A Familiar, Invisible Engine Is Driving the AI Revolution
Matan Gavish · Harvard Data Science Review · 2024
A Familiar, Invisible Engine Is Driving the AI Revolution 2 People in 17th-to 20th-century Europe were not smarter or harder working than people in 10th-century Europe.How, then, were they able to figure out the structure of matter and the universe, go flying into space, double the average length of human life, and master the transfer of energy and information-in just 300 years?What was the mysterious mechanism driving science and innovation at such an incredible pace?In my view, David Donoho's (2024) article calls our attention to such a mechanism, the overwhelming effects of which are plain to see, while it itself remains invisible.Elusive as it may be, in what follows I will claim that understanding the mechanism that Donoho is laying out is perhaps the most crucial insight for today's statisticians and data scientists. Cooperation in Large Groups Is a Collective Human SuperpowerWe human beings have an exceptional ability to cooperate in large groups.One can easily make the case that it is this capacity-and not our thumbs-that made us the undisputed masters of our planet.Cooperation in large groups is so natural to us that we hardly ever notice it for the collective superpower that it truly is.Scientists and engineers in 17th-to 20th-century Europe, and gradually everywhere else, could accomplish so much in 300 years because they were able to cooperate with other scientists and engineers in large groups, which transcended space and time.Isaac Newton did not have to personally know Johannes Kepler; Albert Einstein did not have to personally know Albert Michelson and Edward Morley.Every single discovery in science, engineering, and medicine is the result of an individual extending the work of many hundreds or thousands of peers whom they never could have met in person.How did they cooperate?What makes cooperation in a scientific community possible?They had (i) a joint goal, (ii) a mode of large-scale participation in the collaborative effort, and (iii) shared critical standards for choosing which contribution points 'forward.'Focusing on the scientific enterprise, the joint goal was to provide a human-graspable description of observed natural phenomena; the mode of participation was contribution of peer-reviewed papers to scientific journals; and the shared critical standards were Francis Bacon's scientific method.Other large-scale collaborative communities such as engineering, medicine, and mathematics used variations on this theme, with different joint goals and different shared critical standards.Donoho mentioned fish in water, and I was reminded of someone else who spoke of fish being blind to water: Marshall McLuhan, the 20th-century Canadian scholar who defined what we know today as the field of