# Gary Marcus

> Source: https://aiwiki.ai/wiki/gary_marcus
> Updated: 2026-06-24
> Categories: People
> From AI Wiki (https://aiwiki.ai), a free encyclopedia of artificial intelligence. Quote with attribution.

Gary F. Marcus (born 1970 in Baltimore, Maryland) is an American [cognitive scientist](/wiki/cognitive_science), author, and entrepreneur who is the best known public critic of contemporary [deep learning](/wiki/deep_learning) and [large language models](/wiki/large_language_model), and a leading advocate of [neurosymbolic AI](/wiki/neurosymbolic_ai). He is professor emeritus of psychology and neural science at [New York University](/wiki/nyu), where he taught from 1992 to 2014, the author of the 2018 paper "Deep Learning: A Critical Appraisal" (arXiv:1801.00631) and the 2022 essay "Deep Learning Is Hitting a Wall," and the witness who testified before the U.S. Senate alongside OpenAI chief executive [Sam Altman](/wiki/sam_altman) on May 16, 2023. [1][8][11] His core argument, repeated across decades, is that scaling neural networks alone will not deliver reliable artificial intelligence and that progress requires hybrid systems that combine neural networks with the explicit rules of classical [symbolic AI](/wiki/symbolic_ai).

Marcus founded two artificial intelligence companies: [Geometric Intelligence](/wiki/geometric_intelligence), acquired by Uber in December 2016 to form the core of [Uber AI Labs](/wiki/uber_ai_labs), and the robotics company [Robust.AI](/wiki/robust_ai), co-founded in 2019. [3][4] He has written six books, three of them on artificial intelligence, including "Rebooting AI" (2019, with [Ernest Davis](/wiki/ernest_davis)) and "Taming Silicon Valley" (2024). [15][16] He runs the [Substack](/wiki/substack) newsletter "Marcus on AI" (originally titled "The Road to AI We Can Trust"), launched in 2022, in which he has documented hallucinations, regressions, and reliability problems in commercial language models. [2] His public disagreements with researchers including [Yann LeCun](/wiki/yann_lecun) and [Yoshua Bengio](/wiki/yoshua_bengio) have made him one of the most visible skeptics of the view that scaling alone will produce general intelligence.

## Facts

| | |
|---|---|
| Born | 1970, Baltimore, Maryland, United States |
| Nationality | American |
| Spouse | Athena Vouloumanos |
| Children | 2 |
| Alma mater | [Hampshire College](/wiki/hampshire_college) (BA, 1989); [Massachusetts Institute of Technology](/wiki/mit) (PhD, 1993) |
| Doctoral advisor | [Steven Pinker](/wiki/steven_pinker) |
| Fields | Cognitive psychology, language acquisition, artificial intelligence |
| Institutions | [New York University](/wiki/nyu) (1992 to 2014; emeritus thereafter) |
| Companies founded | [Geometric Intelligence](/wiki/geometric_intelligence) (2014, acquired by Uber 2016); [Robust.AI](/wiki/robust_ai) (2019) |
| Notable books | "The Algebraic Mind" (2001); "Kluge" (2008); "Rebooting AI" (2019); "Taming Silicon Valley" (2024) |
| Newsletter | "Marcus on AI" on [Substack](/wiki/substack), since 2022 |
| Notable testimony | U.S. Senate Judiciary Subcommittee, May 16, 2023 |

## Early life and education

Marcus was born in 1970 in Baltimore, Maryland, and grew up in Hagerstown, Maryland and in Williamstown, Massachusetts. In published interviews and in his book "Kluge," he has described an interest in computer programming that began in childhood: at age eight he wrote a Logo program that translated English into Pig Latin, and as a teenager he built simulations of intelligent behavior in BASIC.

