Yann LeCun

Let’s get this out of the way: A brain is not a cluster of graphics processing units, and if it were, it would run software far more complex than the typical artificial neural network. Yet neural networks were inspired by the brain’s architecture.

Some of deep learning’s bedrock datasets came under scrutiny as researchers combed them for built-in biases. Researchers found that popular datasets impart biases against socially marginalized groups to trained models due to the ways the datasets were compiled, labeled, and used.

A new model designed to sharpen images tends to turn some dark faces white, igniting fresh furor over bias in machine learning. Photo Upsampling via Latent Space Exploration (Pulse) generates high-resolution versions of low-resolution images.

Geoffrey Hinton, Yoshua Bengio, and Yann LeCun presented their latest thinking about deep learning’s limitations and how to overcome them.

How is it that many people learn to drive a car fairly safely in 20 hours of practice, while current imitation learning algorithms take hundreds of thousands of hours, and reinforcement learning algorithms take millions of hours? Clearly we’re missing something big.

A year-long Twitter feud breathed fresh life into a decades-old argument over AI’s direction. Gary Marcus, a standard bearer of logic-based AI, waged a tireless Twitter campaign to knock deep learning off its pedestal and promote other AI approaches.