cannot serve as the basis for Strong Al—that is, artificial intelligence that emulates human-level reasoning and competence. To achieve human-level intelligence, learning machines need the guidance of a blueprint of reality, a model—similar to a road map that guides us in driving through an unfamiliar city.
To be more specific, current learning machines improve their performance by optimizing parameters for a stream of sensory inputs received from the environment. It is a slow process, analogous to the natural-selection process that drives Darwinian evolution. It explains how species like eagles and snakes have developed superb vision systems over millions of years. It cannot explain, however, the super-evolutionary process that enabled humans to build eyeglasses and telescopes over barely a thousand years. What humans had that other species lacked was a mental representation of their environment—representations that they could manipulate at will to imagine alternative hypothetical environments for planning and learning.
Historians of Homo sapiens such as Yuval Noah Harari and Steven Mithen are in general agreement that the decisive ingredient that gave our ancestors the ability to achieve global dominion about forty thousand years ago was their ability to create and store a mental representation of their environment, interrogate that representation, distort it by mental acts of imagination, and finally answer the “What if?” kind of questions. Examples are interventional questions (“What if I do such-and-such?”) and retrospective or counterfactual questions (“What if I had acted differently?”). No learning machine in operation today can answer such questions. Moreover, most learning machines do not possess a representation from which the answers to such questions can be derived.
With regard to causal reasoning, we find that you can do very little with any form of model-blind curve fitting, or any statistical inference, no matter how sophisticated the fitting process is. We have also found a theoretical framework for organizing such limitations, which forms a hierarchy.
On the first level, you have statistical reasoning, which can tell you only how seeing one event would change your belief about another. For example, what can a symptom tell you about a disease?
Then you have a second level, which entails the first but not vice versa. It deals with actions. “What will happen if we raise prices?” “What if you make me laugh?” That second level of the hierarchy requires information about interventions which is not available in the first. This information can be encoded in a graphical model, which merely tells us which variable responds to another.
The third level of the hierarchy is the counterfactual. This is the language used by scientists. “What if the object were twice as heavy?” “What if I were to do things differently?” “Was it the aspirin that cured my headache, or the nap I took?” Counterfactuals are at the top level in the sense that they cannot be derived even if we could predict the effects of all actions. They need an extra ingredient, in the form of equations, to tell us how variables respond to changes in other variables.
One of the crowning achievements of causal-inference research has been the algorithmization of both interventions and counterfactuals, the top two layers of the hierarchy. In other words, once we encode our scientific knowledge in a model (which may be qualitative), algorithms exist that examine the model and determine if a given query, be it about an intervention or about a counterfactual, can be estimated from the available data—and, if so, how. This capability has transformed dramatically the way
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