Causal disentanglement is the next frontier in AI

phys.org | 2/20/2019 | Staff
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Recreating the human mind's ability to infer patterns and relationships from complex events could lead to a universal model of artificial intelligence.

A major challenge for artificial intelligence (AI) is having the ability to see past superficial phenomena to guess at the underlying causal processes. New research by KAUST and an international team of leading specialists has yielded a novel approach that moves beyond superficial pattern detection.

Humans - Sense - Intuition - Inference - Insight

Humans have an extraordinarily refined sense of intuition or inference that give us the insight, for example, to understand that a purple apple could be a red apple illuminated with blue light. This sense is so highly developed in humans that we are also inclined to see patterns and relationships where none exist, giving rise to our propensity for superstition.

This type of insight is such a challenge to codify in AI that researchers are still working out where to start: yet it represents one of the most fundamental difference between natural and machine thought.

Years - Collaboration - KAUST-affiliated - Researchers - Hector

Five years ago, a collaboration between KAUST-affiliated researchers Hector Zenil and Jesper Tegnér, along with Narsis Kiani and Allan Zea from Sweden's Karolinska Institutet, began adapting algorithmic information theory to network and systems biology in order to address fundamental problems in genomics and molecular circuits. That collaboration led to the development of an algorithmic approach to inferring causal processes that could form the basis of a universal model of AI.

"Machine learning and AI are becoming ubiquitous in industry, science and society," says KAUST professor Tegnér. "Despite recent progress, we are still far from achieving...
(Excerpt) Read more at: phys.org
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