Autotelic Agents that Use and Ground Large Language Models (Oudeyer)

Pierre-Yves Oudeyer, Inria, Bordeaux, 21 mar 2024

ABSTRACT: Developmental AI aims to design and study artificial agents that are capable of open-ended learning. I will discuss two fundamental ingredients: (1) curiosity-driven exploration mechanisms, especially mechanisms enabling agents to invent and sample their own goals (such agents are called ‘autotelic’; (2) language and culture enabling enabling agents to learn from others’ discoveries, through the internalization of cognitive tools. I will discuss the main challenges in designing autotelic agents (e.g., how can they be creative in choosing their own goals?) and how some of them require language and culture to be addressed. I will also discuss using LLMs as proxies for human culture in autotelic agents, and how autotelic agents can leverage LLMs to learn faster, but also to align and ground them on the dynamics of the environment they interact with. I will also address some of the current main limitations of LLMs.

Pierre-Yves Oudeyer and his team at INRIA Bordeaux study open lifelong learning and the self-organization of behavioral, cognitive and language structures, at the frontiers of AI and cognitive science. In the field of developmental AI, we use machines as tools to better understand how children learn, and to study how machines could learn autonomously as children do and could integrate into human cultures. We study models of curiosity-driven autotelic learning, enabling humans and machines to set their own goals and self-organize their learning program. We also work on applications in education and assisted scientific discovery, using AI techniques to serve humans, and encourage learning, curiosity, exploration and creativity.

Colas, C; T Karch, C Moulin-Frier, PY Oudeyer (2022) Language and Culture Internalisation for Human-Like Autotelic AI Nature Machine Intelligence 4 (12), 1068-1076 https://arxiv.org/abs/2206.01134

Carta, T., Romac, C., Wolf, T., Lamprier, S., Sigaud, O., & Oudeyer, P. Y. (2023). Grounding large language models in interactive environments with online reinforcement learning. ICML https://arxiv.org/abs/2302.02662

Colas, C., Teodorescu, L., Oudeyer, P. Y., Yuan, X., & Côté, M. A. (2023). Augmenting Autotelic Agents with Large Language Models. arXiv preprint arXiv:2305.12487. https://arxiv.org/abs/2305.12487

The Grounding Problem in Language Models is not only about Grounding (Lenci)

Alessandro Lenci, Linguistics, U. Pisa, February 29, 2024

ABSTRACT: The Grounding Problem is typically assumed to concern the lack of referential competence of AI models. Language Models (LMs) that are trained only on texts without direct access to the external world are indeed rightly regarded to be affected by this limit, as they are ungrounded. On the other hand Multimodal LMs do have extralinguistic training data and show important abilities to link language with the visual world. In my talk, I will argue that incorporating multimodal data is a necessary but not sufficient condition to properly address the Grounding Problem. When applied to statistical models based on distributional co-occurrences like LMs, the Grounding Problem should be reformulated in a more extensive way, which sets an even higher challenge for current data-driven AI models.

Alessandro Lenci is Professor of linguistics and director of the Computational Linguistics Laboratory (CoLing Lab), University of Pisa. His main research interests are computational linguistics, natural language processing, semantics and cognitive science.

Lenci A., & Sahlgren (2023). Distributional Semantics, Cambridge, Cambridge University Press.

Lenci, A. (2018). Distributional models of word meaning. Annual review of Linguistics, 4, 151-171.

Lenci, A. (2023). Understanding Natural Language Understanding Systems. A Critical Analysis. Sistemi Intelligenti, arXiv preprint arXiv:2303.04229.

Lenci, A., & Padó, S. (2022). Perspectives for natural language processing between AI, linguistics and cognitive science. Frontiers in Artificial Intelligence, 5, 1059998.

What counts as understanding? (Lupyan)

Gary Lupyan, University of Wisconsin-Madison, Feb 22, 2024

ABSTRACT: The question of what it means to understand has taken on added urgency with the recent leaps in capabilities of generative AI such as large language models (LLMs). Can we really tell from observing the behavior of LLMs whether underlying the behavior is some notion of understanding? What kinds of successes are most indicative of understanding and what kinds of failures are most indicative of a failure to understand? If we applied the same standards to our own behavior, what might we conclude about the relationship between between understanding, knowing and doing?

