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

Updated 3 days ago

Rank #59 in Technology category

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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

265 Ratings
Average Ratings
233
15
7
3
7

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

265 Ratings
Average Ratings
233
15
7
3
7

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

Listen to:

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 3 days 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.

AI for Healthcare with Peter Lee - TWiML Talk #231

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In this episode, we’re joined by Peter Lee, Corporate Vice President at Microsoft Research responsible for the company’s healthcare initiatives.

Peter and I met a few months ago at the Microsoft Ignite conference, where he gave me some really interesting takes on AI development in China. You can find more on that topic in the show notes. This conversation centers the three impact areas Peter sees for AI in healthcare, namely diagnostics and therapeutics, tools, and the future of precision medicine. We dig into some examples in each area, and Peter details the realities of applying machine learning and some of the impediments to rapid scale.

We’d like to thank Microsoft for their support and their sponsorship of this series. Microsoft is committed to ensuring the responsible development and use of AI and is empowering people around the world with intelligent technology to help solve previously intractable societal challenges spanning sustainability, accessibility and humanitarian action. Learn more at Microsoft.ai.

The complete show notes for this episode can be found at twimlai.com/talk/231.

Feb 18 2019

57mins

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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

50mins

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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

1hr 6mins

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Deep Neural Nets for Visual Recognition with Matt Zeiler - TWiML Talk #22

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Today we bring you our final interview from backstage at the NYU FutureLabs AI Summit. Our guest this week is Matt Zeiler. Matt graduated from the University of Toronto where he worked with deep learning researcher Geoffrey Hinton and went on to earn his PhD in machine learning at NYU, home of Yann Lecun. In 2013 Matt’s founded Clarifai, a startup whose cloud-based visual recognition system gives developers a way to integrate visual identification into their own products, and whose initial image classification algorithm achieved top 5 results in that year’s ImageNet competition. I caught up with Matt after his talk “From Research to the Real World”. Our conversation focused on the birth and growth of Clarifai, as well as the underlying deep neural network architectures that enable it. If you’ve been listening to the show for a while, you’ve heard me ask several guests how they go about evolving the architectures of their deep neural networks to enhance performance. Well, in this podcast Matt gives the most satisfying answer I’ve received to date by far. Check it out. I think you’ll enjoy it. The show notes can be found at twimlai.com/talk/22.

May 05 2017

24mins

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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

1hr

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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

46mins

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Deep Reinforcement Learning Primer and Research Frontiers with Kamyar Azizzadenesheli - TWiML Talk #177

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Today we’re joined by Kamyar Azizzadenesheli, PhD student at the University of California, Irvine, and visiting researcher at Caltech where he works with Anima Anandkumar, who you might remember from TWiML Talk 142.

We begin with a reinforcement learning primer of sorts, in which we review the core elements of RL, along with quite a few examples to help get you up to speed. We then discuss a pair of Kamyar’s RL-related papers: “Efficient Exploration through Bayesian Deep Q-Networks” and “Sample-Efficient Deep RL with Generative Adversarial Tree Search.” In addition to discussing Kamyar’s work, we also chat a bit of the general landscape of RL research today. So whether you’re new to the field or want to dive into cutting-edge reinforcement learning research with us, this podcast is here for you!

If you'd like to skip the Deep Reinforcement Learning primer portion of this and jump to the research discussion, skip ahead to the 34:30 mark of the episode.

Aug 30 2018

1hr 35mins

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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

1hr 18mins

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Exploring TensorFlow 2.0 with Paige Bailey - TWiML Talk #242

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Today we're joined by Paige Bailey, a TensorFlow developer advocate at Google to discuss the TensorFlow 2.0 alpha release.

