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Gary Marcus

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6 papers
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6

AAAI Conference 2026 Conference Paper

The Future Is Neuro-Symbolic: Where Has It Been, and Where Is It Going?

  • Vaishak Belle
  • Gary Marcus

This report explores the evolution and current state of neuro- symbolic artificial intelligence, an approach that integrates neural network capabilities with symbolic reasoning. We trace the historical context from early AI aspirations to modern implementations and successes, highlighting key paradigms, and other logical and semantical considerations. We argue against the “scaling is all you need” hypothesis, and point to persistent challenges in reliable symbolic reasoning with deep and large models. We conclude by suggesting that despite numerous implementation choices and the ”broad church” nature of neuro-symbolic AI, these approaches offer the most promising path towards AI systems that combine pattern recognition with robust reasoning, particularly for applications requiring structured knowledge, explainability, and trustworthiness.

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AAAI Conference 2024 Conference Paper

The Defeat of the Winograd Schema Challenge (Abstract Reprint)

  • Vid Kocijan
  • Ernest Davis
  • Thomas Lukasiewicz
  • Gary Marcus
  • Leora Morgenstern

The Winograd Schema Challenge—a set of twin sentences involving pronoun reference disambiguation that seem to require the use of commonsense knowledge—was proposed by Hector Levesque in 2011. By 2019, a number of AI systems, based on large pre-trained transformer-based language models and fine-tuned on these kinds of problems, achieved better than 90% accuracy. In this paper, we review the history of the Winograd Schema Challenge and discuss the lasting contributions of the flurry of research that has taken place on the WSC in the last decade. We discuss the significance of various datasets developed for WSC, and the research community's deeper understanding of the role of surrogate tasks in assessing the intelligence of an AI system.

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AIJ Journal 2023 Journal Article

The defeat of the Winograd Schema Challenge

  • Vid Kocijan
  • Ernest Davis
  • Thomas Lukasiewicz
  • Gary Marcus
  • Leora Morgenstern

The Winograd Schema Challenge—a set of twin sentences involving pronoun reference disambiguation that seem to require the use of commonsense knowledge—was proposed by Hector Levesque in 2011. By 2019, a number of AI systems, based on large pre-trained transformer-based language models and fine-tuned on these kinds of problems, achieved better than 90% accuracy. In this paper, we review the history of the Winograd Schema Challenge and discuss the lasting contributions of the flurry of research that has taken place on the WSC in the last decade. We discuss the significance of various datasets developed for WSC, and the research community's deeper understanding of the role of surrogate tasks in assessing the intelligence of an AI system.

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TMLR Journal 2022 Journal Article

Benchmarking Progress to Infant-Level Physical Reasoning in AI

  • Luca Weihs
  • Amanda Yuile
  • Renée Baillargeon
  • Cynthia Fisher
  • Gary Marcus
  • Roozbeh Mottaghi
  • Aniruddha Kembhavi

To what extent do modern AI systems comprehend the physical world? We introduce the open-access Infant-Level Physical Reasoning Benchmark (InfLevel) to gain insight into this question. We evaluate ten neural-network architectures developed for video understanding on tasks designed to test these models' ability to reason about three essential physical principles which researchers have shown to guide human infants' physical understanding. We explore the sensitivity of each AI system to the continuity of objects as they travel through space and time, to the solidity of objects, and to gravity. We find strikingly consistent results across 60 experiments with multiple systems, training regimes, and evaluation metrics: current popular visual-understanding systems are at or near chance on all three principles of physical reasoning. We close by suggesting some potential ways forward.

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AIJ Journal 2017 Journal Article

Commonsense reasoning about containers using radically incomplete information

  • Ernest Davis
  • Gary Marcus
  • Noah Frazier-Logue

In physical reasoning, humans are often able to carry out useful reasoning based on radically incomplete information. One physical domain that is ubiquitous both in everyday interactions and in many kinds of scientific applications, where reasoning from incomplete information is very common, is the interaction of containers and their contents. We have developed a preliminary knowledge base for qualitative reasoning about containers, expressed in a sorted first-order language of time, geometry, objects, histories, and actions. We have demonstrated that the knowledge suffices to justify a number of commonsense physical inferences, based on very incomplete knowledge.

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AIJ Journal 2016 Journal Article

The scope and limits of simulation in automated reasoning

  • Ernest Davis
  • Gary Marcus

In scientific computing and in realistic graphic animation, simulation – that is, step-by-step calculation of the complete trajectory of a physical system – is one of the most common and important modes of calculation. In this article, we address the scope and limits of the use of simulation, with respect to AI tasks that involve high-level physical reasoning. We argue that, in many cases, simulation can play at most a limited role. Simulation is most effective when the task is prediction, when complete information is available, when a reasonably high quality theory is available, and when the range of scales involved, both temporal and spatial, is not extreme. When these conditions do not hold, simulation is less effective or entirely inappropriate. We discuss twelve features of physical reasoning problems that pose challenges for simulation-based reasoning. We briefly survey alternative techniques for physical reasoning that do not rely on simulation.

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v2026.09.13