Institute of Integrative and Interdisciplinary Research

Artificial Intelligence Systems

This program investigates the structural, informational, and thermodynamic foundations of artificial intelligence systems. It examines how large-scale AI produces local entropy inversion through algorithmic compression, the thermodynamic costs of irreversible computation, the reality of emergent abilities in language models, information-theoretic principles underlying machine learning optimization, lessons from complexity science for effective AI governance, and the dynamics of the AI-centered infosphere. The framework connects formal models with practical implications for AI architecture, AI-human interaction, societal transformation, and governance.

The research questions below outline the current frontier problems pursued within this framework. The accompanying reference collection integrates key literature with ongoing contributions developed at the Institute.

Research Questions: Artificial Intelligence Systems

  • How do large-scale AI systems produce local entropy inversion through algorithmic compression?
  • What is the thermodynamic cost of computation with irreversibility and stochastic processes?
  • Are emergent abilities of large language models genuine or measurement artifacts?
  • What lessons from complex systems science apply to AI governance?
  • What is the relationship between information theory and machine learning optimization?
  • How is AI transforming the dynamics of human communication and norm formation?
  • What are the structural conditions for the emergence of a unified civilization of autonomous agents?
  • How does AI-mediated information convergence reshape the structure of the infosphere?
  • How can clinical discontinuity be addressed through AI-driven coherence restoration?

Publications

Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D. (2016). Concrete problems in AI safety. arXiv preprint arXiv:1606.06565. https://doi.org/10.48550/arXiv.1606.06565

Anderljung, M., Barnhart, J., Korber, A., Leung, J., O’Keefe, C., Whittlestone, J., … Heim, L. (2023). Frontier AI regulation: Managing emerging risks to public safety. arXiv preprint arXiv:2307.03718. https://doi.org/10.48550/arXiv.2307.03718

Bengio, Y., Hinton, G., & Yao, A. (2024). Managing extreme AI risks amid rapid progress. Science, 384(6698), 842–845. https://doi.org/10.1126/science.adn0117

Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arber, S., von Arx, S., … Liang, P. (2022). On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258. https://doi.org/10.48550/arXiv.2108.07258

Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., … Zhang, Y. (2023). Sparks of artificial general intelligence: Early experiments with GPT-4. arXiv preprint arXiv:2303.12712. https://doi.org/10.48550/arXiv.2303.12712

Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., … Vayena, E. (2021). An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Ethics, Governance, and Policies in Artificial Intelligence, 19–39. https://doi.org/10.1007/978-3-030-81907-1_3

Gershenson, C. (2025). Complexity, artificial life, and artificial intelligence. Artificial Life, 31(3), 289–303. https://doi.org/10.1162/artl_a_00462

Hendrycks, D., Mazeika, M., & Woodside, T. (2023). An overview of catastrophic AI risks. arXiv preprint arXiv:2306.12001. https://doi.org/10.48550/arXiv.2306.12001

Ilcic, A., Fuentes, M., & Lawler, J. (2025). Artificial intelligence, complexity, and systemic resilience in global governance. Frontiers in Artificial Intelligence, 8, 1562095. https://doi.org/10.3389/frai.2025.1562095

Jaradat, Y., Masoud, M., Manasrah, A., Alia, M., Suwais, K., & Almanasra, S. (2025). Exploring the intersection of information theory and machine learning. The International Arab Journal of Information Technology, 22(5), 845–858. https://doi.org/10.34028/iajit/22/5/1

Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., … Amodei, D. (2020). Scaling laws for neural language models. arXiv preprint arXiv:2001.08361. https://doi.org/10.48550/arXiv.2001.08361

Kriger, B. (2026). AI-Extended Agents and the Transformation of Human Communication: A Game-Theoretic Model of Norm Shift in Populations with AI-Mediated Communicators. Zenodo. https://doi.org/10.5281/zenodo.18521341

Kriger, B. (2026). Dynamics of information convergence: Empirical analysis of time density in the AI-centered infosphere. https://doi.org/10.5281/ZENODO.18256945

Kriger, B. (2026). Estimation of the contribution of biospheric and synthetic cognitive systems to the total effective complexity of the universe. https://doi.org/10.5281/ZENODO.18261863

Kriger, B. (2026). Local entropy inversion in large-scale AI systems: Thermodynamics of algorithmic compression. https://doi.org/10.5281/ZENODO.18262199

Kriger, B. (2026). The Inevitability of a Unified Civilization of Autonomous Agents: Why the Biological Basis of Subjecthood Becomes Irrelevant. Zenodo. https://doi.org/10.5281/zenodo.18512941

Kriger, B. (2026). The Stimulus Problem: A Formal Theory of Goal Generation in Post-Scarcity Information Environments. Zenodo. https://doi.org/10.5281/zenodo.18511908

Kriger, B., & Kriger, B. (2024). Clinical Discontinuity and AI: Restoring Coherence in Healthcare. https://doi.org/10.5281/zenodo.18395736

Manzano, G., Kardeş, G., Roldán, É., & Wolpert, D. H. (2024). Thermodynamics of computations with absolute irreversibility, unidirectional transitions, and stochastic computation times. Physical Review X, 14, 021026. https://doi.org/10.1103/PhysRevX.14.021026

Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187–192. https://doi.org/10.1126/science.adh2586

Russell, S. (2019). Human Compatible: Artificial Intelligence and the Problem of Control. Viking.

Schaeffer, R., Miranda, B., & Koyejo, O. (2023). Are emergent abilities of large language models a mirage? Advances in Neural Information Processing Systems, 36, 55565–55581.

Sepúlveda-Fontaine, S. A., & Amigó, J. M. (2024). Applications of entropy in data analysis and machine learning: A review. Entropy, 26(12), 1126. https://doi.org/10.3390/e26121126

Shur-Ofry, M., Abiri, E., Arbel-Raviv, R., Feder, M., Lavi, R., Sarfraz, M. U., & Tennenholtz, M. (2025). Lessons from complex systems science for AI governance. Patterns, 6(8), 101341. https://doi.org/10.1016/j.patter.2025.101341

Söderqvist, B., de Vries, H. P., & van der Vlist, F. (2024). Understanding the development of emerging complex intelligent systems. Journal of Engineering and Technology Management, 72, 101815. https://doi.org/10.1016/j.jengtecman.2024.101815

Wei, J., Tay, Y., Bommasani, R., Raffel, C., Zoph, B., Borgeaud, S., … Dean, J. (2022). Emergent abilities of large language models. Transactions on Machine Learning Research. https://doi.org/10.48550/arXiv.2206.07682

Wolpert, D. H. (2024). Is stochastic thermodynamics the key to understanding the energy costs of computation? Proceedings of the National Academy of Sciences, 121(45), e2321112121. https://doi.org/10.1073/pnas.2321112121

Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., Du, Y., Yang, C., Chen, Y., Chen, Z., Jiang, J., Ren, R., Li, Y., Tang, X., Liu, Z., … Wen, J.-R. (2023). A survey of large language models. arXiv preprint arXiv:2303.18223. https://doi.org/10.48550/arXiv.2303.18223