Universality Hypothesis

Creator

Creator

Seonglae Cho

Created

Created

2024 Apr 6 13:13

Editor

Editor

Seonglae Cho

Edited

Edited

2024 Nov 21 21:28

Refs

Refs

Natural Abstraction Hypothesis

Different models learn similar features and circuits when trained on similar tasks.

Even the discovery of similar circuits in humans and AI supports this claim

Neuromorphic computing

Neuromorphic computing is an approach to computing that is inspired by the structure and function of the human brain.[1][2] A neuromorphic computer/chip is any device that uses physical artificial neurons to do computations.[3][4] In recent times, the term neuromorphic has been used to describe analog, digital, mixed-mode analog/digital VLSI, and software systems that implement models of neural systems (for perception, motor control, or multisensory integration). Recent advances have even discovered ways to mimic the human nervous system through liquid solutions of chemical systems.[5]

Neuromorphic computing

https://en.wikipedia.org/wiki/Neuromorphic_computing

Convergent learning (2016)

https://arxiv.org/pdf/1511.07543.pdf

A Toy Model of Universality (2023)

https://arxiv.org/pdf/2302.03025.pdf

evidence

https://arxiv.org/pdf/2212.04938

(PDF) Brains and algorithms partially converge in natural language processing

PDF | Deep learning algorithms trained to predict masked words from large amount of text have recently been shown to generate activations similar to... | Find, read and cite all the research you need on ResearchGate

(PDF) Brains and algorithms partially converge in natural language processing

https://www.researchgate.net/publication/358653162_Brains_and_algorithms_partially_converge_in_natural_language_processing

(PDF) Brains and algorithms partially converge in natural language processing

Computational Neuroscience

https://arxiv.org/pdf/2211.12935

https://arxiv.org/pdf/2210.06756

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