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Explore novel programming constructs for probabilistic AI that automate and hide complex mathematical and numerical details in this conference talk from Strange Loop. Learn about a new conceptual framework that replaces arcane mathematical objects with more accessible code, enabling programmers without advanced mathematical training to engage in state-of-the-art AI programming. Discover how to write stochastic simulators that produce imaginary data sets and create metaprograms to analyze simulator code alongside real-world data, inverting the simulator to infer explanatory events. Witness practical applications using Gen, a general-purpose probabilistic programming system, including inferring 3D structure from images and finding hidden compositional structure in time series data for improved forecasts. Gain insights from Marco Cusumano-Towner, creator of Gen and PhD student at MIT, as he demonstrates how these constructs make powerful AI approaches more feasible and accessible.
