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The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

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Machine learning and artificial intelligence are dramatically changing the way businesses operate and people live. The TWIML AI Podcast brings the top minds and ideas from the world of ML and AI to a broad and influential community of ML/AI researchers, data scientists, engineers and tech-savvy business and IT leaders.Hosted by Sam Charrington, a sought after industry analyst, speaker, commentator and thought leader.Technologies covered include machine learning, artificial intelligence, deep learning, natural language processing, neural networks, analytics, deep learning, computer science, data science and more.

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Machine learning and artificial intelligence are dramatically changing the way businesses operate and people live. The TWIML AI Podcast brings the top minds and ideas from the world of ML and AI to a broad and influential community of ML/AI researchers, data scientists, engineers and tech-savvy business and IT leaders.Hosted by Sam Charrington, a sought after industry analyst, speaker, commentator and thought leader.Technologies covered include machine learning, artificial intelligence, deep learning, natural language processing, neural networks, analytics, deep learning, computer science, data science and more.

iTunes Ratings

251 Ratings
Average Ratings
221
15
7
3
5

Excellent Perspectives in Machine Learning

By Joel Sapp - Feb 26 2019
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Love this podcast. Give it a try.

Awesome podcast

By daniel432! - Aug 13 2018
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Love this podcast! The perspectives from experts are great

iTunes Ratings

251 Ratings
Average Ratings
221
15
7
3
5

Excellent Perspectives in Machine Learning

By Joel Sapp - Feb 26 2019
Read more
Love this podcast. Give it a try.

Awesome podcast

By daniel432! - Aug 13 2018
Read more
Love this podcast! The perspectives from experts are great
Cover image of The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Updated 1 day ago

Read more

Machine learning and artificial intelligence are dramatically changing the way businesses operate and people live. The TWIML AI Podcast brings the top minds and ideas from the world of ML and AI to a broad and influential community of ML/AI researchers, data scientists, engineers and tech-savvy business and IT leaders.Hosted by Sam Charrington, a sought after industry analyst, speaker, commentator and thought leader.Technologies covered include machine learning, artificial intelligence, deep learning, natural language processing, neural networks, analytics, deep learning, computer science, data science and more.

Rank #1: Anticipating Superintelligence with Nick Bostrom - TWiML Talk #181

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In this episode, we’re joined by Nick Bostrom, professor in the faculty of philosophy at the University of Oxford, where he also heads the Future of Humanity Institute, a multidisciplinary institute focused on answering big-picture questions for humanity with regards to AI safety and ethics.

Nick is of course also author of the book “Superintelligence: Paths, Dangers, Strategies.” In our conversation, we discuss the risks associated with Artificial General Intelligence and the more advanced AI systems Nick refers to as superintelligence. We also discuss Nick’s writings on the topic of openness in AI development, and the advantages and costs of open and closed development on the part of nations and AI research organizations. Finally, we take a look at what good safety precautions might look like, and how we can create an effective ethics framework for superintelligent systems.

The notes for this episode can be found at https://twimlai.com/talk/181.

Sep 17 2018
45 mins
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Rank #2: Trust and AI with Parinaz Sobhani - TWiML Talk #208

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In today’s episode we’re joined by Parinaz Sobhani, Director of Machine Learning at Georgian Partners.

In our conversation, Parinaz and I discuss some of the main issues falling under the “trust” umbrella, such as transparency, fairness and accountability. We also explore some of the trust-related projects she and her team at Georgian are working on, as well as some of the interesting trust and privacy papers coming out of the NeurIPS conference.

This week’s series is sponsored by our friends at Georgian Partners. Georgian recently published Building Conversational AI Teams, a comprehensive guide to lead you through sourcing, acquiring and nurturing a successful conversational AI team. Download at: https://gptrs.vc/convoai

For this episode's complete show notes, visit twimlai.com/talk/208.