Paige and I sat down to talk through the latest TensorFlow updates, and we cover a lot of ground, including the evolution of the TensorFlow APIs and the role of eager mode, tf.keras and tf.function, the evolution of TensorFlow for Swift and its inclusion in the new fast.ai course, new updates to TFX (or TensorFlow Extended), Google’s end-to-end machine learning platform, the emphasis on community collaboration with TF 2.0, and a bunch more. The complete show notes for this episode can be found at https://twimlai.com/talk/242

I’d like to send a huge thanks to the TensorFlow team for helping us bring you this podcast series and giveaway. With all the great announcements coming out of the TensorFlow Dev Summit, including the 2.0 alpha, you should definitely check out the latest and greatest at https://tensorflow.org where you can also download and start building with the framework.

In conjunction with the TensorFlow 2.0 alpha release, and our TensorFlow Dev Summit series, we invite you to enter our TensorFlow Edge Kit Giveaway. Winners will receive a gift box from Google that includes some fun toys including the new Coral Edge TPU device and the SparkFun Edge development board powered by TensorFlow. Find out more at https://twimlai.com/tfgiveaway.

Mar 25 2019

41mins

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Building an Autonomous Knowledge Graph with Mike Tung - #319

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Today we’re joined by Mike Tung, Founder, and CEO of Diffbot. In our conversation, we discuss: 

  • Their various tools, including their Knowledge Graph, Extraction API, and CrawlBot.
  • How Knowledge Graph was inspired by Imagenet, how it was built, and how it differs from other, more mainstream knowledge graphs like Google Search and MSFT Bing.
  • How they balance being a research company that is also commercially viable.
  • The developer experience with their tools, and challenges faced.

The complete show notes can be found at twimlai.com/talk/319.

Nov 21 2019

44mins

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Pytorch: Fast Differentiable Dynamic Graphs in Python with Soumith Chintala - TWiML Talk #70

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This week, we’ll be featuring a series of shows recorded from Strange Loop, a great developer-focused conference that takes place every year right in my backyard! The conference is a multi-disciplinary melting pot of developers and thinkers across a variety of fields, and we’re happy to be able to bring a bit of it to those of you who couldn’t make it in person! In this show I speak with Soumith Chintala, a Research Engineer in the Facebook AI Research Lab (FAIR). Soumith joined me at Strange Loop before his talk on Pytorch, the deep learning framework. In this talk we discuss the market evolution of deep learning frameworks and tools, different approaches to programming deep learning frameworks, Facebook’s motivation for investing in Pytorch, and much more. This was a fun interview, I hope you enjoy! The notes for this show can be found at twimlai.com/talk/70 For series information, visit twimlai.com/stloop

Nov 21 2017

44mins

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Deep Robotic Learning with Sergey Levine - TWiML Talk #37

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This week we continue our Industrial AI series with Sergey Levine, an Assistant Professor at UC Berkeley whose research focus is Deep Robotic Learning. Sergey is part of the same research team as a couple of our previous guests in this series, Chelsea Finn and Pieter Abbeel, and if the response we’ve seen to those shows is any indication, you’re going to love this episode! Sergey’s research interests, and our discussion, focus in on include how robotic learning techniques can be used to allow machines to acquire autonomously acquire complex behavioral skills. We really dig into some of the details of how this is done and I found that our conversation filled in a lot of gaps for me from the interviews with Pieter and Chelsea. By the way, this is definitely a nerd alert episode! Notes for this show can be found at twimlai.com/talk/37

Jul 24 2017

49mins

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Integrating Psycholinguistics into AI with Dominique Simmons - TWiML Talk #23

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I think you’re really going to enjoy today’s show. Our guest this week is Dominique Simmons, Applied research Scientist at AI tools vendor Dimensional Mechanics. Dominique brings an interesting background in Cognitive Psychology and psycholinguistics to her work and research in AI and, well, to this podcast. In our conversation, we cover the implications of cognitive psychology for neural networks and AI systems, and in particular how an understanding of human cognition impacts the development of AI models for media applications. We also discuss her research into multimodal training of AI models, and how our understanding of the human brain has influenced this work. We also explore the debate around the biological plausibility of machine learning and AI models. It was a great conversation. The show notes can be found at twimlai.com/talk/23.

May 12 2017

1hr 2mins

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Dissecting the Controversy around OpenAI's New Language Model - TWiML Talk #234

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If you’re listening to this podcast, you’ve likely seen some of the press coverage and discussion surrounding the release, or lack thereof, of OpenAI’s new GPT-2 Language Model. The announcement caused quite a stir, with reactions spanning confusion, frustration, concern, and many points in between. Several days later, many open questions remained about the model and the way the release was handled.

Seeing the continued robust discourse, and wanting to offer the community a forum for exploring this topic with more nuance than Twitter’s 280 characters allow, we convened the inaugural “TWiML Live” panel. I was joined on the panel by Amanda Askell and Miles Brundage of OpenAI, Anima Anandkumar of NVIDIA and CalTech, Robert Munro of Lilt, and Stephen Merity, the latter being some of the most outspoken voices in the online discussion of this issue.

Our discussion thoroughly explored the many issues surrounding the GPT-2 release controversy. We cover the basics like what language models are and why they’re important, and why this announcement caused such a stir, and dig deep into why the lack of a full release of the model raised concerns for so many.

The discussion initially aired via Youtube Live and we’re happy to share it with you via the podcast as well. To be clear, both the panel discussion and live stream format were a bit of an experiment for us and we’d love to hear your thoughts on it. Would you like to see, or hear, more of these TWiML Live conversations? If so, what issues would you like us to take on?

If you have feedback for us on the format or if you’d like to join the discussion around OpenAI’s GPT-2 model, head to the show notes page for this show at twimlai.com/talk/234 and leave us a comment.

Feb 25 2019

1hr 6mins

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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

45mins

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Natural Language Understanding for Amazon Alexa with Zornitsa Kozareva - TWiML Talk #30

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Our guest this week is Zornitsa Kozareva, Manager of Machine Learning with Amazon Web Services Deep Learning, where she leads a group focused on natural language processing and dialogue systems for products like Alexa and Lex, the latter of which we introduce in the podcast. We spend most of our time talking through the architecture of modern Natural Language Understanding systems, including the role of deep learning, and some of the various ways folks are working to overcome the challenges in this field, such as understanding human intent. If you’re interested in this field she mentions the AWS Chatbot Challenge, which you’ve still got a couple more weeks to participate in. The notes for this show can be found at twimlai.com/talk/30.

Jun 29 2017

56mins

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Autonomous Aerial Guidance, Navigation and Control Systems with Christopher Lum - TWiML Talk #129

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Ok, In this episode, I'm joined by Christopher Lum, Research Assistant Professor in the University of Washington’s Department of Aeronautics and Astronautics. Chris also co-heads the University’s Autonomous Flight Systems Lab, where he and his students are working on the guidance, navigation, and control of unmanned systems. In our conversation, we discuss some of the technical and regulatory challenges of building and deploying Unmanned Autonomous Systems. We also talk about some interesting work he’s doing on evolutionary path planning systems as well as an Precision Agriculture use case. Finally, Chris shares some great starting places for those looking to begin a journey into autonomous systems research. The notes for this show can be found at twimlai.com/talk/129.

Apr 19 2018

54mins

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Philosophy of Intelligence with Matthew Crosby - TWiML Talk #91

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This week on the podcast we’re featuring a series of conversations from the NIPs conference in Long Beach, California. I attended a bunch of talks and learned a ton, organized an impromptu roundtable on Building AI Products, and met a bunch of great people, including some former TWiML Talk guests.This time around i'm joined by Matthew Crosby, a researcher at Imperial College London, working on the Kinds of Intelligence Project. Matthew joined me after the NIPS Symposium of the same name, an event that brought researchers from a variety of disciplines together towards three aims: a broader perspective of the possible types of intelligence beyond human intelligence, better measurements of intelligence, and a more purposeful analysis of where progress should be made in AI to best benefit society. Matthew’s research explores intelligence from a philosophical perspective, exploring ideas like predictive processing and controlled hallucination, and how these theories of intelligence impact the way we approach creating artificial intelligence. This was a very interesting conversation, i'm sure you’ll enjoy.

