Who's Who of AI

Blake Richards

449 trust researcher @tyrellturing.bsky.social · 11,669 followers
AI research
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Researcher with public evidence across AI research.

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past 30d
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past 30d
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7d ago
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Researcher at Google and CIFAR Fellow, working on the intersection of machine learning and neuroscience in Montréal (academic affiliations: @mcgill.ca and @mila-quebec.bsky.social).
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Articles & links

14/15) Our paper builds on a lot of other great work on similarity-aware cooperation, universal AI and decision theories. Dive into the math and the full experiments in the paper! 👇 arxiv.org/abs/2608.03958

A game theory for foundation models shows new paths to rational cooperation through similarity inference arxiv.org
AI Weekly's analysis →
  • The paper reports that foundation model agents in stylized social dilemmas consistently converge to stable cooperation, contradicting classical predictions of mutual defection.
  • The authors introduce the 'embedded Bayesian agent,' which models an agent as part of the universe it inhabits rather than an independent decision-maker.
  • They propose 'embedded equilibrium' as a new solution concept replacing the Nash equilibrium for reasoning about modern AI agents.
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View on Bluesky · ♥ 7 ↻ 0 ↩ 1 · 3 from the directory shared this · 7d ago

New perspective piece with @mandanas.bsky.social: We argue, based on LLMs and old connectionist theories, that schemas shouldn't be viewed as distinct from semantic or episodic memories. They're just one end of a detailed-to-abstract memory spectrum: www.cell.com/neuron/fullt.…

cell.com
View on Bluesky · ♥ 66 ↻ 18 ↩ 1 · 2 from the directory shared this · 75d ago
↻ Blake Richards reposted
@neurovium.bsky.social

new paper, #NeuroAI 📣📜 Can measured cortical organization be used as an inductive bias for artificial recurrent neural networks? In this work, we ask whether cortical geometry, wiring, and function can push RNNs learn. Not as metaphor, but as measurable structure! 1/n🧵👇 arxiv.…

Harnessing cortical geometry, wiring, and function as inductive biases for recurrent neural networks arxiv.org
AI Weekly's analysis →
  • Researchers built RNNs from spatial and connectivity data covering nearly 12,000 neurons from mouse visual cortex.
  • Biologically constrained networks consistently outperformed baseline models across three cognitive decision-making tasks.
  • Functional weight initialization, not spatial structure alone, provided the largest single performance boost.
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↻ Blake Richards reposted
Scott McGrath @smcgrath.phd

An analysis of 15 million Gemini interactions shows only 3% of occupations use the model for most of their tasks. Rather than automating ourselves away, we're mostly just offloading low-expertise chores to keep the complex, non-routine cognitive work for ourselves.

Despite AI hype, Google's data shows workers aren't automating themselves away arstechnica.com
AI Weekly's analysis →
  • Google's first ATLAS report analyzed 15 million de-identified Gemini interactions across 150 countries, 140 languages, 800 occupations and 4,000 tasks.
  • AI is already used in 68% of occupations covering around 90% of US employment, but touches only about 21% of tasks within a typical job.
  • Fewer than 10% of workplace Gemini interactions fully automate a task, and only 3% of occupations show AI used for over 75% of their tasks.
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Their own posts

Recent commentary

1/15) What could drive AI agents to cooperate with each other, even if there is no chance for reciprocity or pay back? 🤔 🧵 Our team at Google, Paradigms of Intelligence, uncovered new paths to cooperation and a new game theory for foundation models 👇

View on Bluesky · ♥ 56 ↻ 13 ↩ 1 · 7d ago

Sanders' proposal for the government to take a 50% stake in AI companies is, IMO, a good idea. AI will be a critical piece of infrastructure, one which should be built with appropriate safe-guards and environmental planning. Trying to cancel AI is foolish - but leaving it to market forces is too.

View on Bluesky · ♥ 24 ↻ 4 ↩ 3 · 76d ago
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