Dec 11 2018
46 mins
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Rank #3: LSTMs, Plus a Deep Learning History Lesson with Jürgen Schmidhuber - TWiML Talk #44

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This week we have a very special interview to share with you! Those of you who’ve been receiving my newsletter for a while might remember that while in Switzerland last month, I had the pleasure of interviewing Jurgen Schmidhuber, in his lab IDSIA, which is the Dalle Molle Institute for Artificial Intelligence Research in Lugano, Switzerland, where he serves as Scientific Director. In addition to his role at IDSIA, Jurgen is also Co-Founder and Chief Scientist of NNaisense, a company that is using AI to build large-scale neural network solutions for “superhuman perception and intelligent automation.” Jurgen is an interesting, accomplished and in some circles controversial figure in the AI community and we covered a lot of very interesting ground in our discussion, so much so that I couldn't truly unpack it all until I had a chance to sit with it after the fact. We talked a bunch about his work on neural networks, especially LSTM’s, or Long Short-Term Memory networks, which are a key innovation behind many of the advances we’ve seen in deep learning and its application over the past few years. Along the way, Jurgen walks us through a deep learning history lesson that spans 50+ years. It was like walking back in time with the 3 eyed raven. I know you’re really going to enjoy this one, and by the way, this is definitely a nerd alert show! For the show notes, visit twimlai.com/talk/44
Aug 28 2017
1 hour 6 mins
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Rank #4: Machine Learning Platforms at Uber with Mike Del Balso - TWiML Talk #115

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In this episode, I speak with Mike Del Balso, Product Manager for Machine Learning Platforms at Uber. Mike and I sat down last fall at the Georgian Partners Portfolio conference to discuss his presentation “Finding success with machine learning in your company.” In our discussion, Mike shares some great advice for organizations looking to get value out of machine learning. He also details some of the pitfalls companies run into, such as not have proper infrastructure in place for maintenance and monitoring, not managing their expectations, and not putting the right tools in place for data science and development teams. On this last point, we touch on the Michelangelo platform, which Uber uses internally to build, deploy and maintain ML systems at scale, and the open source distributed TensorFlow system they’ve created, Horovod. This was a very insightful interview, so get your notepad ready! Vote on our #MyAI Contest! Over the past few weeks, you’ve heard us talk quite a bit about our #MyAI Contest, which explores the role we see for AI in our personal lives! We received some outstanding entries, and now it’s your turn to check them out and vote for a winner. Do this by visiting our contest page at https://twimlai.com/myai. Voting remains open until Sunday, March 4th at 11:59 PM Eastern time. Be sure to check out some of the great names that will be at the AI Conference in New York, Apr 29–May 2, where you'll join the leading minds in AI, Peter Norvig, George Church, Olga Russakovsky, Manuela Veloso, and Zoubin Ghahramani. Explore AI's latest developments, separate what's hype and what's really game-changing, and learn how to apply AI in your organization right now. Save 20% on most passes with discount code PCTWIML at twimlai.com/ainy2018. The notes for this show can be found at twimlai.com/talk/115.
Mar 01 2018
50 mins
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Rank #5: How ML Keeps Shelves Stocked at Home Depot with Pat Woowong - TWiML Talk #175

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Today we’re joined by Pat Woowong, principal engineer in the applied machine intelligence group at The Home Depot.

We discuss a project that Pat recently presented at the Google Cloud Next conference which used machine learning to predict shelf-out scenarios within stores. We dig into the motivation for this system and how the team went about building it, including what type of models ended up working best, how they collected their data, their use of kubernetes to support future growth in the platform, and much more.

For the complete show notes, visit twimlai.com/talk/175.