Dec 21 2017

31mins

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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

1hr 11mins

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End-to-End Data Science to Drive Business Decisions at LinkedIn with Burcu Baran - TWiML Talk #256

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In this episode of our Strata Data conference series, we’re joined by Burcu Baran, Senior Data Scientist at LinkedIn.

At Strata, Burcu, along with a few members of her team, delivered the presentation “Using the full spectrum of data science to drive business decisions,” which outlines how LinkedIn manages their entire machine learning production process. In our conversation, Burcu details each phase of the process, including problem formulation, monitoring features, A/B testing and more. We also discuss how her “horizontal” team works with other more “vertical” teams within LinkedIn, various challenges that arise when training and modeling such as data leakage and interpretability, best practices when trying to deal with data partitioning at scale, and of course, the need for a platform that reduces the manual pieces of this process, promoting efficiency.

The complete show notes for this episode can be found at https://twimlai.com/talk/256.

For more from the Strata Data conference series, visit twimlai.com/stratasf19.

I want to send a quick thanks to our friends at Cloudera for their sponsorship of this series of podcasts from the Strata Data Conference, which they present along with O’Reilly Media. Cloudera’s long been a supporter of the podcast; in fact, they sponsored the very first episode of TWiML Talk, recorded back in 2016. Since that time Cloudera has continued to invest in and build out its platform, which already securely hosts huge volumes of enterprise data, to provide enterprise customers with a modern environment for machine learning and analytics that works both in the cloud as well as the data center. In addition, Cloudera Fast Forward Labs provides research and expert guidance that helps enterprises understand the realities of building with AI technologies without needing to hire an in-house research team. To learn more about what the company is up to and how they can help, visit Cloudera’s Machine Learning resource center at cloudera.com/ml.

I’d also like to send a huge thanks to LinkedIn for their continued support and sponsorship of the show! Now that I’ve had a chance to interview several of the folks on LinkedIn’s Data Science and Engineering teams, it’s really put into context the complexity and scale of the problems that they get to work on in their efforts to create enhanced economic opportunities for every member of the global workforce. AI and ML are integral aspects of almost every product LinkedIn builds for its members and customers and their massive, highly structured dataset gives their data scientists and researchers the ability to conduct applied research to improve member experiences. To learn more about the work of LinkedIn Engineering, please visit engineering.linkedin.com/blog.

Apr 24 2019

49mins

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Single Headed Attention RNN: Stop Thinking With Your Head with Stephen Merity - #325

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Today we’re joined by Stephen Merity, startup founder and independent researcher, with  a focus on NLP and Deep Learning. In our conversation, we discuss:

  • Stephen’s newest paper, Single Headed Attention RNN: Stop Thinking With Your Head.
  • His motivations behind writing the paper; the fact that NLP research has been recently dominated by the use of transformer models, and the fact that these models are not the most accessible/trainable for broad use.
  • The architecture of transformers models.
  • How Stephen decided to use SHA-RNNs for this research.
  • How Stephen built and trained the model, for which the code is available on Github.
  • His approach to benchmarking this project.
  • Stephen’s goals for this research in the broader NLP research community. 

The complete show notes for this episode can be found at twimlai.com/talk/325. There you’ll find links to both the paper referenced in this interview, and the code. Enjoy!

Dec 12 2019

59mins

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Automated Model Tuning with SigOpt - #324

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In this TWIML Democast, we're joined by SigOpt Co-Founder and CEO Scott Clark. Scott details the SigOpt platform, and gives us a live demo!

This episode is best consumed by watching the corresponding video demo, which you can find at twimlai.com/talk/324

Dec 09 2019

46mins

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Automated Machine Learning with Erez Barak - #323

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In the final episode of our Azure ML series, we’re joined by Erez Barak, Partner Group Manager of Azure ML at Microsoft. In our conversation, we discuss:

  • Erez’s AutoML philosophy, including how he defines “true AutoML” and his take on the AutoML space, its role and its importance.
  • We also discuss in great detail the application of AutoML as a contributor to the end-to-end data science process, which Erez breaks down into 3 key areas; Featurization, Learner/Model Selection, and Tuning/Optimizing Hyperparameters.
  • Finally, we discuss post-deployment AutoML use cases and other areas under the AutoML umbrella that are currently generating excitement.

