Machine learning is making wind power more predictable. Engie SA, a multinational energy utility based in France, is the first customer for an AI-powered tool from Google that predicts the energy output of wind farms.

A chatbot persuaded at least one person that it has feelings. A senior engineer at Google announced his belief that the company’s latest conversational language model is sentient.

The future of Big AI may lie with product-development teams. Meta reorganized its AI division. Henceforth, AI teams will report to departments that develop key products.

AI startups are helping writers tailor articles that appear near the top of Google’s search results. At least 14 companies sell access to software that uses GPT-3, the language model from OpenAI, to generate headlines, product descriptions, blog posts, and video scripts.

If you want to both synthesize data and find the probability of any given example — say, generate images of manufacturing defects to train a defect detector and identify the highest-probability defects — you may use the architecture known as a normalizing flow.

The emerging generation of trillion-parameter language models take significant computation to train. Activating only a portion of the network at a time can cut the requirement dramatically and still achieve exceptional results.

The next killer AI application may be developed by someone who has never heard of gradient descent. A rising generation of software development platforms serves users who aren’t familiar with AI — and even programming.

The higher the accuracy of a pretrained model, the better its performance after fine-tuning, right? Not necessarily. Researchers conducted a meta-analysis of image-recognition experiments and performed some of their own.

Machines are doing light janitorial work in the uncontrolled environment of Google’s offices. Everyday Robots, a new spin-out from Google’s experimental X Development division, unleashed 100 robots to perform an array of cleanup tasks.

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.

Transformers have matched or exceeded earlier architectures in language modeling and image classification. New work shows they can achieve state-of-the-art results in some reinforcement learning tasks as well.