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Thread: Miscellaneous

  1. #51

    The progress we've made in machine learning - Tom Dietterich

    Published on Oct 31, 2017

    The National Academies of Sciences, Engineering, and Medicine organized a two-day workshop on the capabilities and applications of artificial intelligence and machine learning for the intelligence community on August 9-10, 2017.

  2. #52

    Developing bug-free machine learning systems using formal mathematics

    Published on Nov 5, 2017

    Noisy data, non-convex objectives, model misspecification, and numerical instability can all cause undesired behaviors in machine learning systems. As a result, detecting actual implementation errors can be extremely difficult. We demonstrate a methodology in which developers use an interactive proof assistant to both implement their system and to state a formal theorem defining what it means for their system to be correct. The process of proving this theorem interactively in the proof assistant exposes all implementation errors since any error in the program would cause the proof to fail. As a case study, we implement a new system, Certigrad, for optimizing over stochastic computation graphs, and we generate a formal (i.e. machine-checkable) proof that the gradients sampled by the system are unbiased estimates of the true mathematical gradients. We train a variational autoencoder using Certigrad and find the performance comparable to training the same model in TensorFlow.

  3. #53

    Probabilistic Machine Learning - Prof. Zoubin Ghahramani

    Published on Nov 12, 2017

    Zoubin Ghahramani is Professor of Information Engineering at the University of Cambridge, Co-Director of Uber AI Labs, and the Cambridge Director of the Alan Turing Institute, the UK's national institute for Data Science.

    He is also the Deputy Academic Director of the Leverhulme Centre for the Future of Intelligence. He has worked and studied at the University of Pennsylvania, MIT, the University of Toronto, the Gatsby Unit at UCL, and CMU.

    His research spans Neuroscience, AI, Machine Learning and Statistics. In 2015 he was elected a Fellow of the Royal Society.

    Recorded, 7th March 2017

  4. #54

    Machine learning - a new programming paradigm

    Published on Jun 4, 2018

    In this video from RedHat Summit 2018, Cassie Kozyrkov demystifies machine learning and AI. She describes how they're simply a different way to program computers, letting you explain your wishes with examples instead of instructions. See why this concept is powerful and how to think about applying it to solve your problems.

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