Lenore Blum: AI Consciousness is Inevitable: The Conscious Turing Machine

Written By Will Millership

In this episode of Exploring Machine Consciousness, we speak to distinguished mathematician and computer scientist Lenore Blum about building the Conscious Turing Model and why she believes AI consciousness is inevitable.

Listen or watch this episode below; you can also find it on YouTube, Spotify, Apple Podcasts, or wherever you find your podcasts.

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*Lenore refers to a few slides in this episode; you can see them here. 

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Summary

When Alan Turing invented the theoretical Universal Turing Machine in 1936, he gave us a mathematical model of computation that would revolutionize our world. Now, nearly 90 years later, a husband-and-wife team of computer scientists are working to extend Turing's framework to solve an even deeper puzzle: consciousness itself.

What they're doing is building the Conscious Turing Machine (CTM), a mathematical model that extends Turing's original architecture with the specific computational structures they believe generate consciousness. Unlike researchers trying to determine whether existing AI systems are conscious, the Blums are taking a fundamentally different approach: use mathematics to define what consciousness is computationally, then build it.

Today's guest, distinguished mathematician and computer scientist Lenore Blum, explains why she and her husband Manuel believe machine consciousness isn't just possible, it's inevitable. Their reasoning? If consciousness is computational (and they're betting it is), and we can mathematically specify those computations, then we can build them. It's that simple, and that profound.

The Blums aren't alone in this mathematical assault on consciousness. As president of the Association for Mathematical Consciousness Science, Lenore is part of a growing movement that believes mathematics holds the key. "Mathematics provides a very useful perspective on how to understand the world's complexities," she explains. "You try to extract and simplify and look for fundamental principles."

When asked about whether current large language models like ChatGPT might be conscious, Lenore is skeptical but measured. These systems lack the key components her model identifies: they don't maintain persistent models of the world, they don't sleep or dream, and crucially, they don't have the kind of embodied experience that might ground genuine awareness. Her estimate? Current LLMs have "essentially zero" chance of being conscious.

But that doesn't mean machine consciousness is impossible. The Blums believe we could build conscious machines, we just need to include the right architectural features. And when we do, Lenore suggests we might not need a definitive test for consciousness. Like with humans and animals, we might have to rely on a battery of behavioral indicators and, ultimately, our best judgment.

In this conversation, host Will Millership and Callum Chace discuss with Lenore:

  • How the Conscious Turing Machine (CTM) draws from and extends the foundational ideas of Alan Turing's Universal Turing Machine.

  • Using mathematics to "extract and simplify" the complexities of consciousness, searching for the fundamental, formal principles that define it.

  • How the CTM acts as a high-level framework that aligns with the functionalities of competing theories like Global Workspace Theory and Integrated Information Theory (IIT).

  • Why the Blums believe that AI consciousness is "inevitable" and that this provides a functional "roadmap for a conscious AI".

  • The ethical implications of machine suffering, and why the phenomenon of "pain asymbolia" suggests a conscious AI must be able* *to suffer in order to function.

  • What lessons Alan Turing's original "imitation game" can offer us for creating a practical, real-world test for machine consciousness.

Selections from Lenore’s Work

  • Blum, L., & Blum, M. (2025). Blums’ Conscious Turing Machine.

  • Blum, L., & Blum,M. (2024). AI Consciousness is Inevitable: A Theoretical Computer Science Perspective. arXiv. https://arxiv.org/pdf/2403.17101

  • Blum, L., & Blum, M. (2023). A Theoretical Computer Science Perspective on Consciousness and Artificial General Intelligence.

  • Blum, L., & Blum, M. (2022). A theory of consciousness from a theoretical computer science perspective: Insights from the Conscious Turing Machine. PNAS, 119(21). https://doi.org/10.1073/pnas.21159341

Related Work

  • Arsiwalla, X. D., Kleiner, J., Tull, S., Resende, P., & Kremnitzer, K. (2023). Editorial: Mathematical and empirical foundations of models of consciousness. Frontiers in Applied Mathematics and Statistics, 9. https://doi.org/10.3389/fams.2023.1214939

  • Baars, B. (1997). In the Theater of Consciousness. Oxford University Press, New York, NY.

  • Baddeley, A. (2003). Working memory: looking back and looking forward. Nature Reviews Neuroscience, 4, 829–839. https://www.nature.com/articles/nrn1201

  • Block, N. (1995). On a confusion about a function of consciousness. Brain and Behavioral Sciences, 18(2), 227-247.

  • Chalmers, D. (1995a). Facing up to the problem of consciousness. Journal of Consciousness Studies, 2(3), 1995.

  • Cogitate, C., Ferrante, O., & Gorska-Klimowska, U. (2025). Adversarial testing of global neuronal workspace and integrated information theories of consciousness. Nature, 642, 133–142. https://doi.org/10.1038/s41586-025-08888-1

  • Dehaene, S. (2014). Consciousness and the Brain: Deciphering How the Brain Codes Our Thoughts. New York: Viking Press.

  • Dehaene, S., & Changeux, J. P. (2005). Ongoing Spontaneous Activity Controls Access to Consciousness: A Neuronal Model for Inattentional Blindness. PLoS Biology, 3(5). https://doi.org/10.1371/journal.pbio.0030141

  • Dennett, D. C. (1991). Consciousness Explained. Boston; Toronto; London: Little, Brown and Co.

  • Dennett, D. (2001). Are we explaining consciousness yet? Cognition, 79, 221-237.

  • Dennett, D. (2019). Welcome to strong illusionism. Journal of Consciousness Studies, 26(9–10).

  • Frankish, K. (2016a). Illusionism as a Theory of Consciousness. Journal of Consciousness Studies, 23(11-12), 11-39.

  • Gershman, S. J., Fiete, I., & Irie, K. (2025). Key-value memory in the brain. Neuron, 113(11), 1694-1707.e1. https://doi.org/10.1016/j.neuron.2025.02.029

  • Graziano, M. S., Guterstam, A., Bio, B., & Wilterson, A. (2020). Toward a standard model of consciousness: Reconciling the attention schema, global workspace, higher-order thought, and illusionist theories. Cognitive Neuropsychology, 37(3-4), 155-172.

  • Humphrey, N. (2023b). Sentience: The Invention of Consciousness. The MIT Press.

  • Kirkeby-Hinrup, A., et al. (2024). Evaluating the explanatory power of the Conscious Turing Machine.

  • Mitchell, K. (2023). What questions should a real theory of consciousness encompass? http://www.wiringthebrain.com/2023/09/what-questions-should-real-theory-of.html

  • Nagel, T. (1974). What Is It Like to Be a Bat? The Philosophical Review, 83(4), 435-450.

  • Seth, A. K. (2025). Conscious artificial intelligence and biological naturalism. Behavioral and Brain Sciences, 1-42. https://doi.org/10.1017/S0140525X25000032

  • Tononi, G., Boly, M., Massimini, M., & Koch, C. (2016). Integrated information theory: from consciousness to its physical substrate. Nature Reviews Neuroscience, 17, 450–461. https://doi.org/10.1038/nrn.2016.44

  • Turing, A. M. (1937). On Computable Numbers, with an Application to the Entscheidungsproblem. Proceedings of the London Mathematical Society, 2(42), 230-265. https://doi.org/10.1112/plms/s2-42.1.230

  • Turing, A. M. (1950). Computing Machinery and Intelligence. Mind, LIX(236), 433–460. https://doi.org/10.1093/mind/LIX.236.433

Will Millership
Next

Clara Colombatto: Perceptions of Consciousness, Intelligence, and Trust in Large Language Models