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    Technology

    Machine Learning Street Talk (MLST)

    Welcome! We engage in fascinating discussions with pre-eminent figures in the AI field. Our flagship show covers current affairs in AI, cognitive science, neuroscience and philosophy of mind with in-depth analysis. Our approach is unrivalled in terms of scope and rigour – we believe in intellectual diversity in AI, and we touch on all of the main ideas in the field with the hype surgically removed. MLST is run by Tim Scarfe, Ph.D (https://www.linkedin.com/in/ecsquizor/) and features regular appearances from MIT Doctor of Philosophy Keith Duggar (https://www.linkedin.com/in/dr-keith-duggar/).

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    Copyright: © Machine Learning Street Talk (MLST)

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    Latest Episodes:
    CURL: Contrastive Unsupervised Representations for Reinforcement Learning May 02, 2020
    Show notes

    According to Yann Le Cun, the next big thing in machine learning is unsupervised learning. Self-supervision has changed the entire game in the last few years in deep learning, first transforming the language world with word2vec and BERT -- but now it's turning computer vision upside down.


    This week Yannic, Connor and I spoke with one of the authors, Aravind Srinivas who recently co-led the hot-off-the-press CURL: Contrastive Unsupervised Representations for Reinforcement Learning alongside Michael (Misha) Laskin. CURL has had an incredible reception in the ML community in the last month or so. Remember the Deep Mind paper which solved the Atari games using the raw pixels? Aravind's approach uses contrastive unsupervised learning to featurise the pixels before applying RL. CURL is the first image-based algorithm to nearly match the sample-efficiency and performance of methods that use state-based features! This is a huge step forwards in being able to apply RL in the real world.


    We explore RL and self-supervision for computer vision in detail and find out about how Aravind got into machine learning.


    Original YouTube Video: https://youtu.be/1MprzvYNpY8


    Paper:

    CURL: Contrastive Unsupervised Representations for Reinforcement Learning

    Aravind Srinivas, Michael Laskin, Pieter Abbeel

    https://arxiv.org/pdf/2004.04136.pdf


    Yannic's analysis video: https://www.youtube.com/watch?v=hg2Q_O5b9w4


    #machinelearning #reinforcementlearning #curl #timscarfe #yannickilcher #connorshorten


    Music credit; https://soundcloud.com/errxrmusic/in-my-mind


    Exploring Open-Ended Algorithms: POET Apr 24, 2020
    Show notes

    Three YouTubers; Tim Scarfe - Machine Learning Dojo (https://www.youtube.com/channel/UCXvHuBMbgJw67i5vrMBBobA), Connor Shorten - Henry AI Labs (https://www.youtube.com/channel/UCHB9VepY6kYvZjj0Bgxnpbw) and Yannic Kilcher (https://www.youtube.com/channel/UCZHmQk67mSJgfCCTn7xBfew). We made a new YouTube channel called Machine Learning Street Talk. Every week we will talk about the latest and greatest in AI. Subscribe now!

    Special guests this week; Dr. Mathew Salvaris (https://www.linkedin.com/in/drmathewsalvaris/), Eric Craeymeersch (https://www.linkedin.com/in/ericcraeymeersch/), Dr. Keith Duggar (https://www.linkedin.com/in/dr-keith-duggar/), Dmitri Soshnikov (https://www.linkedin.com/in/shwars/)

    We discuss the new concept of an open-ended, or "AI-Generating" algorithm. Open-endedness is a class of algorithms which generate problems and solutions to increasingly complex and diverse tasks. These algorithms create their own curriculum of learning. Complex tasks become tractable because they are now the final stepping stone in a lineage of progressions. In many respects, it's better to trust the machine to develop the learning curriculum, because the best curriculum might be counter-intuitive. These algorithms can generate a radiating tree of evolving challenges and solutions just like natural evolution. Evolution has produced an eternity of diversity and complexity and even produced human intelligence as a side-effect! Could AI-generating algorithms be the next big thing in machine learning?

    Wang, Rui, et al. "Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions." arXiv preprint arXiv:2003.08536 (2020). https://arxiv.org/abs/2003.08536

    Wang, Rui, et al. "Paired open-ended trailblazer (poet): Endlessly generating increasingly complex and diverse learning environments and their solutions." arXiv preprint arXiv:1901.01753 (2019). https://arxiv.org/abs/1901.01753

    Watch Yannic’s video on POET: https://www.youtube.com/watch?v=8wkgDnNxiVs
    and on the extended POET: https://youtu.be/gbG1X8Xq-T8
    Watch Connor’s video https://www.youtube.com/watch?v=jxIkPxkN10U
    UberAI labs video: https://www.youtube.com/watch?v=RX0sKDRq400

    #reinforcementlearning #machinelearning #uber #deeplearning #rl #timscarfe #connorshorten #yannickilcher



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