Marcus attended [Hampshire College](/wiki/hampshire_college), a small liberal arts institution in Amherst, Massachusetts, completing his bachelor's degree in 1989 in three years through the college's Division III thesis system. His undergraduate work focused on cognition and computation. He has named Neil Stillings, a Hampshire cognitive scientist, as an early mentor. Hampshire College later named him a Distinguished Alumnus. [18]

From Hampshire he went directly to [MIT](/wiki/mit) for graduate work in the Department of Brain and Cognitive Sciences, where he became the first doctoral student of [Steven Pinker](/wiki/steven_pinker). His PhD, completed in 1993 with a thesis titled "On rules and exceptions: an investigation of inflectional morphology," examined how children learn the past tense of English verbs. [1] The thesis fed directly into the long running "past tense debate" between Pinker's group and proponents of [connectionism](/wiki/connectionism) including David Rumelhart and Jay McClelland, who had argued in 1986 that a single neural network could account for both regular and irregular forms. Marcus and Pinker published a series of papers between 1992 and 1995 arguing that the data showed two systems at work, a rule for regulars and an associative memory for irregulars. The Society for Research in Child Development published the resulting monograph "Overregularization in Language Acquisition" in 1992. [14]

## Academic career at NYU

Marcus joined [New York University](/wiki/nyu) in 1992 and was promoted to full professor in the Department of Psychology, with a secondary appointment in the Center for Neural Science. He also served as a co-founder and director of the NYU Center for Language and Music, an interdisciplinary research group studying the cognitive and neural foundations of music and speech. He took emeritus status in 2014 to focus on his startup work, although he retained his NYU affiliation. [1]

His academic work falls into three main areas. In language acquisition, he and his collaborators conducted infant studies showing that seven month olds can extract abstract algebraic rules from short artificial speech samples, a result published in Science in 1999 ("Rule learning by seven-month-old infants"). [13] In cognitive architecture, his book "The Algebraic Mind: Integrating Connectionism and Cognitive Science" (MIT Press, 2001) argued that neural networks of the time could not support the kind of variable binding and structured representation that humans use, and that hybrid models were needed. In genetics and development, his book "The Birth of the Mind: How a Tiny Number of Genes Creates the Complexities of Human Thought" (Basic Books, 2004) explained how a small genome can specify a complex brain.

His 2008 trade book "Kluge: The Haphazard Construction of the Human Mind" (Houghton Mifflin) argued that the human mind is a patchwork of older and newer systems rather than an optimal design. [17] The book was a New York Times bestseller and was reviewed in venues including the New York Review of Books and the Guardian.

Marcus has been elected a fellow of the American Association for the Advancement of Science. He received the Robert J. Glushko Prize for Outstanding Article in Cognitive Science (2003) and was awarded the Bertrand Russell Society's Bertrand Russell Award. [19]

## Career timeline

| Year | Event |
|---|---|
| 1989 | BA, [Hampshire College](/wiki/hampshire_college) |
| 1992 | Joins NYU as assistant professor; co-authors first papers with Steven Pinker |
| 1993 | PhD, [MIT](/wiki/mit), under [Steven Pinker](/wiki/steven_pinker) |
| 1999 | "Rule learning by seven-month-old infants" published in Science |
| 2001 | Publishes "The Algebraic Mind" |
| 2004 | Publishes "The Birth of the Mind" |
| 2008 | Publishes "Kluge"; book reaches NYT bestseller list |
| 2014 | Co-founds [Geometric Intelligence](/wiki/geometric_intelligence); takes emeritus status at NYU |
| 2016 | Geometric Intelligence acquired by Uber (December) |
| 2018 | Posts "Deep Learning: A Critical Appraisal" on arXiv (January) |
| 2018 | Debates [Yoshua Bengio](/wiki/yoshua_bengio) at the Montreal AI Debate (December 23) |
| 2019 | Co-founds [Robust.AI](/wiki/robust_ai); publishes "Rebooting AI" with [Ernest Davis](/wiki/ernest_davis) |
| 2022 | Launches "Marcus on AI" newsletter on [Substack](/wiki/substack); publishes "Deep Learning Is Hitting a Wall" |
| 2023 | Testifies at Senate Judiciary AI hearing (May 16) |
| 2024 | Publishes "Taming Silicon Valley" with MIT Press |

