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.

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.

« 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.

From the History of Philosophy to AI: Does Thinking Require Sensing? (Chalmers)

David Chalmers , Center for Mind, Brain & Consciousness, NYU, 28-Sep 2023

VIDEO

ABSTRACT: There has recently been widespread discussion of whether large language models might be sentient or conscious. Should we take this idea seriously? I will discuss the underlying issue and will break down the strongest reasons for and against. I suggest that given mainstream assumptions in the science of consciousness, there are significant obstacles to consciousness in current models: for example, their lack of recurrent processing, a global workspace, and unified agency. At the same time, it is quite possible that these obstacles will be overcome in the next decade or so. I conclude that while it is somewhat unlikely that current large language models are conscious, we should take seriously the possibility that extensions and successors to large language models may be conscious in the not-too-distant future.

David Chalmers is University Professor of Philosophy and Neural Science and co-director of the Center for Mind, Brain, and Consciousness at New York University. He is the author of The Conscious Mind (1996), Constructing The World (2010), and Reality+: Virtual Worlds and the Problems of Philosophy (2022). He is known for formulating the “hard problem” of consciousness, and (with Andy Clark) for the idea of the “extended mind,” according to which the tools we use can become parts of our minds.

Chalmers, D. J. (2023). Could a large language model be conscious?. arXiv preprint arXiv:2303.07103.

Chalmers, D.J. (2022) Reality+: Virtual worlds and the problems of philosophy. Penguin

Chalmers, D. J. (1995). Facing up to the problem of consciousness. Journal of Consciousness Studies, 2(3), 200-219.

Clark, A., & Chalmers, D. (1998). The extended mind. Analysis, 58(1), 7-19.

Grounding in Large Language Models: Functional Ontologies for AI (Mollo)

Dimitri Coelho Mollo. Philosophy of AI, Umeå University, 21 sept 2023

VIDEO

ABSTRACT: I will describe joint work with Raphaël Millière, arguing that language grounding (but not language understanding) is possible in some current Large Language Models (LLMs). This does not mean, h that the way language grounding works in LLMs is similar to how grounding works in humans. The differences open up two options: narrowing the notion of grounding to only the phenomenon in humans; or pluralism about grounding, extending the notion more broadly to systems that fulfil the requirements for intrinsic content. Pluralism invites applying recent work in comparative and cognitive psychology to AI, especially the search for appropriate ontologies to account for cognition and intelligence. This can help us better understand the capabilities and limitations of current AI systems, as well as potential ways forward.

Dimitri Coelho Mollo is Assistant Professor with focus in Philosophy of Artificial Intelligence at the Department of Historical, Philosophical and Religious Studies, at Umeå University, Sweden, and focus area coordinator at TAIGA (Centre for Transdisciplinary AI), for the area ‘Understanding and Explaining Artificial Intelligence’. I am also an external Principal Investigator at the Science of Intelligence Cluster, in Berlin, Germany. My research focuses on foundational and epistemic questions within artificial intelligence and cognitive science, looking for ways to improve our understanding of mind, cognition, and intelligence in biological and artificial systems. My work often intersects issues in Ethics of Artificial Intelligence, Philosophy of Computing, and Philosophy of Biology.

Coelho Mollo and Millière (2023), The Vector Grounding Problem

Francken, Slors, Craver (2022), Cognitive ontology and the search for neural mechanisms: three foundational problems

Conscious processing, inductive biases and generalization in deep learning (Bengio)

Yoshua Bengio, MILA, Université de Montréal 17 feb 2023

VIDEO

Abstract: Humans are very good at “out-of-distribution” generalization (compared to current AI systems). It would be useful to determine the inductive biases they exploit and translate them into machine-language architectures, training frameworks and experiments. I will discuss several of these hypothesized inductive biases. Many exploit notions in causality and connect abstractions in representation learning (perception and interpretation) with reinforcement learning (abstract actions). Systematic generalizations may arise from efficient factorization of knowledge into recomposable pieces. This is partly related to symbolic AI (aas seen in the errors and limitations of reasoning in humans, as well as in our ability to learn to do this at scale, with distributed representations and efficient search). Sparsity of the causal graph and locality of interventions — observable in the structure of sentences — may reduce the computational complexity of both inference (including planning) and learning. This may be why evolution incorporated this as “consciousness.” I will also suggest some open research questions to stimulate further research and collaborations.

Yoshua Bengio, Professor, University of Montreal, founder and scientific director of Mila – Institut québécois d’AI, and co-director CIFAR’s Machine Learning, Biological Learning program as a Senior Fellow. He also serves as scientific director of IVADO.

Butlin, P., Long, R., Elmoznino, E., Bengio, Y., Birch, J., Constant, A., … & VanRullen, R. (2023). Consciousness in artificial intelligence: insights from the science of consciousness. arXiv preprint arXiv:2308.08708.

Zador, A., Escola, S., Richards, B., Ölveczky, B., Bengio, Y., Boahen, K., … & Tsao, D. (2023). Catalyzing next-generation artificial intelligence through neuroai. Nature Communications, 14(1), 1597.