Dileep George @dileeplearning

Paradigms of Intelligence Team Advance our understanding of how intelligence evolves to develop new technologies for the benefit of humanity and other sentient life - Paradigms of Intelligence Team

1/4) I’m excited to announce that I have joined the Paradigms of Intelligence team at Google (github.com/paradigms-of...)! Our team, led by @blaiseaguera.bsky.social, is bringing forward the next stage of AI by pushing on some of the assumptions that underpin current ML.

#MLSky #AI #neuroscience

Jesus Christ.

Yes, it counts.

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🚨 New preprint! 🚨

Excited and proud (& a little nervous 😅) to share our latest work on the importance of #theta-timescale spiking during #locomotion in #learning. If you care about how organisms learn, buckle up. 🧵👇

📄 www.biorxiv.org/content/10.1...
💻 code + data 🔗 below 🤩

#neuroskyence

Thank you! Looks like I was able to fix it by adding one more rule to the domain forwarding setup at squarespace.

Hmmm….i need to vibe fix this….tomorrow!

AGI Comics — #23: Artificial General Productivity A comic series.

#AGIComics now has a website! And it is 100% vibe coded!

Check out agicomics.net

Illustration of the brain in blue and yellow

12 leading neuroscientists tackle a big question: Will we ever understand the brain?

Their reflections span philosophy, complexity, and the limits of scientific explanation.

www.sainsburywellcome.org/web/blog/wil...

Illustration by @gilcosta.bsky.social & @joanagcc.bsky.social

🎯

Ohh ok I realize that @tyrellturing.bsky.social mentioned evolution. Fine then. But then which neuroscientist believes this?

Hmm…I don’t think it’s impossible.

Evolution could create structures in the brain that are in correspondence with structure in the world.

Clone-structured graph representations enable flexible learning and vicarious evaluation of cognitive maps - Nature Communications Higher-order sequence learning using a structured graph representation - clone-structured cognitive graphs (CSCG) – can explain how the hippocampus learns cognitive maps. CSCG provides novel explanati...

Here's the CSCG paper: www.nature.com/articles/s41...

And here' the CML paper:
www.nature.com/articles/s41...

Good conclusion :-).

somehow chatGPT understand my opinion about successor representations? 4/

I didn't mention partial observability specifically, so it is impressive that this was picked up. Looks like we did something right in our CSCG paper in making this explicit? 3/

It is quite impressive that chatGPT picked up these nuances, picks up a relevant quote from the paper and even emphasizes portions of the response. 2/

This paper turned up on a feed, I was intrigued by it and started reading...

..but then I was quite baffled because our CSCG work seem to have tackled many of these problems in a more general setting and it's not even mentioned!

So I asked ChatGPT... ...I'm impressed by the answer1. 1/🧵

Graph schemas as abstractions for transfer learning, inference, and planning Transferring latent structure from one environment or problem to another is a mechanism by which humans and animals generalize with very little data. Inspired by cognitive and neurobiological insights...

Some of our work could explain this kind of latent graph learning and schema-like abstraction. 2/
arxiv.org/abs/2302.07350

Wow, very cool to see this work from Alla Karpova's lab. She had shown me the results when I visited @hhmijanelia.bsky.social and I was blown away.

www.biorxiv.org/content/10.1...

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it takes a bit of getting immersed in the field to know this :-)

This comic would be an inside joke. Many neuroscience papers that study a brain region postulate that the current does a simple transformation of the input to make it easy for brain regions that are 'downstream' to solve the real problem effectively deferring the real problem. ...

𝗛𝗼𝘄 𝘀𝗵𝗼𝘂𝗹𝗱 𝘄𝗲 𝗱𝗲𝗳𝗶𝗻𝗲 𝗮𝗻𝗱 𝗱𝗲𝘁𝗲𝗿𝗺𝗶𝗻𝗲 𝗮 𝗯𝗿𝗮𝗶𝗻 𝗿𝗲𝗴𝗶𝗼𝗻'𝘀 𝗶𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝗰𝗲?
We introduce the idea of "importance" in terms of the extent to which a region's signals steer/contribute to brain dynamics as a function of brain state.
Work by @codejoydo.bsky.social
elifesciences.org/reviewed-pre...

It's kinda obvious. #AGIComics has already figured out which brain region is the most important. 😇

And whether top-down't influence is multiplicative or not is very context-dependent. (this is also what is seen in neurobiology). www.science.org/doi/10.1126/...

www.science.org/doi/10.1126/...

Explaining away is a concrete role for feedback computations, and here is one example showing its effect. ....there are many more examples in the paper.
www.science.org/cms/10.1126/...

A detailed theory of thalamic and cortical microcircuits for predictive visual inference A generative vision model offers a detailed theory for cortical columns, thalamus, and computations in cortical circuits.

just want to re-point to this work and remind you that we have proposed and built models that incorporate topdown feedback, and have a theory about it.
www.science.org/doi/10.1126/...

ohh...yes...this is exactly what I think after reading some of the "deep research" reports. ....written by a committee

jumping on the Gemini 2.5 bandwagon... it's an incredible model. really feels like an(other) inflection point. talking to Claude 3.7 feels like talking to a competent colleague who knows about everything, but makes mistakes. Gemini 2.5 feels like talking to a world-class expert with A+ intuitions

already being scaled up...