## What companies did Gary Marcus found?

### Geometric Intelligence and Uber AI Labs

In October 2014, Marcus co-founded [Geometric Intelligence](/wiki/geometric_intelligence) together with the Cambridge machine learning professor [Zoubin Ghahramani](/wiki/zoubin_ghahramani), the University of Central Florida computer scientist Kenneth Stanley, and the cognitive scientist Douglas Bemis. [4] The startup was based in New York with a research office in San Francisco. Its public technical claim was that it could train more accurate models from less data than mainstream deep learning approaches by using ideas from cognitive psychology and probabilistic programming. The company stayed in stealth for most of its existence and did not publish its core methods.

On December 5, 2016, Uber announced that it had acquired Geometric Intelligence to form a new research group called Uber AI Labs. [3] The acquisition figure was not disclosed publicly. Geometric Intelligence's roughly fifteen researchers, including Marcus, Ghahramani, Bemis, Kenneth Stanley, Jason Yosinski, and Jeff Clune, became the founding staff of [Uber AI Labs](/wiki/uber_ai_labs), which was based in San Francisco and reported to Uber's chief product officer Jeff Holden. [4] Marcus served as the lab's first director. He left the position in early 2017 after a few months, returning to academic and writing work, while many of the original researchers stayed at Uber for several more years and published widely cited work on neuroevolution, novelty search, and uncertainty estimation.

### Robust.AI

In 2019 Marcus co-founded [Robust.AI](/wiki/robust_ai) with the roboticist Rodney Brooks (a co-founder of iRobot and Rethink Robotics), Henrik Christensen, Mohamed Amer, and Anthony Jules. [5] The company is based in San Carlos, California and develops mobile robots for warehouses and material handling. Its first product, the Carter robot, is a collaborative pushcart that can operate autonomously, follow a human worker, or be pushed by hand, and is sold under a robot as a service (RaaS) model. [5]

Marcus served as chief executive officer at founding. In October 2020 the company announced that it had raised a total of $22.5 million, including a $15 million Series A led by Jazz Venture Partners with participation from Playground Global, Liquid 2, Fontinalis, and individual investors including Jaan Tallinn and Mark Leslie. [5] In late 2021 Marcus stepped down from the chief executive role; co-founder Anthony Jules became chief executive, and Marcus left the board the same year, remaining an investor and informal advisor. In April 2023 Robust.AI raised a further $20 million round (a Series A-1) led by Prime Movers Lab, with Future Ventures, Energy Impact Partners, Jazz Venture Partners, and Playground Global participating, bringing reported total funding to about $42.5 million. [5] In October 2024 the company launched an upgraded Carter Pro robot and has deployed Carter with DHL Supply Chain in the United States and Mexico. [5]

## Public commentary on AI

From roughly 2012 onward, Marcus published a sequence of essays and academic papers questioning whether deep neural networks were on a path to general intelligence. The first widely read example was "Is 'Deep Learning' a Revolution in Artificial Intelligence?," published by The New Yorker on November 25, 2012, in response to the ImageNet results that year. He argued that, while convolutional networks were impressive at perception, the new techniques did not address compositionality, causal reasoning, or common sense.

### What is "Deep Learning: A Critical Appraisal" (2018)?

On January 2, 2018, Marcus posted a paper on arXiv titled "Deep Learning: A Critical Appraisal" (arXiv:1801.00631). [11] It listed ten concerns about then current deep learning practice, including data hunger, shallow transfer to new tasks, lack of explicit world models, vulnerability to adversarial examples, and difficulty with hierarchical structure. [11] The paper concluded that "deep learning, which is fundamentally a technique for recognizing patterns, is at its best when all we need are rough-ready results," and argued that the field would need to be supplemented by other techniques to reach general intelligence. [11] It drew responses from researchers including [Yann LeCun](/wiki/yann_lecun), Thomas Dietterich, and Yoav Goldberg, and as of 2026 had accumulated more than 1,100 citations on Semantic Scholar, making it one of the most read non peer reviewed papers on the topic. [11]

