Thinking fast and slow in AI

Despite seeing tremendous advances in the recent decade, artificial intelligence is still lacking sorely in basic areas such as generalizability, adaptability, and causality. Today’s AI systems—mostly centered around machine learning and deep learning—are limited to narrow applications, require large amounts of training data or experience, and are very sensitive to changes in their environments.

Researchers are looking to various fields of science to find solutions to the current limits of AI systems. A new concept, proposed by researchers from various organizations and universities, draws inspiration from the two-system thinking framework proposed by Nobel laureate psychologist and economist Daniel Kahneman. Introduced in a paper published online, the technique is called SlOw and Fast AI (SOFAI). SOFAI uses meta-cognition to arbitrate between different modes of inference to improve the efficiency of AI systems in using data and compute resources.

Read the full article on TechTalks.

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