Dec 23, 2020

Every year for the past decade, I flew to Singapore or Hong Kong to celebrate my mother’s birthday with her on December 22. This year, for the first time, we did it via Zoom.

Every year for the past decade, I flew to Singapore or Hong Kong to celebrate my mother’s birthday with her on December 22. This year, for the first time, we did it via Zoom.

Neural networks for natural language processing got bigger, more prolific, and more fun to play with. Language models, which already had grown to gargantuan size, continued to swell, yielding chatbots that mimic AI luminaries and have very strange ideas about horses.

Institutional hurdles to AI for medicine began to fall, setting the stage for widespread clinical use of deep learning in medical devices and treatments. DeepMind’s AlphaFold model determined the three-dimensional shape of a protein in just hours.

The worldwide pandemic and a contentious U.S. election whipped up a storm of automated disinformation, and some big AI companies reaped the whirlwind.

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.

While generative adversarial networks were infiltrating cultural, social, and scientific spheres, they quietly transformed the web into a bottomless well of synthetic images of . . . well, you name it.

AI accelerated the search for a coronavirus vaccine, detected Covid-19 cases, and otherwise softened the pandemic’s blow. Machine learning researchers worldwide scrambled to harness the technology against the coronavirus.