'We May Have a Crisis on Our Hands': The Unregulated Rise of Emotionally Intelligent AI Pillay is an editorial fellow at TIME. Pillay is an editorial fellow at TIME. At least once a month, two-thirds of people who regularly use AI turn to their bots for advice on sensitive personal issues and emotional support. Many people now report trusting their chatbots more than their elected representatives, civil servants, faith leaders--and the companies building AI. That's according to data from 70 countries, gathered by the Collective Intelligence Project (CIP).

Bayesian brain theory suggests that the brain employs generative models to understand the external world.

Intrigued by the claims of emergent reasoning capabilities in LLMs trained on general web corpora, in this paper, we set out to investigate their planning capabilities.

Simultaneous behavioral and electrophysiological recordings call for new methods to reveal the interactions between neural activity and behavior. A milestone would be an interpretable model of the co-variability of spiking activity and behavior across trials. Here, we model a mouse cortical sensory-motor pathway in a tactile detection task reported by licking with a large recurrent spiking neural network (RSNN), fitted to the recordings via gradient-based optimization. We focus specifically on the difficulty to match the trial-to-trial variability in the data. Our solution relies on optimal transport to define a distance between the distributions of generated and recorded trials. The technique is applied to artificial data and neural recordings covering six cortical areas. We find that the resulting RSNN can generate realistic cortical activity and predict jaw movements across the main modes of trial-to-trial variability. Our analysis also identifies an unexpected mode of variability in the data corresponding to task-irrelevant movements of the mouse.