Biologically-inspired AI and Mortal Computation

MLST is sponsored by Tufa Labs: Are you interested in working on ARC and cutting-edge AI research with the MindsAI team (current ARC winners)? Focus: ARC, LLMs, test-time-compute, active inference, system2 reasoning, and more. Future plans: Expanding to complex environments like Warcraft 2 and Starcraft 2. Interested? Apply for an ML research position: benjamin@tufa.ai Professor Alexander Ororbia from the Rochester Institute of Technology takes Tim Scarfe through the case for bio-inspired AI. The central idea is mortal computation: you cannot divorce the software from the hardware that runs it. The brain manages remarkable things on a few watts because its computations are entangled with its physical substrate. GPT-class models, running on von Neumann architectures designed for immortal computation -- where software and hardware are deliberately decoupled -- pay a staggering energy penalty for that separation. Ororbia explains the building blocks: Markov blankets as the formalism for system boundaries, Karl Friston's free energy principle as the optimization target, and the MILLS framework (Mortal Inference, Learning, and Selection) operating across multiple timescales. He then surveys the landscape of alternatives to backpropagation -- predictive coding, Hebbian learning, contrastive methods, and Geoff Hinton's forward-forward algorithm -- showing how each maps to observations from neuroscience. The conversation gets practical with Ororbia's ngc-learn library for implementing these algorithms, the stability-plasticity dilemma in continual learning, and the current state of neuromorphic hardware from Intel Loihi to IBM TrueNorth. He closes with his neural generative coding work, which showed that predictive coding networks can synthesize data they were never trained on -- outperforming VAEs and GANs -- and his vision for bio-inspired AI systems that coexist with humanity rather than replacing it. --- TIMESTAMPS: 00:00:00 Introduction to Bio-Inspired AI and Mortal Computation 00:04:50 Principles of Mortal Computation 00:17:41 Markov Blankets and Free Energy Principle 00:24:38 MILLS Framework and Biological Systems 00:31:00 Challenging Backpropagation: Alternative Approaches 00:31:49 Predictive Coding and Free Energy Principle 00:41:52 Biologically Plausible Credit Assignment Methods 00:50:11 Taxonomy of Bio-inspired Learning Algorithms 00:59:30 Forward-Only Learning and ngc-learn Implementation 01:03:25 Stability-Plasticity Dilemma and Continual Learning 01:09:00 Neuromorphic Hardware and Challenges 01:12:58 Neural Generative Coding and Future Directions --- REFERENCES: website: [00:04:43] The Levin Lab https://drmichaellevin.org/ [00:18:20] Good Regulator Theorem https://en.wikipedia.org/wiki/Good_regulator [00:41:52] Hebbian Theory https://en.wikipedia.org/wiki/Hebbian_theory [00:45:00] Hopfield Network https://en.wikipedia.org/wiki/Hopfield_network [01:09:00] Intel Loihi 2 https://www.intel.com/content/www/us/en/research/neuromorphic-computing-loihi-2-technology-brief.html paper: [00:04:50] Mortal Computation: A Foundation for Biomimetic Intelligence https://arxiv.org/abs/2311.09589 [00:06:53] The Forward-Forward Algorithm https://arxiv.org/abs/2212.13345 [00:07:20] There's Plenty of Room Right Here https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10046700/ [00:17:41] The Free-Energy Principle: A Rough Guide to the Brain https://www.fil.ion.ucl.ac.uk/~karl/The%20free-energy%20principle%20-%20a%20rough%20guide%20to%20the%20brain.pdf [00:31:49] Predictive Coding in the Visual Cortex https://www.nature.com/articles/nn0199_79 [00:41:52] Brain-Inspired Machine Intelligence: Neurobiologically-Plausible Credit Assignment https://arxiv.org/abs/2312.09257 [00:45:50] A Tutorial on Energy-Based Learning https://yann.lecun.com/exdb/publis/pdf/lecun-06.pdf [00:46:40] A Learning Algorithm for Boltzmann Machines https://www.cs.toronto.edu/~hinton/absps/cogscibm.pdf [00:50:11] A Review of Neuroscience-Inspired Machine Learning https://arxiv.org/abs/2403.18929 [00:53:20] NEAT: NeuroEvolution of Augmenting Topologies https://nn.cs.utexas.edu/downloads/papers/stanley.ec02.pdf [00:56:40] A Path Towards Autonomous Machine Intelligence https://openreview.net/pdf?id=BZ5a1r-kVsf [00:59:30] Test-Time Model Adaptation with Only Forward Passes https://arxiv.org/abs/2404.01650 [01:03:25] Spiking Neural Predictive Coding for Continual Learning https://www.sciencedirect.com/science/article/pii/S0925231223004150 [01:10:00] IBM TrueNorth https://research.ibm.com/publications/truenorth-design-and-tool-flow-of-a-65-mw-1-million-neuron-programmable-neurosynaptic-chip book: [00:24:38] Active Inference: The Free Energy Principle in Mind, Brain, and Behavior https://direct.mit.edu/books/oa-monograph/5299/Active-InferenceThe-Free-Energy-Principle-in-Mind --- LINKS: Full Transcript: https://app.rescript.info/share/cfa14d0f39f86d035d5caf6173d6207f Download PDF transcript: https://app.rescript.info/api/public/sessions/94d5c32d7c507e6a/pdf