If artificial intelligence is rapidly eating software, then Google may have the biggest appetite around.

At the company’s annual developer conference today, CEO Sundar Pichai announced a new computer processor designed to perform the kind of machine learning that has taken the industry by storm in recent years (see “10 Breakthrough Technologies: Deep Learning”).

The announcement reflects how rapidly artificial intelligence is transforming Google itself, and it is the surest sign yet that the company plans to lead the development of every relevant aspect of software and hardware.

Perhaps most importantly, for those working in machine learning at least, the new processor not only executes at blistering speed, it can also be trained incredibly efficiently. Called the Cloud Tensor Processing Unit, the chip is named after Google’s open-source TensorFlow machine-learning framework.

Training is a fundamental part of machine learning. To create an algorithm capable of recognizing hot dogs in images, for example, you would feed in thousands of examples of hot-dog images—along with not-hot-dog examples—until it learns to recognize the difference. But the calculations required to train a large model are so vastly complex that training might take days or weeks.

Pichai also announced the creation of machine-learning supercomputers, or Cloud TPU pods, based on clusters of Cloud TPUs wired together with high-speed data connections. And he said Google was creating the TensorFlow Research Cloud, consisting of thousands of TPUs accessible over the Internet.

“We are building what we think of as AI-first data centers,” Pichai said during his presentation. “Cloud TPUs are optimized for both training and inference. This lays the foundation for significant progress [in AI].”

Google will make 1,000 Cloud TPU systems available to artificial intelligence researchers willing to openly share details of their work.

Pichai also announced a number of AI research initiatives during his speech. These include an effort to develop algorithms capable of learning how to do the time-consuming work involved with fine-tuning other machine-learning algorithms. And he said Google was developing AI tools for medical image analysis, genomic analysis, and molecule discovery.