The Future of Machine Intelligence

Introduction

Machine intelligence has been the subject of both exuberance and skepticism for decades. The promise of thinking, reasoning machines appeals to the human imagination, and more recently, the corporate budget. Beginning in the 1950s, Marvin Minksy, John McCarthy and other key pioneers in the field set the stage for today’s breakthroughs in theory, as well as practice. Peeking behind the equations and code that animate these peculiar machines, we find ourselves facing questions about the very nature of thought and knowledge. The mathematical and technical virtuosity of achievements in this field evoke the qualities that make us human: Everything from intuition and attention to planning and memory. As progress in the field accelerates, such questions only gain urgency.

Heading into 2016, the world of machine intelligence has been bustling with seemingly back-to-back developments. Google released its machine learning library, TensorFlow, to the public. Shortly thereafter, Microsoft followed suit with CNTK, its deep learning framework. Silicon Valley luminaries recently pledged up to one billion dollars towards the OpenAI institute, and Google developed software that bested Europe’s Go champion. These headlines and achievements, however, only tell a part of the story. For the rest, we should turn to the practitioners themselves. In the interviews that follow, we set out to give readers a view to the ideas and challenges that motivate this progress.

We kick off the series ...