MIT researchers have developed a new chip designed to implement neural networks. It is 10 times as efficient as a mobile GPU, so it could enable mobile devices to run powerful artificial-intelligence algorithms locally, rather than uploading data to the Internet for processing.
In recent years, some of the most exciting advances in artificial intelligence have come courtesy of convolutional neural networks, large virtual networks of simple information-processing units, which are loosely modeled on the anatomy of the human brain. Neural networks are typically implemented using graphics processing units (GPUs), special-purpose graphics chips found in all computing devices with screens. A mobile GPU, of the type found in a cell phone, might have almost 200 cores, or processing units, making it well suited to simulating a network of distributed processors.
Deep learning has already had a huge impact on computer vision and speech recognition, and it’s making inroads in areas as computer-unfriendly as cooking. Now a new startup led by University of Toronto professor Brendan Frey wants to cause similar reverberations in genomic medicine.
Deep Genomics plans to identify gene variants and mutations never before observed or studied and find how these link to various diseases. And through this work the company believes it can help usher in a new era of personalized medicine.
Genomic research is hard. Scientists still know relatively little about our genes and how they interrelate. But Frey and others in the field now know enough that they can equip machines to do the heavy lifting. And there’s an awful lot of this heavy lifting to do. “Genomics is no longer about small datasets,” Frey tells Gizmag. “It’s now about very, very large datasets.”
For context, the first effort to sequence a full human genome took 13 years – running from 1990 to 2003. There are now many companies working to sequence many genomes at a time. The largest of these is called Illumina. “Illumina,” Frey says, “expects to sequence one million genomes in the next year. Each genome contains three billion letters. That’s a lot of data.”