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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 entire 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. AI-aided engineering of the ecologies is already here.

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