectures would make it effective to do text inference locally [1], till then great on you if you can spend car money on your setup. hope it is a great learning experience as well.

[1] https://deepmind.google/models/gemma/diffusiongemma/

There is a huge overhang. Same techniques and sometimes even the same models can drive a lot of robotics coordination and decision making.

Here is probably one of the more clear examples. A model trained on video and also robotic simulation/recording (probably ensembled with control systems/mobility models) will likely be at the core of how robots make decisions and plan.

https://deepmind.google/blog/gemini-robotics-2-brings-whole-body-intelligence-to-robots/

This is way outside my area of expertise though. I've only dabbled in more classic robotics and control systems, but these multi-modal sequence to sequence models are highly adaptive and can effectively transfer learning across very different domains.

Literally the only thing people cared about was Audio Overviews. NotebookLM had launched months earlier without that feature and was immediately ignored. The reason people liked audio overviews was because the voice model was amazing. This guy and the labs team had nothing to do with that. It was developed by a research team at Google Research/GDM who got maybe 1% of the credit: https://deepmind.google/discover/blog/pushing-the-frontiers-... It’s amazing how these people still insist on denying the massive contribution the audio model made, which is apparent by the lack of credits or acknowledgements to the team anywhere public despite these self aggrandizing blogs and postcast appearances.