Google has begun building a larger newresearch center that will employ hundreds of people, the latest sign that the competition to turn these radical new machines into practical tools is growing more intense. It’s in Santa Barbara, California, where Google’s first quantum computing lab already employs dozens of researchers and engineers.
Operations at the new Google Quantum AI campus, which have already begun with a few initial researchers, will give Google a greater role in manufacturing its own machines, Google said at itson Tuesday. In-house manufacturing, combined with an increase in the number of quantum computers, should accelerate progress.
One top job at Google’s new quantum computing center is making the fundamental data processing elements, called qubits, more reliable, said Jeff Dean, senior vice president of Google Research and Health, who helped build some of Google’s most important technologies like search, advertising and AI. Qubits are easily perturbed by outside forces that derail calculations, but error correction technology will let quantum computers work longer so they become more useful.
“We are hoping the timeline will be that in the next year or two we’ll be able to have a demonstration of an error-correcting qubit,” Dean told CNET in a briefing before the conference.
that can bring great power to bear on complex problems, like developing new drugs or materials, that bog down classical machines. The quantum machines, however, rely on the weird physical laws that govern ultrasmall particles. Several tech giants and startups are pursuing quantum computer development, and their efforts for now remain expensive research projects that haven’t proven their potential.
“We hope to one day create an error-corrected quantum computer,” said Sundar Pichai, chief executive of Google parent company Alphabet, said during the Google I/O keynote speech.
Google is spotlighting its quantum computing work at Google I/O, a conference geared chiefly for programmers who need to work with the search giant’s Android phone software, Chrome web browser and other projects. The conference provides Google a chance to show off globe-scale infrastructure, burnish its reputation for innovation and generally geek out. Google is also using the show to tout new AI technology that brings computers a bit closer to human intelligence and to provide details of its custom hardware for accelerating AI.
As one of Google’s top engineers, Dean is a major force in the computing industry, a rare example of a programmer to be profiled in The New Yorker magazine. He’s been instrumental in building key technologies like MapReduce, which helped propel Google to the top of the search engine business, and TensorFlow, which powers its extensive use of artificial intelligence technology. He’s now facing cultural and political challenges, too, most notably the very public departure of AI researcher Timnit Gebru.
Google’s TPU AI accelerators
At I/O, Dean also revealed new details of Google’s AI acceleration hardware, custom processors it calls tensor processing units. Dean described how the company hooks 4,096 of its fourth-generation TPUs into a single pod that’s 10 more powerful than earlier pods with TPU v3 chips.
“A single pod is an incredibly large amount of computational power,” Dean said. “We have many of them deployed now in many different data centers, and by the end of the year we expect to have dozens of them deployed.” Google uses the TPU pods chiefly for training AI, the computationally intense process that generates the AI models that later show up in our phones, smart speakers and other devices.
Previous AI pod designs had a dedicated collection of TPUs, but with TPU v4, Google connects them with fast fiber-optic lines so different modules can be yoked together into a group. That means modules that are down for maintenance can easily be sidestepped, Dean said.
The approach has been profoundly important to Google’s success. While some computer users focused on expensive, ultra-reliable computing equipment, Google has employed cheaper equipment since its earliest days. However, it designed its infrastructure so that it could continue working even when individual elements failed.
Google is also trying to improve its AI software with a technique called multiple modalities. Today, separate AI systems are trained to recognize text, speech, photos and videos. Google wants a broader AI that spans all those inputs. Such a system would, for example, recognize a leopard regardless of whether it saw a photo or heard someone speak the word, Dean said.