Gary Lupyan is Professor of Psychology at the University of Wisconsin-Madison. His work has focused on how natural language scaffolds and augments human cognition, and attempts to answer the question of what the human mind would be like without language. He also studies the evolution of language, and the ways that language adapts to the needs of its learners and users.

References

Liu, E., & Lupyan, G. (2023). Cross-domain semantic alignment: Concrete concepts are more abstract than you think. Philosophical Transactions of the Royal Society B. DOI: 10.1098/rstb.2021-0372

Duan, Y., & Lupyan, G. (2023). Divergence in Word Meanings and its Consequence for Communication. In Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 45, No. 45)

van Dijk, B. M. A., Kouwenhoven, T., Spruit, M. R., & van Duijn, M. J. (2023). Large Language Models: The Need for Nuance in Current Debates and a Pragmatic Perspective on Understanding (arXiv:2310.19671). arXiv.

Aguera y Arcas, B. (2022). Do large language models understand us? Medium.

Titus, L. M. (2024). Does ChatGPT have semantic understanding? A problem with the statistics-of-occurrence strategy. Cognitive Systems Research, 83.

Pezzulo, G., Parr, T., Cisek, P., Clark, A., & Friston, K. (2024). Generating meaning: Active inference and the scope and limits of passive AI. Trends in Cognitive Sciences, 28(2), 97–112.

Language Writ Large: LLMs, ChatGPT, Meaning and Understanding (Harnad)

Stevan Harnad. UQÀM, McGill , 25 January 2004

VIDEO

ABSTRACT: Apart from what (little) OpenAI may be concealing from us, we all know (roughly) how ChatGPT works (its huge text database, its statistics, its vector representations, and their huge number of parameters, its next-word training, etc.). But none of us can say (hand on heart) that we are not surprised by what ChatGPT has proved to be able to do with these resources. It has even driven some of us to conclude that it actually understands. It’s not true that it understands. But it is also not true that we understand how it can do what it can do. I will suggest some hunches about benign “biases” — convergent constraints that emerge at LLM-scale that may be helping ChatGPT do so much better than we would have expected. These biases are inherent in the nature of language itself, at LLM-scale, and they are closely linked to what it is that ChatGPT lacks, which is direct sensorimotor grounding to integrate its words with their referents in the real world and to integrate its propositions with their meanings. These benign biases are related to (1) the parasitism of indirect verbal grounding on direct sensorimotor grounding, (2) the circularity of verbal definition, (3) the “mirroring” of language production and comprehension, (4) iconicity in propositions at LLM-scale, (5) computational counterparts of human “categorical perception” in category learning by neural nets, and perhaps also (6) a conjecture by Chomsky about the laws of thought.

Stevan Harnad is Professor of psychology and cognitive science at UQÀM. His research is on category-learning, symbol-grounding, language-evolution, and Turing-Testing

Bonnasse-Gahot, L., & Nadal, J. P. (2022). Categorical perception: a groundwork for deep learning. Neural Computation, 34(2), 437-475.

Harnad, S. (2024). Language Writ Large: LLMs, ChatGPT, Grounding, Meaning and Understanding. arXiv preprint arXiv:2402.02243.

Harnad, S. (2012). From sensorimotor categories and pantomime to grounded symbols and propositions In: Gibson, KR & Tallerman, M (eds.) The Oxford Handbook of Language Evolution 387-392.

Harnad, S. (2008) The Annotation Game: On Turing (1950) on Computing, Machinery, and Intelligence. In: Epstein, R, Roberts, Gary & Beber, G. (eds.) Parsing the Turing Test: Philosophical and Methodological Issues in the Quest for the Thinking Computer. Springer, pp. 23-66.

Thériault, C., Pérez-Gay, F., Rivas, D., & Harnad, S. (2018). Learning-induced categorical perception in a neural network model. arXiv preprint arXiv:1805.04567.

Vincent‐Lamarre, P; Blondin-Massé, A; Lopes, M; Lord, M; Marcotte, O; & Harnad, S (2016). The latent structure of dictionaries. Topics in Cognitive Science 8(3): 625-659.