### Bengio debate (Montreal, 2018)

On December 23, 2018, Marcus debated [Yoshua Bengio](/wiki/yoshua_bengio) at the inaugural "AI Debate" in Montreal, organized by Vincent Boucher's Montreal AI. [20] The motion concerned whether deep learning, with sufficient scale and engineering, could deliver human level cognition. Marcus argued for hybrid models incorporating prior knowledge and explicit symbolic structure; Bengio argued for further development of representation learning and self supervised methods. Both later said they agreed on more than the framing of the debate suggested, and they co-authored "AI Debate 2" with several other researchers in 2020.

### LeCun exchanges

Marcus and [Yann LeCun](/wiki/yann_lecun), Meta's chief AI scientist, have engaged in a long running public exchange on the limits of current networks. Their disagreements played out on Twitter (later X), in lectures, and in joint events such as a 2017 panel at NYU. LeCun has at various points said that Marcus's criticisms target a strawman of deep learning, while Marcus has said that LeCun has shifted his own position toward acknowledging the need for world models and energy based architectures. After 2022, both wrote about the need for systems with explicit planning components, with continuing disagreement about how much of this would emerge from learning alone.

### Critique of GPT models

With the release of GPT-2 in 2019 and [GPT-3](/wiki/gpt_3) in 2020, Marcus and Davis published a long article in MIT Technology Review ("GPT-3, Bloviator: OpenAI's language generator has no idea what it's talking about," August 22, 2020) that catalogued biological, physical, and social reasoning errors produced by GPT-3. [9] The piece included examples in which the system claimed it would take roughly two rainbows to jump from Hawaii to seventeen and similar nonsense. After [GPT-4](/wiki/gpt_4) launched in March 2023, Marcus published a follow up assessment on Substack arguing that, although GPT-4 was a real improvement, it still produced what he called confabulations or [hallucinations](/wiki/hallucination) at significant rates. He has continued to track and tabulate hallucination examples on "Marcus on AI."

### Why did Marcus say "deep learning is hitting a wall"?

In March 2022, Marcus and Davis published "Deep Learning Is Hitting a Wall" in Nautilus magazine (March 10, 2022), arguing that pure scaling would face diminishing returns and that hybrid systems would be needed. [10] The essay stated that the strategy of building larger systems with more chips and more data was "perhaps already approaching a point of diminishing returns" and that current systems did not "genuinely understand human language." [10] The article generated a sharp public exchange with researchers at OpenAI and DeepMind. In a separate essay in 2023, Marcus offered a public bet to [Elon Musk](/wiki/elon_musk) on whether Musk's prediction of artificial general intelligence by 2025 would be met; Musk did not formally accept the wager.

### What did Gary Marcus tell the Senate on May 16, 2023?

On May 16, 2023, Marcus testified before the U.S. Senate Judiciary Subcommittee on Privacy, Technology, and the Law in a hearing titled "Oversight of A.I.: Rules for Artificial Intelligence," chaired by Senator Richard Blumenthal of Connecticut. [7][8] The other witnesses were [Sam Altman](/wiki/sam_altman), chief executive of OpenAI, and [Christina Montgomery](/wiki/christina_montgomery), chief privacy and trust officer of IBM. [7] In his written testimony, Marcus warned that "we have built machines that are like bulls in a china shop, powerful, reckless, and difficult to control," and called for the creation of a federal agency to license and audit large AI systems, transparency requirements about training data and outputs, international coordination similar to the model of the International Atomic Energy Agency, and heightened responsibilities on developers when their models could plausibly be used to generate misinformation. [8] The hearing was widely covered, with the New York Times noting that all three witnesses, including Altman, agreed in principle that some form of licensing or oversight would be appropriate. [7] Marcus appeared again at follow up congressional events in 2023 and 2024, including the Schumer AI Insight Forums.

