The Limits of AI: Induction, Deduction, and Why Models Can't Jump

The article discusses the limitations of current AI systems, specifically large language models (LLMs), in terms of their reasoning capabilities. It explores the three modes of inference, as defined by philosopher Charles Sanders Peirce: induction, deduction, and abduction. The article then explains why LLMs have excelled at induction and deduction but struggle with abduction, which involves generating novel hypotheses.

Induction involves generalizing from observed data to create probable rules and regularities. Deduction, on the other hand, uses established premises and axioms to derive guaranteed conclusions. Abduction, the final mode, is the creative leap that invents a new explanatory premise or axiom to explain anomalies.

The article compares the reasoning abilities of LLMs to classical examples, such as the beanbag scenario. Deduction would involve deriving the conclusion that the beans in Bag A are white, given the initial premises. Induction would involve generalizing that all beans from Bag A are likely white based on observed evidence. Abduction would involve hypothesizing that the white beans came from Bag A.

The article emphasizes that LLMs have mastered induction and deduction due to their extensive training on vast amounts of data and their ability to follow established logical rules. However, they are structurally incapable of abduction because they lack the capacity to invent entirely new axioms or concepts when faced with unexpected situations. This fundamental limitation defines the current computational upper bound of machine intelligence and sets it apart from human reasoning capabilities.