Pérez-Gay Juárez, F., Sicotte, T., Thériault, C., & Harnad, S. (2019). Category learning can alter perception and its neural correlates. PloS one, 14(12), e0226000.

Mechanistic Explanation in Deep Learning (Millière)

Raphaël Millière, Philosophy, Macquarie University, 14 September, 2024

VIDEO

Abstract: Deep neural networks such as large language models (LLMs) have achieved impressive performance across almost every domain of natural language processing, but there remains substantial debate about which cognitive capabilities can be ascribed to these models. Drawing inspiration from mechanistic explanations in life sciences, the nascent field of “mechanistic interpretability” seeks to reverse-engineer human-interpretable features to explain how LLMs process information. This raises some questions: (1) Are causal claims about neural network components, based on coarse intervention methods (such as “activation patching”), genuine mechanistic explanations? (2) Does the focus on human-interpretable features risk imposing anthropomorphic assumptions? My answer will be “yes” to (1) and “no” to (2), closing with a discussion of some ongoing challenges.

Raphael Millière is Lecturer in Philosophy of Artificial Intelligence at Macquarie University in Sydney, Australia. His interests are in the philosophy of artificial intelligence, cognitive science, and mind, particularly in understanding artificial neural networks based on deep learning architectures such as Large Language Models. He has investigated syntactic knowledge, semantic competence, compositionality, variable binding, and grounding.

Elhage, N., et al. (2021). A mathematical framework for transformer circuits. Transformer Circuits Thread

Machamer, P., Darden, L., & Craver, C. F. (2000). Thinking about Mechanisms. Philosophy of Science, 67(1), 1–25.

Millière, R. (2023). The Alignment Problem in Context. arXiv preprint arXiv:2311.02147

Mollo, D. C., & Millière, R. (2023). The vector grounding problem. arXiv preprint arXiv:2304.01481

Yousefi, S., et al. (2023). In-Context Learning in Large Language Models: A Neuroscience-inspired Analysis of Representations. arXiv preprint arXiv:2310.00313.

Falsifying the Integrated Information Theory of Consciousness (Hanson)

Jake R Hanson, Sr. Data Scientist, Astrophysics, 07-Dec 2023

VIDEO

Abstract: Integrated Information Theory is a prominent theory of consciousness in contemporary neuroscience, based on the premise that feedback, quantified by a mathematical measure called Phi, corresponds to subjective experience. A straightforward application of the mathematical definition of Phi fails to produce a unique solution due to unresolved degeneracies inherent in the theory. This undermines nearly all published Phi values to date. In the mathematical relationship between feedback and input-output behavior in finite-state systems automata theory shows that feedback can always be disentangled from a system’s input-output behavior, resulting in Phi=0 for all possible input-output behaviors. This process, known as “unfolding,” can be accomplished without increasing the system’s size, leading to the conclusion that Phi measures something fundamentally disconnected from what could ground the theory experimentally. These findings demonstrate that IIT lacks a well-defined mathematical framework and may either be already falsified or inherently unfalsifiable according to scientific standards.

Jake Hanson is a Senior Data Scientist at a financial tech company in Salt Lake City, Utah. His doctoral research in Astrophysics from Arizona State University focused on the origin of life via the relationship between information processing and fundamental physics. He demonstrated that there were multiple foundational issues with IIT, ranging from poorly defined mathematics to problems with experimental falsifiability and pseudoscientific handling of core ideas.

Hanson, J.R., & Walker, S.I. (2019). Integrated information theory and isomorphic feed-forward philosophical zombies. Entropy, 21.11, 1073.

Hanson, J.R., & Walker, S.I. (2021). Formalizing falsification for theories of consciousness across computational hierarchies.Neuroscience of Consciousness, 2021.2, niab014.

Hanson, J.R., & Walker, S.I. (2021). Falsification of the Integrated Information Theory of Consciousness. Diss. Arizona State University, 2021.

Hanson, J.R., & Walker, S.I. (2023). On the non-uniqueness problem in Integrated Information Theory. Neuroscience of Consciousness, 2023.1, niad014.

