When you look at a baseball player hitting the ball, you can make inferences about causal relations between different elements. For instance, you can see the bat and the baseball player’s arm moving in unison, but you also know that it is the player’s arm causing the bat’s movement and not the other way around. You also don’t need to be told that the bat is causing the sudden change in the ball’s direction.
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Likewise, you can think about counterfactuals, such as what would happen if the ball flew a bit higher and didn’t hit the bat.
Such inferences come to us humans intuitively. We learn them at a very early age, without being explicitly instructed by anyone and just by observing the world. But for algorithms, which have managed to outperform humans in complicated tasks such as Go and chess, causality remains a challenge. Machine learning algorithms, especially deep neural networks, are especially good at ferreting out subtle patterns in huge sets of data. They can transcribe audio in real time, label thousands of images and video frames per second, and examine X-ray and MRI scans for cancerous patterns. But they struggle to make simple causal inferences like the ones we just saw in the baseball example above.
In a paper titled “Towards Causal Representation Learning,” researchers at the Max Planck Institute for Intelligent Systems, the Montreal Institute for Learning Algorithms (Mila), and Google Research discuss the challenges arising from the lack of causal representations in models and provide directions for creating systems that can learn causal representations.
This is one of several efforts that aim to explore and solve ’s lack of causality, which can be key to overcoming some of the major challenges the field faces today.
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