AI & innovation articles
I contribute articles to Forbes on AI & ML, discussing important intersections with these technologies and society, listed below. See lavin.io/research for AI research publications and more.
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“Doing The Hard Things: AI, Space, and Climate Science”
This is not a time for an international arms race in AI. Rather, it is a time for collaboration across governments, corporate R&D, academia, and startups. It is a time to push the frontier of AI and space for climate science and humankind.
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“Machine Learning Is No Place To “Move Fast And Break Things”
Machine learning systems can be massively useful, but also fail critically in unforeseen ways. We need systems engineering in synergy with experimentation and innovation.
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“Healthcare Needs AI, AI Needs Causality”
Healthcare is ripe for the power of machine learning, but only with the scientific rigor of causal reasoning.
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“Interpreting AI Is More Than Black And White”
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?
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Causal reasoning is a necessary ingredient to human-level artificial intelligence. We’re not there, yet.
Also presentations and lectures, podcasts and panels, etc.
(Note this has not been updated since 2019)
On brains n bits, AI, and SW. For example,
- “Python for AI Research” on Talk Python to Me Podcast
- “Machine Learning in Medicine: Priorities for the Research to Product Gap” lecture at Cornell Medicine: video, slides
- “How The Brain is Inspiring AI” at Forbes 30 Under 30 summit
- “Accelerating Gaussian Processes and Deep Kernel Networks on GPUs” for NVIDIA GTC: video, slides
- Expert panel on AI ethics at the 2020 ICML Workshop on Challenges Deploying ML Systems.
- This 2016 TEDX talk at Cornell Tech on artifical and biological neurons:
