January 17, 2021
The original article was published at Digital Trends.
Remember the amazing, revelatory feeling when you first discovered the existence of cause and effect? That’s a trick question. Kids start learning the principle of causality from as early as eight months old, helping them to make rudimentary inferences about the world around them. But most of us don’t remember much before the age of around three or four, so the important lesson of “why” is something we simply take for granted.
It’s not only a crucial lesson for humans to learn, but also one that today’s artificial intelligence systems are pretty darn bad at. While modern A.I. is capable of beating human players at Go and driving cars on busy streets, this is not necessarily comparable with the kind of intelligence humans might use to master these abilities. That’s because humans — even small infants — possess the ability to generalize by applying knowledge from one domain to another. For A.I. to live up to its potential, this is something it also needs to be able to do.
“For instance, if the robot learned how to build a tower using some blocks, it may want to transfer these skills to building a bridge or even a house-like structure,” Ossama Ahmed, a master’s student at ETH Zurich in Switzerland, told Digital Trends. “One way to achieve this might be learning the causal relationships between the different environment variables. Or imagine that the TriFinger robot used in CausalWorld suddenly loses one finger due to a hardware malfunction. How can it still build the goal shape with only two fingers instead?”
“Most of modern A.I. is based on statistical learning, which is all about extracting statistical information — for example, correlations — from data,” Bernhard Schölkopf, director of the Max Planck Institute, told Digital Trends. “This is great because it allows us to predict one quantity from others, but only as long as nothing changes. When you intervene in a system, then all bets are off. To make predictions in such cases, we need to go beyond statistical learning, towards causality. Ultimately, if future A.I. is to be about thinking in the sense of ‘acting in imagined spaces,’ then interventions are key, and thus causality needs to be taken into account.”
In the field of AI and Causality, Professor Judea Pearl is a pioneer for developing a theory of causal and counterfactual inference based on structural models. In 2011, Professor Pearl won the Turing Award, computer science’s highest honor, for “fundamental contributions to artificial intelligence through the development of a calculus of probabilistic and causal reasoning.” In 2020, Michael Dukakis Institute also awarded Professor Pearl as World Leader in AI World Society (AIWS.net) for Leadership and Innovation (MDI) and Boston Global Forum (BGF). At this moment, Professor Judea is a Mentor of AIWS.net and Head of Modern Causal Inference section, which is one of important AIWS.net topics on AI Ethics.