Check out the complete show notes at twimlai.com/talk/323!

Dec 06 2019

43mins

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Responsible AI in Practice with Sarah Bird - #322

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Today we continue our Azure ML at Microsoft Ignite series joined by Sarah Bird, Principal Program Manager at Microsoft. In our conversation, we discuss:

  • Sarah’s work in machine learning systems, with a focus on bringing machine learning research into production through Azure ML, with an emphasis on responsible AI.
  • A set of newly released tools focused on responsible machine learning, Azure Machine Learning 'Machine Learning Interpretability Toolkit’
  • Moving from “Black-Box” models to “Glass-Box Models”
  • Sarah’s recent work in differential privacy, including risks and benefits
  • Her work in the broader ML community, including being a founding member of the MLSys conference and workshops.

Check out the complete show notes at twimlai.com/talk/322.

Dec 04 2019

38mins

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Enterprise Readiness, MLOps and Lifecycle Management with Jordan Edwards - #321

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Today we’re joined by Jordan Edwards, Principal Program Manager for MLOps on Azure ML at Microsoft. In our conversation, Jordan details:

  • How Azure ML accelerates model lifecycle management with MLOps, enabling data scientists to collaborate with IT teams to increase the pace of model development and deployment.
  • Problems associated with generalizing ML at scale at Microsoft, and how those problems are prioritized, 
  • What is MLOps, and the role of testing is in an MLOps environment, and experiences working with customers to implement these tests. 
  • The “four phases” along the journey of customer implementation of MLOps, how companies should look at hiring ML Engineers vs DevOps Engineers, and other aspects of managing model life cycles that Jordan finds important for us to think about. 

The complete show notes can be found at twimlai.com/talk/321

Dec 02 2019

39mins

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DevOps for ML with Dotscience - #320

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Today we’re joined by Luke Marsden, Founder and CEO of Dotscience. Luke walks us through the Dotscience platform and their manifesto on DevOps for ML.

Thanks to Luke and Dotscience for their sponsorship of this Democast and their continued support of TWIML.  

Head to https://twimlai.com/democast/dotscience to watch the full democast!

Nov 26 2019

47mins

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Building an Autonomous Knowledge Graph with Mike Tung - #319

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Today we’re joined by Mike Tung, Founder, and CEO of Diffbot. In our conversation, we discuss: 

  • Their various tools, including their Knowledge Graph, Extraction API, and CrawlBot.
  • How Knowledge Graph was inspired by Imagenet, how it was built, and how it differs from other, more mainstream knowledge graphs like Google Search and MSFT Bing.
  • How they balance being a research company that is also commercially viable.
  • The developer experience with their tools, and challenges faced.

The complete show notes can be found at twimlai.com/talk/319.

Nov 21 2019

44mins

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The Next Generation of Self-Driving Engineers with Aaron Ma - Talk #318

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Today we’re joined by our youngest guest ever (by far), Aaron Ma, an 11-year-old middle school student and machine learning engineer in training. Aaron has completed over 80(!) Coursera courses and is the recipient of 3 Udacity nano-degrees. In our conversation, we discuss:

  • Aaron’s research interests, reinforcement learning, and self-driving cars,
  • His experiences participating in over 35 kaggle competitions
  • How he balances his passion for machine learning with things like chores and homework.

This was a really fun interview! 

The complete show notes for this episode can be found at twimlai.com/talk/318.

