The Future of Machine Learning and JavaScript

There are many exciting things happening with AI, from which, until recently, JavaScript developers were largely shut out. But things are changing, if you can do `npm install @tensorflow/tfjs` or make an API call, you can now do AI. In this fast-paced talk, I’ll open your mind to what’s possible by demoing several AI-powered JavaScript […]

Deep Learning Frameworks 2019

Which deep learning framework should you use? In this video I’ll compare 10 deep learning frameworks across a wide variety of metrics. PyTorch, Tensorflow, MXNet, Chainer, CNTK, Sonnet, DeepLearning4J, CoreML, ONNX, we’ve got a lot to cover in this video! Using code, programmatic features, and theory, I’ll navigate this field ultimately coming to some clear […]

#038 – Professor Kenneth Stanley – Why Greatness Cannot Be Planned

Professor Kenneth Stanley is currently a research science manager at OpenAI in San Fransisco. We’ve Been dreaming about getting Kenneth on the show since the very begininning of Machine Learning Street Talk. Some of you might recall that our first ever show was on the enhanced POET paper, of course Kenneth had his hands all […]

Deep Learning State of the Art (2019) – MIT

New lecture on recent developments in deep learning that are defining the state of the art in our field (algorithms, applications, and tools). This is not a complete list, but hopefully includes a good sampling of new exciting ideas. For more lecture videos visit our website or follow code tutorials on our GitHub repo. INFO: […]

#040 – Adversarial Examples (Dr. Nicholas Carlini, Dr. Wieland Brendel, Florian Tramèr)

Pod version; https://anchor.fm/machinelearningstreettalk/episodes/040—Adversarial-Examples-Dr–Nicholas-Carlini–Dr–Wieland-Brendel–Florian-Tramr-epo5qr Adversarial examples have attracted significant attention in machine learning, but the reasons for their existence and pervasiveness remain unclear. there’s good reason to believe neural networks look at very different features than we would have expected. As articulated in the 2019 “features not bugs” paper Adversarial examples can be directly attributed to […]

Neural Differential Equations

This won the best paper award at NeurIPS (the biggest AI conference of the year) out of over 4800 other research papers! Neural Ordinary Differential Equations is the official name of the paper and in it the authors introduce a new type of neural network. This new network doesn’t have any layers! Its framed as […]

Harri Valpola: System 2 AI and Planning in Model-Based Reinforcement Learning

In this episode of Machine Learning Street Talk, Tim Scarfe, Yannic Kilcher and Connor Shorten interviewed Harri Valpola, CEO and Founder of Curious AI. We continued our discussion of System 1 and System 2 thinking in Deep Learning, as well as miscellaneous topics around Model-based Reinforcement Learning. Dr. Valpola describes some of the challenges of […]

My Prediction About Autonomous Cars | Answers With Joe

Get two months of Skillshare for free: https://skl.sh/joescott4 You’ve been hearing a lot about autonomous, self-driving cars lately. Here I take a look at where we are, when we’ll get there, and how it will change the world. Support me on Patreon! http://www.patreon.com/answerswithjoe Get cool nerdy t-shirts at http://www.answerswithjoe.com/shirts Thinking of getting a Tesla? Get […]

Exploring Open-Ended Algorithms: POET

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 […]

Self-Driving Cars: State of the Art (2019)

Introductory lecture of the MIT Self-Driving Cars series (6.S094) with an overview of the autonomous vehicle industry in 2018 and looking forward to 2019, including Waymo, Tesla, Cruise, Ford, GM, and out-of-the-box ideas of boring tunnels, flying cars, connected vehicles, and more. This covers the state of the art in terms of industry developments and […]

#035 Christmas Community Edition!

Welcome to the Christmas special community edition of MLST! We discuss some recent and interesting papers from Pedro Domingos (are NNs kernel machines?), Deepmind (can NNs out-reason symbolic machines?), Anna Rodgers – When BERT Plays The Lottery, All Tickets Are Winning, Prof. Mark Bishop (even causal methods won’t deliver understanding), We also cover our favourite […]

OpenAI Spinning Up in Deep RL Workshop

Opening & Intro to RL, Part 1, by Joshua Achiam at 25:11 Intro to RL, Part 2, by Joshua Achiam at 1:48:42 Learning Dexterity, by Matthias Plappert at 2:26:26 AI Safety: An Introduction, by Dario Amodei at 2:58:00 Recorded on Feburary 2, 2019. Learn more: https://openai.com/blog/spinning-up-in-deep-rl-workshop-review/ YouTube Source for this AI Video

#59 – Jeff Hawkins (Thousand Brains Theory)

The ultimate goal of neuroscience is to learn how the human brain gives rise to human intelligence and what it means to be intelligent. Understanding how the brain works is considered one of humanity’s greatest challenges. Jeff Hawkins thinks that the reality we perceive is a kind of simulation, a hallucination, a confabulation. He thinks […]

TensorFlow Dev Summit 2019 Keynote

Join the TensorFlow team as they kick off #TFDevSummit 2019! Learn about TensorFlow’s 2019 roadmap and what’s new for Google’s open source #MachineLearning platform. See the revamped dev site → https://www.tensorflow.org/ Watch all TensorFlow Dev Summit ’19 sessions → http://bit.ly/TFDS19Sessions Learn more on the event homepage → http://bit.ly/TFDS19 Subscribe to the TensorFlow YouTube channel! → […]

Cognitive Architecture: Knowledge, Patterns, and Reasoning

Video of lecture at Stanford University course SC-152 October 29, 2019 Topics: Cognitive Architecture, Soar, Executive Function, Reactive and Deliberative systems, Marr’s levels of abstraction, Lida, Copycat/Metacat, architecture of conversational agents. YouTube Source for this AI Video

#51 Francois Chollet – Intelligence and Generalisation

In today’s show we are joined by Francois Chollet, I have been inspired by Francois ever since I read his Deep Learning with Python book and started using the Keras library which he invented many, many years ago. Francois has a clarity of thought that I’ve never seen in any other human being! He has […]

I Forced a Drone Bot To Follow Me

Update: The #IBMDroneDrop is back for 2019. IBM Developer is giving away 1,500 more DJI Tello Drones. Enter now to start the challenges and stay in the game. Contest ends June 16, 2019: https://ibm.biz/IBMDroneDrop WATCH PART 2: https://youtu.be/esw88_gKOpA IBM Blog post with more information: https://developer.ibm.com/blogs/2018/11/12/win-a-drone-program-a-drone-change-the-world/ SUBSCRIBE FOR MORE: http://sefdstuff.com/science SUPPORT ON PATREON: https://www.patreon.com/Jabrils My First […]

#55 Self-Supervised Vision Models (Dr. Ishan Misra – FAIR).

Dr. Ishan Misra is a Research Scientist at Facebook AI Research where he works on Computer Vision and Machine Learning. His main research interest is reducing the need for human supervision, and indeed, human knowledge in visual learning systems. He finished his PhD at the Robotics Institute at Carnegie Mellon. He has done stints at […]