The Race to Buy the Human Brains Behind Deep Learning Machines

Any aspiring science fiction writer looking for a good protagonist could do worse than ripping off the Wikipedia page for Demis Hassabis: He grew up in England as a chess prodigy and built absurdly sophisticated video games before getting a degree in computer science from Cambridge, started studying neuroscience and publishing respected papers on amnesia and other topics, and then proceeded to co-found one of the hottest artificial-intelligence startups. Now that his company, DeepMind, has been snapped up by Google for a reported $400 million to $500 million (depending on your tech blog of choice), exactly how this latest twist will change his story remains to be seen—but there’s a decent chance Hassabis will ultimately become commander of an army of humanoid Googlebots.

Google’s acquisition of Hassabis and the rest of the DeepMind team points to the surging interest in the field of deep learning, a funky part of computer science seen as key to building truly intelligent machines. It centers on having computers learn to do tasks and find patterns on their own. Google, for example, received attention a couple of years ago, when its network of self-learning computers were able to understand the concept of a cat and find cats in YouTube videos. (There’s obviously way more complexity to deep learning than cat videos, and you’re welcome to expand your horizons on the subject here.)

Exactly what DeepMind has been doing in the deep learning field is a bit of a mystery. The company’s website says: “We combine the best techniques from machine learning and systems neuroscience to build powerful general-purpose learning algorithms. … Our first commercial applications are in simulations, e-commerce and games.”

Some members of DeepMind have published papers over the past couple of years through NIPS, the Neural Information Processing Systems foundation, that provide some insights into their work. One describes a set of algorithms that can mine a social network for patterns at record speed. Another, titled “Playing Atari with Deep Reinforcement Learning” (PDF), describes the ability to train computers to outperform humans at playing such classic video games as Breakout, Enduro, and Pong.

Elon Musk invested in DeepMind, as did the Founders Fund and Jaan Tallinn, an early Kazaa and Skype employee with a penchant for backing artificial-intelligence projects. “DeepMind is bona fide in terms of its research capabilities and depth,” says Peter Lee, who heads Microsoft Research.

According to Lee, Microsoft, Facebook, and Google find themselves in a battle for deep learning talent. Microsoft has gone from four full-time deep learning experts to 70 in the past three years. “We would have more if the talent was there to be had,” he says. “Last year, the cost of a top, world-class deep learning expert was about the same as a top NFL quarterback prospect. The cost of that talent is pretty remarkable.”

    Before it's here, it's on the Bloomberg Terminal.