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Thread: Miscellaneous

  1. #51

    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.

  2. #52

    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

  3. #53

    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.

  4. #54

    Learning to dress: synthesizing human dressing motion via deep reinforcement learning

    Published on Sep 10, 2018

    Video results for the paper "Learning To Dress: Synthesizing Human Dressing Motion via Deep Reinforcement Learning" to be presented at Siggraph Asia 2018.

  5. #55
    Article "How to tell whether machine-learning systems are robust enough for the real world"
    New method quickly detects instances when neural networks make mistakes they shouldn’t.

    by Rob Matheson
    May 10, 2019

  6. #56

    Machine learning: living in the age of AI | A WIRED film

    Published on Jun 20, 2019

    “Machine Learning: Living in the Age of AI,” examines the extraordinary ways in which people are interacting with AI today. Hobbyists and teenagers are now developing tech powered by machine learning and WIRED shows the impacts of AI on schoolchildren and farmers and senior citizens, as well as looking at the implications that rapidly accelerating technology can have. The film was directed by filmmaker Chris Cannucciari, produced by WIRED, and supported by McCann Worldgroup.

  7. #57

    Deep Learning State of the Art (2020) | MIT Deep Learning Series

    Jan 10, 2020

    Lecture on most recent research and developments in deep learning, and hopes for 2020. This is not intended to be a list of SOTA benchmark results, but rather a set of highlights of machine learning and AI innovations and progress in academia, industry, and society in general. This lecture is part of the MIT Deep Learning Lecture Series.


    0:00 - Introduction
    0:33 - AI in the context of human history
    5:47 - Deep learning celebrations, growth, and limitations
    6:35 - Deep learning early key figures
    9:29 - Limitations of deep learning
    11:01 - Hopes for 2020: deep learning community and research
    12:50 - Deep learning frameworks: TensorFlow and PyTorch
    15:11 - Deep RL frameworks
    16:13 - Hopes for 2020: deep learning and deep RL frameworks
    17:53 - Natural language processing
    19:42 - Megatron, XLNet, ALBERT
    21:21 - Write with transformer examples
    24:28 - GPT-2 release strategies report
    26:25 - Multi-domain dialogue
    27:13 - Commonsense reasoning
    28:26 - Alexa prize and open-domain conversation
    33:44 - Hopes for 2020: natural language processing
    35:11 - Deep RL and self-play
    35:30 - OpenAI Five and Dota 2
    37:04 - DeepMind Quake III Arena
    39:07 - DeepMind AlphaStar
    41:09 - Pluribus: six-player no-limit Texas hold'em poker
    43:13 - OpenAI Rubik's Cube
    44:49 - Hopes for 2020: Deep RL and self-play
    45:52 - Science of deep learning
    46:01 - Lottery ticket hypothesis
    47:29 - Disentangled representations
    48:34 - Deep double descent
    49:30 - Hopes for 2020: science of deep learning
    50:56 - Autonomous vehicles and AI-assisted driving
    51:50 - Waymo
    52:42 - Tesla Autopilot
    57:03 - Open question for Level 2 and Level 4 approaches
    59:55 - Hopes for 2020: autonomous vehicles and AI-assisted driving
    1:01:43 - Government, politics, policy
    1:03:03 - Recommendation systems and policy
    1:05:36 - Hopes for 2020: Politics, policy and recommendation systems
    1:06:50 - Courses, Tutorials, Books
    1:10:05 - General hopes for 2020
    1:11:19 - Recipe for progress in AI
    1:14:15 - Q&A: what made you interested in AI
    1:15:21 - Q&A: Will machines ever be able to think and feel?
    1:18:20 - Q&A: Is RL a good candidate for achieving AGI?
    1:21:31 - Q&A: Are autonomous vehicles responsive to sound?
    1:22:43 - Q&A: What does the future with AGI look like?
    1:25:50 - Q&A: Will AGI systems become our masters?

  8. #58

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