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How to Create Generalizable AI?
Anima Anandkumar, NVIDIA
Current deep-learning benchmarks focus on generalization on the same distribution as the training data. However, real-world applications require generalization to new, unseen scenarios, domains, and tasks. I'll present key ingredients that I believe are critical towards achieving this, including (1) compositional systems that have modular and interpretable components; (2) unsupervised learning to discover new concepts; (3) feedback mechanisms for robust inference; and (4) causal discovery and inference that capture underlying relationships and invariances. Domain knowledge and structure can help enable learning in these challenging settings. My talk is beginner-friendly and will give a high-level overview of these challenges.