From folding proteins to growing clouds
Not quite 18 months into his job as Mr. AI for Amazon Web Services, Matt Wood is convinced the fledgling business will someday be bigger than the $20 billion/year AWS itself. At a corporate event in San Francisco, Wood talked with EE Times about the status and outlook of deep learning, what Amazon wants in semiconductors for it and his not-so-strange career path from genomics to cloud computing.
After earning a PhD in bioinformatics in 2004, Wood went to work for a U.K. institute that handled a third of the initial work decoding the human genome.
“It was just a sample to get a blueprint. We did 40 other species including zebra fish and the duck-billed platypus — an odd creature with 10 sex chromosomes,” Wood quipped.
Technology caught up with what was a billion-dollar effort that took a decade. A nearby U.K. startup developed a $100,000 system that could sequence a genome in a week.
“They were just around the corner, so they sent their first instrument over in the back of a taxi. Within six months we had 200 more, working on thousands of genomes and cell lines,” he recalled.
The advance was opening the door to leaps such as personalized treatments for cancer. There was just one problem.
“It was data-intensive — we generated several hundred terabytes a week. We had a data center, but we couldn’t get any more power on the site without spending tens of millions,” he said.
“With no more storage on the premises, I called a friend who had just joined Amazon. It was around the time AWS was just getting started. They gave me a $300 credit in return for writing a white paper on how to start a cloud-based genomics platform — I still haven’t finished the white paper,” he quipped.
What he did get was “religion around cloud computing” and a phone call from AWS offering him a job.
He helped set technical strategy for the team, and since 2008 has had a hand in launching a laundry list of AWS products including Lambda, which AWS pitches as the future of software development. He was also present at the birth of Alexa, Amazon’s virtual voice assistant, embedded in its Echo smart speaker.
“Echo came from a brainstorm about what things we could build if we had infinite compute. The original idea was like the Star Trek computer you talk to--that was the seed for what become Echo,” he said.
Wood was tapped to head AWS’s AI efforts in part because of his genomics background.
“Today’s machine learning uses the same foundational concepts we were using for folding proteins. The big change was in deep convolutions in the networks to build a hierarchical view for interpreting data such as images. Adding deep layers lets you fine tune a model to select images of cats from dogs, for example,” he said.
Next page: Price/performance is the metric for chips
Page 1 / 3 Next >