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#computationalneuroscience

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I just discovered the ARC-AGI initiative and the associated test to estimate how close "AI" models are from #AGI

arcprize.org/arc-agi

While I found the initiative interesting, I'm not sure I understand what in this test really guarantees that the model is capable of some form of generalization and problem-solving.
Wouldn't it be possible for specialized pattern-matching/discovering algorithms to solve such problems?
I imagine some computer scientists, mathematicians or computational neuroscientists have already had a look at this, so would anyone knows of some articles/blogs on the topic?

Maybe @wim_v12e? Is this something you already looked at?

ARC PrizeARC Prize - What is ARC-AGI?Learn more about the only AI benchmark that measures AGI progress.

Deep adaptive learning predicts and diagnoses CSVD-related cognitive decline using radiomics from T2-FLAIR: a multi-centre study

Patient enrolment and baseline characteristics A total of 783 su…
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newsbeep.com/us/12439/

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A few weeks ago, I shared a differential equations tutorial for beginners, written from the perspective of a neuroscientist who's had to grapple with the computational part. Following up on that, I've now tackled the first real beast encountered by most computational neuroscience students: the Hodgkin-Huxley model.

While remaining incredibly elegant to this day, this model is also a mathematically dense system of equations that can overwhelm and discourage beginners, especially those with non-mathematical backgrounds. Similar to the first tutorial, I've tried to build intuition step-by-step, starting with a simple RC circuit, layering in Na⁺ and K⁺ channels, and ending with the full spike-generation story.

Feedback is welcome, especially from fellow non-math converts.
neurofrontiers.blog/building-a

#ComputationalNeuroscience #Python #hodgkinHuxleyModel #math #biophysics

From: @neurofrontiers
neuromatch.social/@neurofronti

Neurofrontiers · Building a virtual neuron - part 2 - Neurofrontiers
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