Nov 18 2019

47mins

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Spiking Neural Networks: A Primer with Terrence Sejnowski - #317

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On today’s episode, we’re joined by Terrence Sejnowski, Francis Crick Chair, head of the Computational Neurobiology Laboratory at the Salk Institute for Biological Studies and faculty member at UC San Diego. In our conversation with Terry, we discuss:

  • His role as a founding researcher in the field of computational neuroscience, and as a founder of the annual Telluride Neuromorphic Cognition Engineering Workshop. 
  • We dive deep into the world of spiking neural networks and brain architecture,
  • the relationship of neuroscience to machine learning, and ways to make NN’s more efficient through spiking. 
  • Terry also gives us some insight into hardware used in this field, characterizes the major research problems currently being undertaken, and the future of spiking networks. 

Check out the complete show notes at twimlai.com/talk/317.

Nov 14 2019

49mins

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Bridging the Patient-Physician Gap with ML and Expert Systems w/ Xavier Amatriain - #316

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Today we’re joined by return guest Xavier Amatriain, Co-founder and CTO of Curai. In our conversation, we discuss

  • Curai’s goal of providing the world’s best primary care to patients via their smartphone, and how ML & AI will bring down costs healthcare accessible and scaleable. 
  • The shortcomings of traditional primary care, and how Curai fills that role, 
  • Some of the unique challenges his team faces in applying this use case in the healthcare space. 
  • Their use of expert systems, how they develop and train their models with synthetic data through noise injection
  • How NLP projects like BERT, Transformer, and GPT-2 fit into what Curai is building. 

Check out the complete show notes page at twimlai.com/talk/316

Nov 11 2019

39mins

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What Does it Mean for a Machine to "Understand"? with Thomas Dietterich - #315

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Today we’re joined by Tom Dietterich, Distinguished Professor Emeritus at Oregon State University. We had the pleasure of discussing Tom’s recent blog post, “What does it mean for a machine to “understand,” in which he discusses:

  • Tom’s position on what qualifies as machine “understanding”, including a few examples of systems that he believes exhibit understanding.
  • The role of deep learning in achieving artificial general intelligence.
  • The current “Hype Engine” that exists around AI Research, and SOOO much more.  

Make sure you check out the show notes at twimlai.com/talk/315, where you’ll find links to Tom’s blog post, as well as a ton of other references. 

Nov 07 2019

38mins

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Scaling TensorFlow at LinkedIn with Jonathan Hung - #314

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Today we’re joined by Jonathan Hung, Sr. Software Engineer at LinkedIn, who we caught up with at TensorFlow World last week. In our conversation, we discuss: 

  • Jonathan’s presentation at the event focused on LinkedIn’s efforts scaling Tensorflow.
  • Jonathan’s work as part of the Hadoop infrastructure team, including experimenting on Hadoop with various frameworks, and their motivation for using TensorFlow on their pre-existing Hadoop clusters infrastructure. 
  • TonY, or TensorFlow on Yard, LinkedIn’s framework that natively runs deep learning jobs on Hadoop, and its relationship with Pro-ML, LinkedIn’s internal AI Platform, which we’ve discussed on earlier episodes of the podcast (Link).
  • Finally, we discuss how far LinkedIn’s Hadoop infrastructure has come since 2017, and their foray into using Kubernetes for research. 

The complete show notes can be found at twimlai.com/talk/314.

Nov 04 2019

35mins

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Machine Learning at GitHub with Omoju Miller - #313

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Today we’re joined by Omoju Miller, a Sr. machine learning engineer at GitHub. In our conversation, we discuss:

  • Her dissertation, Hiphopathy, A Socio-Curricular Study of Introductory Computer Science, 
  • Her work as an inaugural member of the Github machine learning team
  • Her two presentations at Tensorflow World, “Why is machine learning seeing exponential growth in its communities” and “Automating your developer workflow on GitHub with Tensorflow.”

The complete show notes for this episode can be found at twimlai.com/talk/313

Oct 31 2019

43mins

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Using AI to Diagnose and Treat Neurological Disorders with Archana Venkataraman - #312

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Today we’re joined by Archana Venkataraman, John C. Malone Assistant Professor of Electrical and Computer Engineering at Johns Hopkins University, and MIT 35 innovators under 35 recipient.