### Substack newsletter

Marcus launched the "Marcus on AI" newsletter on [Substack](/wiki/substack) in mid 2022, originally under the title "The Road to AI We Can Trust." [2] By late 2024 the newsletter had a reported readership in the tens of thousands of paid subscribers and several times that number of free subscribers, making it among the more widely read individual writers on artificial intelligence. The newsletter publishes near daily posts on hallucinations, hype cycles, regulation, and corporate disclosures. Marcus has used the platform to track failures of named systems including ChatGPT, Bard (later Gemini), Sydney (the Bing chat preview), and Sora.

## Books

| Year | Title | Publisher | Co-author |
|---|---|---|---|
| 2001 | The Algebraic Mind: Integrating Connectionism and Cognitive Science | MIT Press | (none) |
| 2004 | The Birth of the Mind: How a Tiny Number of Genes Creates the Complexities of Human Thought | Basic Books | (none) |
| 2008 | Kluge: The Haphazard Construction of the Human Mind | Houghton Mifflin | (none) |
| 2012 | Guitar Zero: The New Musician and the Science of Learning | Penguin Press | (none) |
| 2014 | The Future of the Brain (edited volume) | Princeton University Press | Jeremy Freeman (co-editor) |
| 2019 | Rebooting AI: Building Artificial Intelligence We Can Trust | Pantheon Books | [Ernest Davis](/wiki/ernest_davis) |
| 2024 | Taming Silicon Valley: How We Can Ensure That AI Works for Us | MIT Press | (none) |

### Rebooting AI (2019)

"Rebooting AI: Building Artificial Intelligence We Can Trust" was published by Pantheon Books on September 10, 2019. [15] Co-written with the NYU computer scientist Ernest Davis, the book argued that progress toward trustworthy AI would require deeper integration of common sense knowledge, causal models, and structured reasoning rather than continued scaling of pattern recognizers. The book proposed sixteen "preconditions for trust," including verifiability, traceability, robustness to distribution shift, and transparent reporting of failures. It received reviews in the New York Times, Wall Street Journal, and Nature, and was named one of seven "must read" books on AI by Forbes in 2019.

### Taming Silicon Valley (2024)

"Taming Silicon Valley: How We Can Ensure That AI Works for Us" was published by MIT Press in September 2024. [16] The book extended Marcus's regulatory arguments from the 2023 Senate hearing into a full proposal: an independent AI agency, mandatory pre-deployment evaluations for foundation models above a capability threshold, liability for downstream harms, and stronger consumer protection rules for AI generated content. Reviewers in the Financial Times, Science, and the Atlantic broadly agreed with the diagnosis while disagreeing on specific remedies. [21]

## Notable public debates

| Year | Topic | Counterpart(s) | Venue |
|---|---|---|---|
| 1990s | Past tense regularity (rules versus connectionism) | David Rumelhart, Jay McClelland, James Plunkett | Cognitive Science journals |
| 2012 | Whether deep learning revolutionizes AI | Various | The New Yorker |
| 2018 | Limits of deep learning | [Yoshua Bengio](/wiki/yoshua_bengio) | Montreal AI Debate, December 23 |
| 2019 to present | Symbolic versus statistical approaches | [Yann LeCun](/wiki/yann_lecun) | Twitter/X, conference panels |
| 2020 | GPT-3 capabilities | OpenAI researchers | MIT Technology Review |
| 2022 | Whether deep learning is hitting a wall | DeepMind, OpenAI | Nautilus, Substack |
| 2023 | AI policy and oversight | [Sam Altman](/wiki/sam_altman), [Christina Montgomery](/wiki/christina_montgomery) | U.S. Senate, May 16 |
| 2023 | AGI timelines | Elon Musk | Public Twitter wager |

## Scientific positions and arguments

### What does Gary Marcus believe about AI?

Marcus's central scientific position, repeated across decades of writing, is that progress toward robust artificial intelligence will require systems that combine the pattern recognition strengths of neural networks with the explicit variables, types, and rules of [symbolic AI](/wiki/symbolic_ai). He set out the technical case in "The Algebraic Mind" (2001) and updated it in a 2020 paper, "The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence" (arXiv:2002.06177), which proposed hybrid architectures, explicit cognitive models, formal world knowledge, and reasoning over those models as concrete research directions toward [artificial general intelligence](/wiki/agi). [12] Researchers including Henry Kautz, Artur d'Avila Garcez, and Luis Lamb have advanced overlapping versions of the [neurosymbolic AI](/wiki/neurosymbolic_ai) program in academic conferences such as NeurIPS and AAAI.

