A machine with a grasp of cause and effect could learn more like a human, through imagination and regret.
. It is obvious, to a person at least, that there is no causal relationship between the style and placement of the letter ‘R’ on an X-ray and signs of lung disease. But without that understanding, any differences in how such markings are drawn or positioned could be enough to steer a machine down the wrong path.
In Bhattacharya’s case, it was possible that some of the genes that the system was highlighting were responsible for a better response to the treatment. But a lack of understanding of causality meant that it was also possible that the treatment was affecting the gene expression — or that another, hidden factor was influencing both. The potential solution to this problem lies in something known as causal inference — a formal, mathematical way to ascertain whether one variable affects another.
In 2011, Pearl won the A.M. Turing Award, often referred to as the Nobel prize for computer science, for his work developing a calculus to allow probabilistic and causal reasoning. He describes a three-level hierarchy of reasoning. The base level is ‘seeing’, or the ability to make associations between things. Today’s AI systems are extremely good at this. Pearl refers to the next level as ‘doing’ — making a change to something and noting what happens. This is where causality comes into play.
This approach is akin to how people work something out: people generate possible causal relationships, and assume that the ones that best fit an observation are closest to the truth. Watching a glass shatter when it is dropped it onto concrete, for instance, might lead a person to think that the impact on a hard surface causes the glass to break.
Bhattacharya likes to explain such counterfactuals to his students by reading them ‘The Road Not Taken’ by Robert Frost. In this poem, the narrator talks of having to choose between two paths through the woods, and expresses regret that they can’t know where the other road leads. “He’s imagining what his life would look like if he walks down one path versus another,” Bhattacharya says.
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