What will the U.S. do about Facebook and Google?
Some people would like to see the companies broken up. They propose surgical cuts: Facebook must relinquish WhatsApp and Instagram, while parent company Alphabet could spin off feeder products from advertising-funded search.
On what grounds? We know that bigness per se is no crime. Are these companies “essential facilities” that left fewer, more expensive options for consumers?
The problem is that many of these companies’ products cost us nothing, at least in terms of dollars. It doesn’t look like Facebook or Google have been lowering product quality, either. In general, there’s a lot of competition when it comes to social media platforms and search, even if people choose not to use them very much.
Silicon Valley’s Phoenix-like resurrection is a story of ingenuity and initiative. It is also a story of callousness, predation, and deceit. Harvard Business School professor emerita Shoshana Zuboff argues in her new book that the Valley’s wealth and power are predicated on an insidious, essentially pathological form of private enterprise — what she calls “surveillance capitalism.” Pioneered by Google, perfected by Facebook, and now spreading throughout the economy, surveillance capitalism uses human life as its raw material. Our everyday experiences, distilled into data, have become a privately owned business asset used to predict and mold our behavior, whether we’re shopping or socializing, working or voting.
Zuboff’s fierce indictment of the big internet firms goes beyond the usual condemnations of privacy violations and monopolistic practices. To her, such criticisms are sideshows, distractions that blind us to a graver danger: By reengineering the economy and society to their own benefit, Google and Facebook are perverting capitalism in a way that undermines personal freedom and corrodes democracy.
The work in the lab will focus on a discipline within artificial intelligence known as machine learning, in which computers learn from existing information and develop the ability to draw conclusions and make decisions in new situations that were not in the original data. Examples include speech recognition systems that transcribe a wide spectrum of voices, and self-driving cars that process complex visual cues. In particular, the work will build on recent advances by Hazan, Singer and colleagues in optimization methods for machine learning to improve their speed and accuracy while reducing the required computing power.