Prof. Alexander G. Ororbia is a researcher in the field of bio-inspired artificial intelligence, working on on mortal computation and neurobiologically-plausible learning algorithms. Ororbia takes us on a tour of brain-inspired AI, discussing how concepts like predictive coding, forward-only learning, and neural generative coding can lead to more efficient and adaptable AI systems.
He explores the how we might implement these bio-inspired approaches on neuromorphic hardware, and shares his vision for a future where AI systems are more closely aligned with biological intelligence.
TOC:
- Foundations of Bio-Inspired AI
[00:00:00] 1.1 Introduction to Bio-Inspired AI and Mortal Computation
[00:04:50] 1.2 Principles of Mortal Computation and Biomimetic AI
[00:17:41] 1.3 Markov Blankets and Free Energy Principle
[00:24:38] 1.4 MILLS Framework and Biological Systems - Alternative Learning Paradigms
[00:31:00] 2.1 Challenging Backpropagation: Overview of Alternatives
[00:31:49] 2.2 Predictive Coding and Free Energy Principle
[00:41:52] 2.3 Biologically Plausible Credit Assignment Methods
[00:50:11] 2.4 Taxonomy of Bio-inspired Learning Algorithms - Advanced Bio-Inspired AI Implementations
[00:59:30] 3.1 Forward-Only Learning and NGC Learn Implementation
[01:03:25] 3.2 Stability-Plasticity Dilemma and Bio-Inspired Solutions
[01:09:00] 3.3 Neuromorphic Hardware Landscape and Challenges
[01:12:58] 3.4 Neural Generative Coding and Predictive Coding Advancements
[01:20:36] 3.5 Latent Space Predictions in Forward-Only Learning
REFS:
The Levin Lab
https://drmichaellevin.org/
Mortal Computation: A Foundation for Biomimetic Intelligence
https://arxiv.org/pdf/2311.09589
The Forward-Forward Algorithm: Some Preliminary Investigations
https://arxiv.org/pdf/2212.13345
Good regulator
https://en.wikipedia.org/wiki/Good_re…
The free-energy principle: a rough guide to the brain?
https://www.fil.ion.ucl.ac.uk/~karl/T…
Hebbian theory
https://en.wikipedia.org/wiki/Hebbian…
There’s Plenty of Room Right Here
https://www.ncbi.nlm.nih.gov/pmc/arti…
Active Inference: The Free Energy Principle in Mind, Brain, and Behavior
https://direct.mit.edu/books/oa-monog…
Brain-Inspired Machine Intelligence: A Survey of Neurobiologically-Plausible Credit Assignment
https://arxiv.org/pdf/2312.09257
Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects
https://www.nature.com/articles/nn019…
Hopfield network
https://en.wikipedia.org/wiki/Hopfiel…
A Tutorial on Energy-Based Learning
https://yann.lecun.com/exdb/publis/pd…
A Learning Algorithm for Boltzmann Machines
https://www.cs.toronto.edu/~hinton/ab…
A Review of Neuroscience-Inspired Machine Learning
https://arxiv.org/pdf/2403.18929
Spiking neural predictive coding for continually learning from data streams
https://www.sciencedirect.com/science…
Neuroanatomy, Basal Ganglia
https://www.ncbi.nlm.nih.gov/books/NB…
Intel Loihi 2
https://www.intel.com/content/www/us/…
IBM TrueNorth
https://research.ibm.com/publications…
GC (Generative Coding)
https://www.researchgate.net/publicat…
NeuroEvolution of Augmenting Topologies (NEAT)
https://nn.cs.utexas.edu/downloads/pa…
Contrastive and Non-Contrastive Self-Supervised Learning Recover Global and Local Spectral Embedding Methods
https://arxiv.org/pdf/2205.11508
A Path Towards Autonomous Machine Intelligence (Yann LeCun)
https://openreview.net/pdf?id=BZ5a1r-…
Test-Time Model Adaptation with Only Forward Passes
https://arxiv.org/pdf/2404.01650v2