Aug 23 2018
45 mins
Play

Rank #6: Reinforcement Learning Deep Dive with Pieter Abbeel - TWiML Talk #28

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This week our guest is Pieter Abbeel, Assistant Professor at UC Berkeley, Research Scientist at OpenAI, and Cofounder of Gradescope. Pieter has an extensive background in AI research, going way back to his days as Andrew Ng’s first PhD student at Stanford. His research today is focused on deep learning for robotics. During this conversation, Pieter and I really dig into reinforcement learning, a technique for allowing robots (or AIs) to learn through their own trial and error. Nerd alert!! This conversation explores cutting edge research with one of the leading researchers in the field and, as a result, it gets pretty technical at times. I try to uplevel it when I can keep up myself, so hang in there. I promise that you’ll learn a ton if you keep with it. The notes for this show can be found at twimlai.com/talk/28
Jun 17 2017
54 mins
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Rank #7: Training Data for Autonomous Vehicles - Daryn Nakhuda - TWiML Talk #57

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The episode you are about to hear is the first of a new series of shows on Autonomous Vehicles. We all know that self-driving cars is one of the hottest topics in ML & AI, so we had to dig a little deeper into the space. To get us started on this journey, I’m excited to present this interview with Daryn Nakhuda, CEO and Co-Founder of MightyAI. Daryn and I discuss the many challenges of collecting training data for autonomous vehicles, along with some thoughts on human-powered insights and annotation, semantic segmentation, and a ton more great stuff. For the notes for this show, Visit twimlai.com/talk/57. For series info, visit twimlai.com/AV2017
Oct 23 2017
49 mins
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Rank #8: Contextual Modeling for Language and Vision with Nasrin Mostafazadeh - TWiML Talk #174

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Today we’re joined by Nasrin Mostafazadeh, Senior AI Research Scientist at New York-based Elemental Cognition.

Our conversation focuses on Nasrin’s work in event-centric contextual modeling in language and vision, which she sees as a means of giving AI systems a bit of “common sense.” We discuss Nasrin’s work on the Story Cloze Test, which is a reasoning framework for evaluating story understanding and generation. We explore the details of this task--including what constitutes a “story”--and some of the challenges it presents and approaches for solving it. We also discuss how you model what a computer understands, building semantic representation algorithms, different ways to approach “explainability,” and multimodal extensions to her contextual modeling work.

The notes for this episode can be found at https://twimlai.com/talk/174.

Aug 20 2018
49 mins
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Rank #9: Evolutionary Algorithms in Machine Learning with Risto Miikkulainen - TWiML Talk #47

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My guest this week is Risto Miikkulainen, professor of computer science at UT-Austin and vice president of Research at Sentient Technologies. Risto came locked and loaded to discuss a topic that we've received a ton of requests for -- evolutionary algorithms. During our talk we discuss some of the things Sentient is working on in the financial services and retail fields, and we dig into the technology behind it, evolutionary algorithms, which is also the focus of Risto’s research at UT. I really enjoyed this interview and learned a ton, and I’m sure you will too! Notes for this show can be found at twimlai.com/talk/47.
Sep 11 2017
1 hour
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Rank #10: Trends in Computer Vision with Siddha Ganju - TWiML Talk #218

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In the final episode of our AI Rewind series, we’re excited to have Siddha Ganju back on the show.

Siddha, who is now an autonomous vehicles solutions architect at Nvidia shares her thoughts on trends in Computer Vision in 2018 and beyond. We cover her favorite CV papers of the year in areas such as neural architecture search, learning from simulation, application of CV to augmented reality, and more, as well as a bevy of tools and open source projects.

The complete show notes for this episode can be found at https://twimlai.com/talk/218

For more information on our AI Rewind series, visit https://twimlai.com/rewind18.

Jan 07 2019
1 hour 11 mins
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Rank #11: Practical Deep Learning with Rachel Thomas - TWiML Talk #138

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In this episode, i'm joined by Rachel Thomas, founder and researcher at Fast AI. If you’re not familiar with Fast AI, the company offers a series of courses including Practical Deep Learning for Coders, Cutting Edge Deep Learning for Coders and Rachel’s Computational Linear Algebra course. The courses are designed to make deep learning more accessible to those without the extensive math backgrounds some other courses assume. Rachel and I cover a lot of ground in this conversation, starting with the philosophy and goals behind the Fast AI courses. We also cover Fast AI’s recent decision to switch to their courses from Tensorflow to Pytorch, the reasons for this, and the lessons they’ve learned in the process. We discuss the role of the Fast AI deep learning library as well, and how it was recently used to held their team achieve top results on a popular industry benchmark of training time and training cost by a factor of more than ten. The notes for this show can be found at twimlai.com/talk/138
May 14 2018
45 mins
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Rank #12: Understanding Deep Neural Nets with Dr. James McCaffrey - TWiML Talk #13

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My guest this week is Dr. James McCaffrey, research engineer at Microsoft Research. James and I cover a ton of ground in this conversation, including recurrent neural nets (RNNs), convolutional neural nets (CNNs), long short term memory (LSTM) networks, residual networks (ResNets), generative adversarial networks (GANs), and more. We also discuss neural network architecture and promising alternative approaches such as symbolic computation and particle swarm optimization. The show notes can be found at twimlai.com/talk/13.
Mar 03 2017
1 hour 18 mins
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Rank #13: Adversarial Attacks Against Reinforcement Learning Agents with Ian Goodfellow & Sandy Huang

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In this episode, I’m joined by Ian Goodfellow, Staff Research Scientist at Google Brain and Sandy Huang, Phd Student in the EECS department at UC Berkeley, to discuss their work on the paper Adversarial Attacks on Neural Network Policies. If you’re a regular listener here you’ve probably heard of adversarial attacks, and have seen examples of deep learning based object detectors that can be fooled into thinking that, for example, a giraffe is actually a school bus, by injecting some imperceptible noise into the image. Well, Sandy and Ian’s paper sits at the intersection of adversarial attacks and reinforcement learning, another area we’ve discussed quite a bit on the podcast. In their paper, they describe how adversarial attacks can also be effective at targeting neural network policies in reinforcement learning. Sandy gives us an overview of the paper, including how changing a single pixel value can throw off performance of a model trained to play Atari games. We also cover a lot of interesting topics relating to adversarial attacks and RL individually, and some related areas such as hierarchical reward functions and transfer learning. This was a great conversation that I’m really excited to bring to you! For complete show notes, head over to twimlai.com/talk/119
Mar 15 2018
50 mins
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Rank #14: ML for Understanding Satellite Imagery at Scale with Kyle Story - TWiML Talk #173

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Today we’re joined by Kyle Story, computer vision engineer at Descartes Labs.

Kyle and I caught up after his recent talk at the Google Cloud Next Conference titled “How Computers See the Earth: A Machine Learning Approach to Understanding Satellite Imagery at Scale.” We discuss some of the interesting computer vision problems he’s worked on at Descartes, including custom object detectors and the company’s geovisual search engine, covering everything from the models they’ve developed and platform they’ve built, to the key challenges they’ve had to overcome in scaling them.

For the complete show notes, visit twimlai.com/talk/173.

Aug 16 2018
56 mins
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Rank #15: Trends in Natural Language Processing with Sebastian Ruder - TWiML Talk #216

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In this episode of our AI Rewind series, we’ve brought back recent guest Sebastian Ruder, PhD Student at the National University of Ireland and Research Scientist at Aylien, to discuss trends in Natural Language Processing in 2018 and beyond.

In our conversation we cover a bunch of interesting papers spanning topics such as pre-trained language models, common sense inference datasets and large document reasoning and more, and talk through Sebastian’s predictions for the new year.

The complete show notes for this episode can be found at twimlai.com/talk/216.

For more information on the AI Rewind 2018 series, visit twimlai.com/rewind18.

Dec 31 2018
53 mins
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Rank #16: Learning to Learn, and other Opportunities in Machine Learning with Graham Taylor - TWiML Talk #62

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The podcast you’re about to hear is the third of a series of shows recorded at the Georgian Partners Portfolio Conference last week in Toronto. My guest this time is Graham Taylor, professor of engineering at the University of Guelph, who keynoted day two of the conference. Graham leads the Machine Learning Research Group at Guelph, and is affiliated with Toronto’s recently formed Vector Institute for Artificial Intelligence. Graham and I discussed a number of the most important trends and challenges in artificial intelligence, including the move from predictive to creative systems, the rise of human-in-the-loop AI, and how modern AI is accelerating with our ability to teach computers how to learn-to-learn. The notes for this show can be found at twimlai.com/talk/62. For series info, visit twimlai.com/GPPC2017
Nov 03 2017
38 mins
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Rank #17: Evaluating Model Explainability Methods with Sara Hooker - TWiML Talk #189

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In this, the first episode of the Deep Learning Indaba series, we’re joined by Sara Hooker, AI Resident at Google Brain.

