scientists are doing science, especially in such data-intensive sciences as sociology and

scientists are doing science, especially in such data-intensive sciences as sociology and epidemiology, for which causal models have become a second language. These disciplines view their linguistic transformation as the Causal Revolution. As Harvard social scientist Gary King puts it, “More has been learned about causal inference in the last few decades than the sum total of everything that had been learned about it in all prior recorded history.” As I contemplate the success of machine learning and try to extrapolate it to the future of AI, I ask myself, “Are we aware of the basic limitations that were discovered in the causal-inference arena? Are we prepared to circumvent the theoretical impediments that prevent us from going from one level of the hierarchy to another level?” I view machine learning as a tool to get us from data to probabilities. But then we still have to make two extra steps to go from probabilities into real understandingnce— two big steps. One is to predict the effect of actions, and the second is counterfactual imagination. We cannot claim to understand reality unless we make the last two steps. In his insightful book Foresight and Understanding (1961), the philosopher Stephen Toulmin identified the transparency-versus-opacity contrast as the key to understanding the ancient rivalry between Greek and Babylonian sciences. According to Toulmin, the Babylonian astronomers were masters of black-box predictions, far surpassing their Greek rivals in accuracy and consistency of celestial observations. Yet Science favored the creative-speculative strategy of the Greek astronomers, which was wild with metaphorical imagery: circular tubes full of fire, small holes through which celestial fire was visible as stars, and hemispherical Earth riding on turtleback. It was this wild modeling strategy, not Babylonian extrapolation, that jolted Eratosthenes (276- 194 BC) to perform one of the most creative experiments in the ancient world and calculate the circumference of the Earth. Such an experiment would never have occurred to a Babylonian data-fitter. Model-blind approaches impose intrinsic limitations on the cognitive tasks that Strong Al can perform. My general conclusion is that human-level AI cannot emerge solely from model-blind learning machines; it requires the symbiotic collaboration of data and models. Data science is a science only to the extent that it facilitates the interpretation of data—a two-body problem, connecting data to reality. Data alone are hardly a science, no matter how “big” they get and how skillfully they are manipulated. Opaque learning systems may get us to Babylon, but not to Athens. 27 HOUSE_OVERSIGHT_016830