LLMs, Patterns, and Understanding (Durt)

Christof Durt , Philosophy, U. Heidelberg, 30-Nov 2023

VIDEO

ABSTRACT: It is widely known that the performance of LLMs is contingent on their being trained with very large text corpora. But what in the text corpora allows LLMs to extract the parameters that enable them to produce text that sounds as if it had been written by an understanding being? In my presentation, I argue that the text corpora reflect not just “language” but language use. Language use is permeated with patterns, and the statistical contours of the patterns of written language use are modelled by LLMs. LLMs do not model understanding directly, but statistical patterns that correlate with patterns of language use. Although the recombination of statistical patterns does not require understanding, it enables the production of novel text that continues a prompt and conforms to patterns of language use, and thus can make sense to humans.

Christoph Durt is a philosophical and interdisciplinary researcher at Heidelberg university. He investigates the human mind and its relation to technology, especially AI. Going beyond the usual side-to-side comparison of artificial and human intelligence, he studies the multidimensional interplay between the two. This involves the study of human experience and language, as well as the relation between them. If you would like to join an international online exchange on these issues, please check the “courses and lectures” section on his website.

Durt, Christoph, Tom Froese, and Thomas Fuchs. preprint. “Against AI Understanding and Sentience: Large Language Models, Meaning, and the Patterns of Human Language Use.”

Durt, Christoph. 2023. “The Digital Transformation of Human Orientation: An Inquiry into the Dawn of a New Era” Winner of the $10.000 HFPO Essay Prize.

Durt, Christoph. 2022. “Artificial Intelligence and Its Integration into the Human Lifeworld.” In The Cambridge Handbook of Responsible Artificial Intelligence, Cambridge University Press.

Durt, Christoph. 2020. “The Computation of Bodily, Embodied, and Virtual Reality” Winner of the Essay Prize “What Can Corporality as a Constitutive Condition of Experience (Still) Mean in the Digital Age?”Phänomenologische Forschungen, no. 2: 25–39.

LLMs: Indication or Representation? (Søgaard)

Anders Søgaard , Computer Science & Philosophy, U. Copenhagen, 23-Nov 2023

VIDEO

ABSTRACT: People talk to LLMs – their new assistants, tutors, or partners – about the world they live in, but are LLMs parroting, or do they (also) have internal representations of the world? There are five popular views, it seems:

LLMs are all syntax, no semantics.
LLMs have inferential semantics, no referential semantics.
LLMs (also) have referential semantics through picturing
LLMs (also) have referential semantics through causal chains.
Only chatbots have referential semantics (through causal chains)

I present three sets of experiments to suggest LLMs induce inferential and referential semantics and do so by inducing human-like representations, lending some support to view (iii). I briefly compare the representations that seem to fall out of these experiments to the representations to which others have appealed in the past.

Anders Søgaard is University Professor of Computer Science and Philosophy and leads the newly established Center for Philosophy of Artificial Intelligence at the University of Copenhagen. Known primarily for work on multilingual NLP, multi-task learning, and using cognitive and behavioral data to bias NLP models, Søgaard is an ERC Starting Grant and Google Focused Research Award recipient and the author of Semi-Supervised Learning and Domain Adaptation for NLP (2013), Cross-Lingual Word Embeddings (2019), and Explainable Natural Language Processing (2021).

Søgaard, A. (2023). Grounding the Vector Space of an Octopus. Minds and Machines 33, 33-54.

Li, J.; et al. (2023) Large Language Models Converge on Brain-Like Representations. arXiv preprint arXiv:2306.01930

Abdou, M.; et al. (2021) Can Language Models Encode Perceptual Structure Without Grounding? CoNLL

Garneau, N.; et al. (2021) Analogy Training Multilingual Encoders. AAAI

« Algorithmes de Deep Learning flous causaux » (Faghihi)

