Dec 25, 2019

Earlier language models powered by Word2Vec and GloVe embeddings yielded confused chatbots, grammar tools with middle-school reading comprehension, and not-half-bad translations. The latest generation is so good, some people consider it dangerous.

An international wave of anti-surveillance sentiment pushed back against the proliferation of face recognition systems.

Makers of self-driving cars predicted a quick race to the finish line, but their vehicles are far from the homestretch. A few years ago, some car companies promised road-ready autonomous vehicles as early as 2017.

Society awakened to the delight, threat, and sheer weirdness of realistic images and other media dreamed up by computers.

The future of machine learning may depend less on amassing ground-truth data than simulating the environment in which a model will operate. Deep learning works like magic with enough high-quality data. When examples are scarce, though, researchers are using simulation to fill the gap.

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.