### Skepticism of pure scaling

Marcus has consistently questioned the proposition that increasing parameters, data, and compute alone will deliver general intelligence. In 2022 he predicted that the marginal returns to scale on benchmarks like MMLU would slow within a few model generations, and in 2024 he argued that the relative gap between successive frontier models was shrinking. [2] He has frequently noted that benchmark gains do not always transfer to real world reliability, and he has cited persistent failures in arithmetic, planning, and tracking the state of simple worlds as evidence.

### Hallucinations and reliability

A recurring theme in Marcus's writing is that large language models produce confident but incorrect outputs at rates that are difficult to predict and control. He has compiled examples involving citation fabrication, biographical errors about living people, faulty arithmetic, broken logic puzzles, and misidentification of widely known cultural facts. He has argued that retrieval, fine tuning, and reinforcement learning from human feedback reduce but do not eliminate this class of errors, and that the field needs better measurement of [hallucination](/wiki/hallucination) frequencies in deployed systems. He has called for product warnings and disclosure regimes analogous to nutrition labels.

### Misinformation and elections

Marcus has warned that generative models lower the cost of producing convincing fabricated text, audio, and video at scale. In 2023 and 2024 he wrote about election misinformation produced with generative tools, including a fabricated Joe Biden robocall in New Hampshire in January 2024 and the use of voice cloning in Slovak elections in 2023. He has urged platforms and AI developers to invest in provenance standards such as C2PA and to publish their content moderation results.

### Evaluation methodology

Marcus has promoted what he calls "adversarial" or "out of distribution" evaluation: testing models with examples slightly different from the training distribution, with novel compositions of familiar concepts, or with materials authored after the training cutoff. He has argued that benchmark scores from public test sets contaminate quickly through inclusion in training corpora, and he has cited cases in which a model that nominally scored well on the bar exam or AP tests performed poorly on closely related but unfamiliar prompts.

## Personal life

Marcus is married to Athena Vouloumanos, a developmental psychologist on the NYU faculty whose research examines infant speech perception. They have two children. The family lives in New York City. Marcus learned guitar in his late thirties and wrote about the experience in "Guitar Zero," published in 2012, which mixes memoir with research on adult learning, neural plasticity, and music cognition.

In personal essays Marcus has named cognitive scientists Steven Pinker and Susan Carey, philosopher Jerry Fodor, and computer scientist Douglas Hofstadter as influences on his thinking. He has cited Fodor and Zenon Pylyshyn's 1988 paper on connectionism and cognitive architecture as a key text for his own approach.

## Reception and influence

Reactions to Marcus's public role have been mixed. Supporters credit him with raising the profile of reliability problems in commercial AI systems and pushing the policy conversation toward concrete oversight. Critics, including some prominent deep learning researchers, have argued that his framing of debates is overly polarized and that hybrid approaches are already widespread in practice through tool use, retrieval augmentation, and program synthesis. Coverage in the New York Times (Cade Metz, March 2023), the Wall Street Journal (Christopher Mims, June 2023), and the Financial Times (John Thornhill, October 2024) has portrayed Marcus as a leading independent voice on AI policy, with these and other profiles often noting his willingness to make falsifiable predictions in public. [6][21]

Within cognitive science, his early work on language acquisition and rule learning is frequently cited in reviews of infant cognition, and the 1999 Science paper has been a standard reference for the abstract rule learning literature. [13]

