eLife

Human EEG and artificial neural networks reveal disentangled representations and processing timelines of object real-world size and depth in natural images

Neural and computational evidence reveals that real-world size is a temporally late, semantically grounded, and hierarchically stable dimension of object representation in both human brains and ...

Interpretable neural networks help reveal the nature of dark matter

A research team from the Xinjiang Astronomical Observatory (XAO) of the Chinese Academy of Sciences has developed an ...
eLife

High-Fidelity Neural Speech Reconstruction through an Efficient Acoustic-Linguistic Dual-Pathway Framework

This study presents a valuable advance in reconstructing naturalistic speech from intracranial ECoG data using a dual-pathway model. The evidence supporting the claims of the authors is solid, ...
Wired

AI Is a Black Box. Anthropic Figured Out a Way to Look Inside

For the past decade, AI researcher Chris Olah has been obsessed with artificial neural networks. One question in particular engaged him, and has been the center of his work, first at Google Brain, ...
Frontiers

High-Dimensional Representations in Brain and Machine: Vector Symbolic Architectures for Neural Computation

Recent advances in neuroscience, cognitive science, and artificial intelligence are converging on the need for representations that are at once distributed, ...
The New York Times

Nobel Physics Prize Awarded for Pioneering A.I. Research by 2 Scientists

With work on machine learning that uses artificial neural networks, John J. Hopfield and Geoffrey E. Hinton “showed a completely new way for us to use computers,” the committee said. By Derrick Bryson ...