Usef Faghihi , Informatique, UQTR, 16-Nov 2023

RÉSUMÉ : Je donnerai un bref aperçu de l’inférence causale et de la manière dont les règles de la logique floue peuvent améliorer le raisonnement causal (Faghihi, Robert, Poirier & Barkaoui, 2020). Ensuite, j’expliquerai comment nous avons intégré des règles de logique floue avec des algorithmes d’apprentissage profond, tels que l’architecture de transformateur Big Bird (Zaheer et al., 2020). Je montrerai comment notre modèle de causalité d’apprentissage profond flou a surpassé ChatGPT sur différentes bases de données dans des tâches de raisonnement (Kalantarpour, Faghihi, Khelifi & Roucaut, 2023). Je présenterai également quelques applications de notre modèle dans des domaines tels que la santé et l’industrie. Enfin, si le temps le permet, je présenterai deux éléments essentiels de notre modèle de raisonnement causal que nous avons récemment développés : l’Effet Causal Variationnel Facile Probabiliste (PEACE) et l’Effet Causal Variationnel Probabiliste (PACE) (Faghihi & Saki, 2023).

Usef Faghihi est professeur adjoint à l’Université du Québec à Trois-Rivières. Auparavant, Usef était professeur à l’Université d’Indianapolis aux États-Unis. Usef a obtenu son doctorat en Informatique Cognitive à l’UQAM. Il est ensuite allé à Memphis, aux États-Unis, pour effectuer un post-doctorat avec le professeur Stan Franklin, l’un des pionniers de l’intelligence artificielle. Ses centres d’intérêt en recherche sont les architectures cognitives et leur intégration avec les algorithmes d’apprentissage profond.

Faghihi, U., Robert, S., Poirier, P., & Barkaoui, Y. (2020). From Association to Reasoning, an Alternative to Pearl’s Causal Reasoning. In Proceedings of AAAI-FLAIRS 2020. North-Miami-Beach (Florida).

Faghihi, U., & Saki, A. (2023). Probabilistic Variational Causal Effect as A new Theory for Causal Reasoning. arXiv preprint arXiv:2208.06269.

Kalantarpour, C., Faghihi, U., Khelifi, E., & Roucaut, F.-X. (2023). Clinical Grade Prediction of Therapeutic Dosage for Electroconvulsive Therapy (ECT) Based on Patient’s Pre-Ictal EEG Using Fuzzy Causal Transformers. Paper presented at the International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023, Tenerife, Canary Islands, Spain.

Zaheer, M., Guruganesh, G., Dubey, K. A., Ainslie, J., Alberti, C., Ontanon, S., . . . Yang, L. (2020). Big bird: Transformers for longer sequences. Advances in neural information processing systems, 33, 17283-17297.

Robotic Grounding and LLMs: Advancements and Challenges (Kennington)

Casey Kennington , Computer Science, Boise State, 09-Nov 2023

VIDEO

ABSTRACT: Large Language Models (LLMs) are p rimarily trained using large amounts of text, but there have also been noteworthy advancements in incorporating vision and other sensory information into LLMs. Does that mean LLMs are ready for embodied agents such as robots? While there have been important advancements, technical and theoretical challenges remain including use of closed language models like ChatGPT, model size requirements, data size requirements, speed requirements, representing the physical world, and updating the model with information about the world in real time. In this talk, I explain recent advance on incorporating LLMs into robot platforms, challenges, and opportunities for future work.

Casey Kennington is associate professor in the Department of Computer Science at Boise State University where he does research on spoken dialogue systems on embodied platforms. His long-term research goal is to understand what it means for humans to understand, represent, and produce language. His National Science Foundation CAREER award focuses on enriching small language models with multimodal information such as vision and emotion for interactive learning on robotic platforms. Kennington obtained his PhD in Linguistics from Bielefeld University, Germany.

Josue Torres-Foncesca, Catherine Henry, Casey Kennington. Symbol and Communicative Grounding through Object Permanence with a Mobile Robot. In Proceedings of SigDial, 2022.

Clayton Fields and Casey Kennington. Vision Language Transformers: A Survey. arXiv, 2023.

Casey Kennington. Enriching Language Models with Visually-grounded Word Vectors and the Lancaster Sensorimotor Norms. In Proceedings of CoNLL, 2021

Casey Kennington. On the Computational Modeling of Meaning: Embodied Cognition Intertwined with Emotion. arXiv, 2023.