## Selected publications

- Marcus, G. F. (1995). Children's overregularization of English plurals: A quantitative analysis. Journal of Child Language, 22(2), 447 to 459.
- Marcus, G. F., Vijayan, S., Bandi Rao, S., and Vishton, P. M. (1999). Rule learning by seven-month-old infants. Science, 283(5398), 77 to 80.
- Marcus, G. F. (2001). The Algebraic Mind: Integrating Connectionism and Cognitive Science. MIT Press.
- Marcus, G. F. (2018). Deep Learning: A Critical Appraisal. arXiv:1801.00631.
- Marcus, G. F., and Davis, E. (2019). Rebooting AI: Building Artificial Intelligence We Can Trust. Pantheon Books.
- Marcus, G. F. (2020). The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence. arXiv:2002.06177.
- Marcus, G. F. (2024). Taming Silicon Valley: How We Can Ensure That AI Works for Us. MIT Press.

## References

1. Gary Marcus faculty profile, Department of Psychology, New York University. https://www.psych.nyu.edu/marcus/
2. "Marcus on AI," Substack newsletter by Gary Marcus (originally "The Road to AI We Can Trust"). https://garymarcus.substack.com
3. "Uber acquires Geometric Intelligence to create Uber AI Labs." Uber Newsroom, December 5, 2016.
4. "NYU Incubated Start-Up Geometric Intelligence Acquired By Uber," NYU News, December 5, 2016; and Ingrid Lunden, "Uber acquires Geometric Intelligence to create an AI lab," TechCrunch, December 5, 2016.
5. "Robust.AI Raises $22.5 Million to Build the World's First Industrial-Grade Cognitive Engine for Robotics," BusinessWire, October 28, 2020; Brian Heater, "Robust.AI raises $20M as it scales robot deliveries for pilot customers," TechCrunch, April 20, 2023; "Robust.AI launches Carter Pro," October 2024.
6. Cade Metz, "How Could A.I. Destroy Humanity?," New York Times, June 10, 2023.
7. Cecilia Kang, "OpenAI's Sam Altman Urges A.I. Regulation in Senate Hearing," New York Times, May 16, 2023.
8. Senate Judiciary Subcommittee on Privacy, Technology, and the Law, Hearing on "Oversight of A.I.: Rules for Artificial Intelligence," May 16, 2023; written testimony of Gary Marcus. https://www.judiciary.senate.gov/imo/media/doc/2023-05-16%20-%20Testimony%20-%20Marcus.pdf
9. Marcus, G. and Davis, E., "GPT-3, Bloviator: OpenAI's language generator has no idea what it's talking about," MIT Technology Review, August 22, 2020.
10. Marcus, G. and Davis, E., "Deep Learning Is Hitting a Wall," Nautilus, March 10, 2022. https://nautil.us/deep-learning-is-hitting-a-wall-238440
11. Marcus, G., "Deep Learning: A Critical Appraisal," arXiv:1801.00631, January 2, 2018. https://arxiv.org/abs/1801.00631
12. Marcus, G., "The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence," arXiv:2002.06177, February 2020.
13. Marcus, G. F., Vijayan, S., Bandi Rao, S., and Vishton, P. M., "Rule learning by seven-month-old infants," Science 283:77 to 80, 1999.
14. Pinker, S. and Marcus, G., "Overregularization in Language Acquisition," Monographs of the Society for Research in Child Development, 57(4), 1992.
15. Marcus, G. and Davis, E., Rebooting AI: Building Artificial Intelligence We Can Trust, Pantheon Books, 2019. ISBN 978-1524748258.
16. Marcus, G., Taming Silicon Valley: How We Can Ensure That AI Works for Us, MIT Press, 2024. ISBN 978-0262551069.
17. Marcus, G., Kluge: The Haphazard Construction of the Human Mind, Houghton Mifflin, 2008. ISBN 978-0618879649.
18. Hampshire College, Distinguished Alumni Awards, news release.
19. Bertrand Russell Society, Award announcements (Marcus listed).
20. "AI Debate: Yoshua Bengio and Gary Marcus," Montreal AI, December 23, 2018.
21. John Thornhill, "What ChatGPT really is," Financial Times, October 2024 (review of Taming Silicon Valley).