TALKS

Invited Talks & Workshops
  • (2026) Cultural contributions of science and impacts of AI on science evaluation, Cultural AI Conference, NYU Digital Theory Lab, NYU, NYC, NY.
  • (2025) AI for Peer Review & the Future of Science, Philosophy Sees the Algorithm: Reconsidering Knowledge and Community in AI-Based Science, The Ohio State University, Columbus, OH.
  • (2025) Invited Keynote Presentation: What could an AI ethics be? An epistemological proposal, Conference on Philosophy of Computing, Universidad Nacional Autónoma de México, Mexico City, MX.
  • (2025) Representation in Brains & Neural Networks, Johns Hopkins University, Baltimore, MD.
  • (2024) Empirical Grounding in (Neuro)scientific ML, Philosophy and Psychology of Generalization and Creativity Group, Simons Institute for the Theory of Computing, Berkeley, CA.
  • (2024) Responsible AI is Sound Science, Workshop on Causation, Complexity, Cognition, and Representation for Responsible AI, Google & the Santa Fe Institute (SFI).
  • (2023) Machine Learning & The Theory-Free Ideal, Workshop on Philosophy of AI in Science, Cambridge University, Cambridge, UK.
  • (2023) Panelist, AI Alignment and Artificial Life, ALIFE 2023 (Conference on Artificial Life), Sapporo, JP.
  • (2023) The Devil in the Data: Assessing the Atheoreticity of Scientific Machine Learning, Foundations of Computation Workshop, Department of Philosophy, Australian National University, Canberra, AU.
  • (2022) Workshop on the Free Energy Principle as Model Structure or Model Template, Universität Wien, Vienna, AT.
  • (2022) Workshop on metascience and theorising, Leiden, NL.
  • (2022) Reification in ML & the FEP, “The Free Energy Principle: Science, Tech and Philosophy” Conference, Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, DE.
  • (2021) Keynote: Recognising & Rectifying Reification: Machine Learning & Model-Target Misidentification, COGNITIO 2021, Université du Québec à Montréal, Québec, CA.

Refereed Talks & Workshops
  • (2024) Andrews & Smart, Militarising ML: Functionality & Harm, Harms and Risks of AI in the Military, Mila - Quebec AI Institute, Montréal, CA.
  • (2024) Andrews & Curtis-Trudel, Machine learning, cognitive prosthetics, and the future of science, Philosophy of Science: Past, Present and Future, Minnesota Center for Philosophy of Science, Minneapolis, MN.
  • (2021) Assessing the FEP in Scientific Practice, 5th International Conference on Interactivity, Language & Cognition, Warsaw, PL.
  • (2021) Machine learning in Scientific Practice: Normative & Descriptive Aims, CUNY Graduate Center Graduate Conference on Artificial Intelligence, New York, NY.
  • (2021) Machine Learning & the scientific method: the case of the Free Energy Principle, Digital Studies of Digital Science, Université Catholique de Louvain, Louvain-la-Neuve, BE.
  • (2018) A Theory of Representation with Error in Deacon & Bickhard, Peripatetic Conference for Cognitive Systems Modeling, Małe Ciche, PL.
  • (2018) Mind the (Informational) Gap: Mind, Machine, & the Space in Between, Workshop on Machine Learning and Explanation in Cognitive Science, Czech Academy of Sciences, Prague, CZ.
  • (2018) Life-mind (dis-)continuities: bridging biological selfhood and biosemiosis, 18th Annual Biosemiotics Gathering, Berkeley, CA.
  • (2018) On the subject of evolution: towards a biological basis of subjectivity, selfhood, and agency, The Science of Consciousness Conference, Tucson, AZ.
  • (2018) Adapting evolution: complexity & culture within a universal Darwinian framework, The Generalized Theory of Evolution Conference, Düsseldorf, DE.