The transformer architecture extended its reach to a variety of new domains.What happened: Originally developed for natural language processing, transformers are becoming the Swiss Army Knife of deep learning.

In 2021, transformers were harnessed to discover drugs, recognize speech, and paint pictures — and much more.

While models like GPT-3 and EfficientNet, which work on text and images respectively, are responsible for some of deep learning’s highest-profile successes, approaches that find relationships between text and images made impressive

The trend toward ever-larger models crossed the threshold from immense to ginormous. Google kicked off 2021 with Switch Transformer, the first published work to exceed a trillion parameters, weighing in at 1.6 trillion.

DeepMind released three papers that push the boundaries — and examine the issues — of large language models.

Facebook released a chatbot that summarizes dialog on the fly and uses the summary to generate further repartee.

The real-estate website Zillow bought and sold homes based on prices estimated by an algorithm — until Covid-19 confounded the model’s predictive power. Zillow, whose core business is providing real-estate information for prospective buyers, shut down its house-flipping division after...

The cost of training top-performing machine learning models has grown beyond the reach of smaller companies.

Chatbots often respond to human input with incorrect or nonsensical answers. Why not enable them to search for helpful information?

Microsoft is making GPT-3 available to selected customers through its Azure cloud service.

To understand a movie scene, viewers often must remember or infer previous events and extrapolate potential consequences. New work improved a model’s ability to do the same.

Google designed its own AI chip for its new smartphone — a snub to Qualcomm, the dominant chip vendor in Android phones. What’s new: Google debuted the Tensor chip last week

Is human creativity being replaced by the synthetic equivalent? The fear: AI is cranking out increasingly sophisticated visual, musical, and literary works. AI-generated media will flood the market, squeezing out human artists and depriving the world of their creativity.

Is AI becoming inbred? The fear: The best models increasingly are fine-tuned versions of a small number of so-called foundation models that were pretrained on immense quantities of data scraped from the web.

Tech companies generally try to be (or to appear to be) socially responsible. Would some rather let AI’s negative impacts slide?