Recursive Cortical Network
George, Lavin, et al. Cortical Microcircuits from a Generative Vision Model. CCN 2018. /// paper
Hierarchical Temporal Memory
Hawkins, Lavin, et al. Biological and Machine Intelligence. 2016. /// book
Data-efficient computer vision
Naud & Lavin (2020), Manifolds for Unsupervised Visual Anomaly Detection. /// paper
Lavin (2020), Probabilistic Programmed Causal Inference.
Lavin & Renard. Technology Readiness Levels for Machine Learning Systems. ICML 2020 Workshop on Challenges in Deploying ML Systems. /// paper
Haney & Lavin (2020). Probabilistic Domain-Expert Hyperspherical Networks.
Lavin (2019), Predictive Modeling Neurodegenerative Processes with a Domain-Specific Probabilistic Programming Language.
Lavin. Integration of human-like covert-overt attention with probabilistic neural networks. Neuroscience to Artificially Intelligent Systems (NAISys) 2020. /// abs
AI & ML articles
I contribute articles to Forbes on AI & ML, discussing important intersections with these technologies and society.
Machine learning systems can be massively useful, but also fail critically in unforeseen ways. We need systems engineering in synergy with experimentation and innovation.
Healthcare is ripe for the power of machine learning, but only with the scientific rigor of causal reasoning.
Black-box AI can be extremely powerful yet difficult to understand and trust. White-box AI is explainable and insightful, but sometimes at the cost of predictive power. How do we mend the gap?
Causal reasoning is a necessary ingredient to human-level artificial intelligence. We’re not there, yet.
On brains n bits, AI, and SW. For example,
- “Python for AI Research” on Talk Python to Me Podcast
- “How The Brain is Inspiring AI” at Forbes Under 30 summit