Alex “Sandy” Pentland

THE HUMAN STRATEGY Alex “Sandy” Pentland Alex “Sandy” Pentland is Toshiba Professor and professor of media arts and sciences, MIT; director of the Human Dynamics and Connection Science labs and the Media Lab Entrepreneurship Program, and the author of Social Physics. In the last half-century, the idea of AI and intelligent robots has dominated thinking about the relationship between humans and computers. In part, this is because it’s easy to tell the stories about AI and robots, and in part because of early successes (e.g., theorem provers that reproduced most of Whitehead and Russell’s Principia Mathematica) and massive military funding. The earlier and broader vision of cybernetics, which considered the artificial as part of larger systems of feedback and mutual influence, faded from public awareness. However, in the intervening years the cybernetics vision has slowly grown and quietly taken over—to the point where it is “in the air.” State-of-the-art research in most engineering disciplines is now framed as feedback systems that are dynamic and driven by energy flows. Even AI is being recast as human/machine “advisor” systems, and the military is beginning large-scale funding in this area—something that should perhaps worry us more than drones and independent humanoid robots. But as science and engineering have adopted a more cybernetics-like stance, it has become clear that even the vision of cybernetics is far too small. It was originally centered on the embeddedness of the individual actor but not on the emergent properties of a network of actors. This is unsurprising, because the mathematics of networks did not exist until recently, so a quantitative science of how networks behave was impossible. We now know that study of the individual does not produce understanding of the system except in certain simple cases. Recent progress in this area was foreshadowed by understanding that “chaos,” and later “complexity,” were the typical behavior of systems, but we can now go far beyond these statistical understandings. We’re beginning to be able to analyze, predict, and even design the emergent behavior of complex heterogeneous networks. The cybernetics view of the connected individual actor can now be expanded to cover complex systems of connected individuals and machines, and the insights we obtain from this broader view are fundamentally different from those obtained from the cybernetics view. Thinking about the network is analogous to thinking about entire ecosystems. How would you guide ecosystems to grow in a good direction? What do you even mean by “a good direction”? Questions like this are beyond the boundary of traditional cybernetic thinking. Perhaps the most stunning realization is that humans are already beginning to use AI and machine learning to guide entire ecosystems, including ecosystems of people, thus creating human-AI ecologies. Now that everything is becoming “datafied,” we can measure most aspects of human life and, increasingly, aspects of all life. This, together with new, powerful machine-learning techniques, means that we can build models of these ecologies in ways we couldn’t before. Well-known examples are weather- and traffic-prediction models, which are being extended to predict the global climate and plan city growth and renewal. AlI-aided engineering of the ecologies is already here. 135 HOUSE_OVERSIGHT_016938