Today we’re excited to kick off our coverage of the 2022 NeurIPS conference with Johann Brehmer, a research scientist at Qualcomm AI Research in Amsterdam. We begin our conversation discussing some of the broader problems that causality will help us solve, before turning our focus to Johann’s paper Weakly supervised causal representation learning, which seeks to prove that high-level causal representations are identifiable in weakly supervised settings. We also discuss a few other papers that the team at Qualcomm presented, including neural topological ordering for computation graphs, as well as some of the demos they showcased, which we’ll link to on the show notes page.
The complete show notes for this episode can be found at twimlai.com/go/605.
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AI’s Legal and Ethical Implications with Sandra Wachter - #521
Compositional ML and the Future of Software Development with Dillon Erb - #520
Generating SQL Database Queries from Natural Language with Yanshuai Cao - #519
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Deep Reinforcement Learning for Game Testing at EA with Konrad Tollmar - #517
Exploring AI 2041 with Kai-Fu Lee - #516
Advancing Robotic Brains and Bodies with Daniela Rus - #515
Neural Synthesis of Binaural Speech From Mono Audio with Alexander Richard - #514
Using Brain Imaging to Improve Neural Networks with Alona Fyshe - #513
Adaptivity in Machine Learning with Samory Kpotufe - #512
A Social Scientist’s Perspective on AI with Eric Rice - #511
Applications of Variational Autoencoders and Bayesian Optimization with José Miguel Hernández Lobato - #510
Codex, OpenAI’s Automated Code Generation API with Greg Brockman - #509
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