Can symbolic models make a comeback in AI?

The split led to the myth that while it was easier to automate man’s higher reasoning functions, it was harder to automate the functions that humans shared with other animals.

Last week, Twitter turned into a battleground in the discussion around the significance of symbolic models in AI versus deep learning. Melanie Mitchell, author and Davis Professor at the Santa Fe Institute, posted a Twitter thread speaking about how the main ideas under artificial intelligence were transforming with time. Mitchell notes that AI was defined as a study of intelligence from the context of symbolic systems and problem-solving. On the other hand, continuous systems, pattern recognition, learning and neural networks were believed to be in the domain of cybernetics. Mitchell points out that these terms have become vastly what constitutes AI now.

Chief AI Scientist at Meta, Yann LeCun, who laid the foundations for deep learning and convolutional neural networks (CNNs), responded to the thread initially stating that it was the media that started referring to deep learning as AI, not him.

LeCun then retweeted a response and sent a flurry of tweets saying that rather than making assumptions, people should conduct thorough research to show that symbolic models worked as well as deep learning. LeCun further clarified himself, saying that he wasn’t being a “bully to people” or “asking them to shut up.” Instead, he was welcoming diverse ideas as long as they were substantiated with proof.

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