Artificial Intelligence Still Can't Form Concepts

Artificial Intelligence Still Can't Form Concepts

By Bennie Mols

Commissioned by CACM Staff

Melanie Mitchell.

"If the goal is to create an AI system that has humanlike abstraction abilities, then it does not make sense to have to train it on tens of thousands of examples," Mitchell said. "The essence of abstraction and analogy is few-shot learning."

Machine translation, automatic speech recognition, and automatic text generation demonstrate the enormous progress artificial intelligence (AI) has made in processing human language. On the other hand, AI has made astonishingly little progress in forming concepts and abstractions. That is the research area of Melanie Mitchell, professor of complexity at the Santa Fe Institute and author of the book Artificial Intelligence – A Guide for Thinking Humans.

Mitchell argues forming concepts is absolutely crucial to unlock the full potential of AI. "A concept is a fundamental unit of understanding," Mitchell said during an interview at the 2023 American Association for the Advancement of Science (AAAS) Annual Meeting in Washington, D.C. "Neural networks can look at a picture and tell whether it contains a dog, a cat, or a car, but they do not have a rich understanding of any of those categories.

"Take the concept of a bridge. Humans can extend the notion of a bridge to abstract levels. We can talk about a bridge between people or bridging the gender gap. We can instantly understand what these expressions mean because we have a rich mental model of what a bridge can be."

Mitchell first started working on concepts and abstraction in 1984, as a Ph.D. student of Douglas Hofstadter. Inspired by Hofstadter's famous book Gödel, Escher, Bach: An Essential Golden Braid, Mitchell decided to contact him, and that was the start of their cooperation. Together they created an AI system called Copycat, which can solve simple letter-string analogy problems. For example, given the letter-strings ABC and PQR, which string follows after AABBCC? Copycat could then find the answer PPQQRR by using a mental model that included symbolic, sub-symbolic, and probabilistic elements.

Copycat had huge limitations: its architecture was ad hoc, it was unclear how general the architecture was, and it was unclear how to form new concepts beyond what was given in its prior conceptual repertoire. In the roughly three decades that have passed since Copycat was released, there have been various efforts to create AI systems that form abstractions and concepts, but the problem fundamentally is still unsolved.

In recent years, some scientists have shown that deep learning systems can perform better than the average human (see for example https://arxiv.org/abs/2012.01944) on Raven's Progressive Matrices, a widely used non-verbal test of general human intelligence and abstract reasoning (for example, given a set of visual geometric designs, the subject has to identify a missing piece at the end). However, Mitchell found that deep learning systems did not accomplish this by learning humanlike concepts, but by finding shortcuts. Furthermore, they needed a large corpus of training examples.

"If the goal is to create an AI system that has humanlike abstraction abilities, then it does not make sense to have to train it on tens of thousands of examples," Mitchell said. "The essence of abstraction and analogy is few-shot learning."

What about large language models, like GPT? Don't they have the capability to form humanlike concepts and abstractions? "Interestingly, they can make analogies to some extent," said Mitchell. "I have tried some letter-string problems in GPT-3, and in some cases it could solve them. It learned, for example, the concept of successorship. Not perfect, not robust, but I found it still surprising that it can do this. Therefore, I don't agree that these systems are only 'stochastic parrots', as some scientists have called them. I have seen evidence of GPT building simple internal models of situations."