Federal funding for cognitive research unexpectedly spurred significant advancements in artificial intelligence, laying the groundwork for the systems that power today's AI technology.

It’s widely believed AI’s inner workings are largely inscrutable. A former OpenAI researcher now says this narrative could be wrong. The idea that the neural networks underpinning modern AI are...

Source: arXiv quant-ph

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We show how to compute the gradient of a neural network error function.

An explosive new essay argues that AI models aren't indecipherable black boxes, and that we can precisely map how they "think."

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The Gap Between What Neural Networks Claim and What They Actually Learn

MITが開発したニューラルネットワークモデル「IRIS」により、細胞間コミュニケーションのシグナリング履歴を高精度に再構築が可能に。

Computer science researchers recently demonstrated a novel technique to conceal dormant functions within artificial neural networks. Even auditors with unrestricted access to model weights cannot trigger the hidden functionality through standard testing. Specifically, engineers store a secondary neural network inside the host model in an encrypted format. Consequently, the covert sub-network activates only when supplied with a cryptographic key known exclusively to the creator.