I had the pleasure of speaking with Sara in the run-up to the Indaba about her work on interpretability in deep neural networks. We discuss what interpretability means and when it’s important, and explore some nuances like the distinction between interpreting model decisions vs model function. We also dig into her paper Evaluating Feature Importance Estimates and look at the relationship between this work and interpretability approaches like LIME.

We also talk a bit about Google, in particular, the relationship between Brain and the rest of the Google AI landscape and the significance of the recently announced Google AI Lab in Accra, Ghana, being led by friend of the show Moustapha Cisse. And, of course, we chat a bit about the Indaba as well.

For the complete show notes for this episode, visit twimlai.com/talk/189.

For more information on the Deep Learning Indaba series, visit twimlai.com/indaba2018

Oct 10 2018
1 hour 5 mins
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Rank #18: Growth Hacking Sports w/ Machine Learning with Noah Gift - TWiML Talk #158

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In this episode of our AI in Sports series I'm joined by Noah Gift, Founder and Consulting CTO at Pragmatic Labs and professor at UC Davis.

Noah previously worked for a startup called Score Sports, which used machine learning to uncover athlete influence on social media and internet platforms. We look into some of his findings in that role, including how to predict the impact of athletes’ social media engagement. We also discuss some of his more recent work in using social media to predict which players hold the most on-court value, and how this work could lead to more complete approaches to player valuation. Finally, we spend some time discussing some areas that Noah sees as ripe for new research and experimentation across sports, and we take a look at his upcoming book Pragmatic AI, An Introduction to Cloud-Based Machine Learning. For those interested in pre-ordering the book, be sure to check out the link in the show notes for a nice discount code.

The notes for this show can be found at twimlai.com/talk/158.

For more on our AI in Sports series visit twimlai.com/aiinsports.

Jun 28 2018
50 mins
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Rank #19: Trends in Reinforcement Learning with Simon Osindero - TWiML Talk #217

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In this episode of our AI Rewind series, we introduce a new friend of the show, Simon Osindero, Staff Research Scientist at DeepMind.

We discuss trends in Deep Reinforcement Learning in 2018 and beyond. We’ve packed a bunch into this show, as Simon walks us through many of the important papers and developments seen last year in areas like Imitation Learning, Unsupervised RL, Meta-learning, and more.

The complete show notes for this episode can be found at https://twimlai.com/talk/217.

For more information on our 2018 AI Rewind series, visit https://twimlai.com/rewind2018.

Jan 03 2019
52 mins
Play

Rank #20: Managing Deep Learning Experiments with Lukas Biewald - TWIML Talk #295

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Today we are joined by Lukas Biewald, CEO and Co-Founder of Weights & Biases. Lukas, previously CEO and Founder of Figure Eight (CrowdFlower), has a straightforward goal: provide researchers with SaaS that is easy to install, simple to operate, and always accessible. Seeing a need for reproducibility in deep learning experiments, Lukas founded Weights & Biases. In this episode we discuss:

  • The experiment tracking tool, how it works, and the components that make it unique in the ML marketplace
  • The open, collaborative culture that Lukas promotes
  • How Lukas got his start in deep learning experiments, what his experiment tracking used to look like, 
  • The current Weights & Biases business success strategy and what his team is working on today

The complete show notes for this episode can be found at twimlai.com/talk/295

Thanks to our friends at Weights & Biases for their support of the show, their sponsorship of this episode, and our upcoming event, TWIMLcon: AI Platforms. 

Registration for TWIMLcon is still open! Visit twimlcon.com/register today! 

Aug 29 2019
43 mins
Play

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