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Understanding Causality Is the Next Challenge for Machine Learning

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“Causality is very important for the next steps of progress of machine learning,” said Yoshua Bengio, a Turing Award-wining scientist known for his work in deep learning, in an interview with IEEE Spectrum in 2019. So far, deep learning has comprised learning from static datasets, which makes AI really good at tasks related to correlations and associations. However, neural nets do not interpret cause-and effect, or why these associations and correlations exist. Nor are they particularly good at tasks that involve imagination, reasoning, and planning. This, in turn, limits AI from being able to generalize their learning and transfer their skills to another related environment.

The lack of generalization is a big problem, says Ossama Ahmed, a master’s student at ETH Zurich who has worked with Bengio’s team to develop a robotic benchmarking tool for causality and transfer learning. “Robots are [often] trained in simulation, and then when you try to deploy [them] in the real world…they usually fail to transfer their learned skills. One of the reasons is that the physical properties of the simulation are quite different from the real world,” says Ahmed. The group’s tool, called CausalWorld, demonstrates that with some of the methods currently available, the generalization capabilities of robots aren’t good enough—at least not to the extent that “we can deploy [them] safely in any arbitrary situation in the real world,” says Ahmed.

CausalWorld’s evaluation protocols, say Ahmed and Träuble, are more versatile than those in previous studies because of the possibility of “disentangling” generalization abilities. In other words, users are free to intervene on a large number of variables in the environment, and thus draw systemic conclusions about what the agent generalizes to—or doesn’t. The next challenge, they say, is to actually use the tools available in CausalWorld to build more generalizable systems.

Despite how dazzled we are by AI’s ability to perform certain tasks, Yoshua Bengio, in 2019, estimated that present-day deep learning is less intelligent than a two-year-old child. Though the ability of neural networks to parallel-process on a large scale has given us breakthroughs in computer vision, translation, and memory, research is now shifting to developing novel deep architectures and training frameworks for addressing tasks like reasoning, planning, capturing causality, and obtaining systematic generalization. “I believe it’s just the beginning of a different style of brain-inspired computation,” Bengio said, adding, “I think we have a lot of the tools to get started.”

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 (