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Abstraction and analogy-making in artificial intelligence

Paper · 2021 · Melanie Mitchell

Summary

Mitchell argues that today's AI, despite its advances in vision, language and robotics, still cannot form concepts, abstractions or analogies the way people do. She links this gap to the brittleness of current systems: weak transfer beyond their training data, odd errors, and sensitivity to adversarial inputs. Drawing on cognitive science, she presents analogy as an everyday mechanism behind categorization, concept formation and counterfactual thinking, and sets aside simple proportional analogies in favor of multipart situations. The paper is a selective review rather than an empirical study. It covers symbolic and hybrid approaches, such as the Structure-Mapping Engine and Hofstadter's active-symbol architecture, along with deep learning and probabilistic program induction, tested mostly in idealized domains such as Raven's Progressive Matrices, letter-string analogies and ARC. Its main conclusion is that the problem is still nearly entirely open, and that progress is hard to measure because current benchmarks overlook generality, robustness and scaling. The paper matters because it proposes concrete design principles for future challenge tasks and evaluation.

Key points

  • The Structure-Mapping Engine (SME; Falkenhainer et al.) builds on Gentner's structure-mapping theory. It pairs base and target elements using match rules, then combines them into consistent "global matches." It favors relations over attributes and rewards systematicity, meaning deeply nested relational structure. SME models only the mapping step, so its inputs must already be encoded as logic.
  • Lovett et al. used SME inside a larger system to solve Raven's Progressive Matrices (RPMs). RPMs are nonverbal tests of fluid intelligence that John Raven created in the 1930s.
  • Following Chollet, benchmarks should not let systems gain performance by training extensively on the target domain. Systems should learn core concepts and then adapt them to new tasks.
  • Mitchell prefers generative tasks, such as letter-string analogies and ARC, over discriminative ones such as RPMs, because they are harder to solve via shortcuts and easier to interpret.
  • Test sets should be hidden, curated by humans rather than generated procedurally, and refreshed over time. Evaluation should measure robustness to noise, conceptual variation and scaling, not just accuracy.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🔬Santa Fe InstituteSanta Fe · United States7 works · 646 citations

 Fields, topics and concepts

📚 Shelves 2The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 works

💡 Concepts 1Key ideas and methods explained on our concept pages.Artificial intelligence · 49 works

Cite

Cite as Melanie Mitchell. 2021. Abstraction and analogy-making in artificial intelligence. Annals of the New York Academy of Sciences.

The paper itself, not this page. BibTeX from the library's record of the work. arXiv 2102.10717

@article{mitchell2021abstraction,
  title = {{Abstraction and analogy-making in artificial intelligence}},
  author = {Melanie Mitchell},
  journal = {Annals of the New York Academy of Sciences},
  year = {2021},
  doi = {10.1111/nyas.14619},
  eprint = {2102.10717},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.1111/nyas.14619}
}

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