Archana’s research at the Neural Systems Analysis Laboratory focuses on developing tools, frameworks, and algorithms to better understand, and treat neurological and psychiatric disorders, including autism, epilepsy, and others. In our conversation, we explore her lab’s work in applying machine learning to these problems, including biomarker discovery, disorder severity prediction, as well as some of the various techniques and frameworks used.

The complete show notes for this episode can be found at twimlai.com/talk/312.

Oct 28 2019

47mins

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Deep Learning for Earthquake Aftershock Patterns with Phoebe DeVries & Brendan Meade - #311

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Today we are joined by Phoebe DeVries, Postdoctoral Fellow in the Department of Earth and Planetary Sciences at Harvard and assistant faculty at the University of Connecticut and Brendan Meade, Professor of Earth and Planetary Sciences and affiliate faculty in computers sciences at Harvard. In this episode, we discuss:

  • Phoebe and Brendan’s work is focused on discovering as much as possible about earthquakes before they happen, and through measuring how the earth’s surface moves, predicting future movement location
  • Their recent paper, ‘Deep learning of aftershock patterns following large earthquakes’, and 
  • The preliminary steps that guided them to using machine learning in the earth sciences
  • Their current research involving calculating stress changes in the crust and upper mantle after a large earthquake and using a neural network to map those changes to predict aftershock locations
  • The complex systems that encompass earth science studies, including the approaches, challenges, surprises, and results that come with incorporating machine learning models and data sets into a new field of study

The complete show notes for this episode can be found at twimlai.com/talk/311.

Oct 25 2019

35mins

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Live from TWIMLcon! Operationalizing Responsible AI - #310

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An often forgotten about topic garnered high praise at TWIMLcon this month: operationalizing responsible and ethical AI. This important topic was combined with an impressive panel of speakers, including: Rachel Thomas, Director, Center for Applied Data Ethics at the USF Data Institute, Guillaume Saint-Jacques, Head of Computational Science at LinkedIn, and Parinaz Sobahni, Director of Machine Learning at Georgian Partners, moderated by Khari Johnson, Senior AI Staff Writer at VentureBeat. This episode covers:

  • The basics of operationalizing AI ethics in a range of orgs and insight into an array of tools, approaches, and methods that have been found useful for teams to use
  • The biggest concerns, like focusing more on harm as opposed to algorithmic bias and encouraging specific responsibility for systems
  • Educating the general public on the realities and misconceptions of probabilistic methods and putting into place preventative guardrails has become imperative for any operation
  • The long-term benefits of ethical decision-making and the challenges of established versus startup companies
  • Questions from the TWIMLcon audience, some common examples of power dynamics in AI ethics, and what we as a community can be doing to push the needle in the very powerful world of responsible AI

The complete show notes can be found at twimlai.com/talk/310

Oct 22 2019

30mins

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Live from TWIMLcon! Scaling ML in the Traditional Enterprise - #309

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In this episode from a stellar TWIMLcon panel, the state and future of larger, more established brands is analyzed and discussed. Hear from Amr Awadallah, Founder and Global CTO of Cloudera, Pallav Agrawal, Director of Data Science at Levi Strauss & Co., and Jürgen Weichenberger, Data Science Senior Principal & Global AI Lead at Accenture, moderated by Josh Bloom Professor at UC Berkeley. In this episode we discuss:

  • For an ML/AI initiative to be successful, a conscious and noticeable shift is now required in how things used to be managed while educating cross-functional teams in data science terms and methodologies 
  • It can be tempting and exciting to constantly be trying out the latest technologies, but brand consistency and sustainability is imperative to success
  • How the real business value - the money - can be found by putting your big ML/AI goals and projects in the core competencies of the company.  
  • Are traditional enterprises fundamentally changing their business through ML/AI, and if so, why? 
  • Real-world examples and thought-provoking ideas for scaling ML/AI in the traditional enterprise

The complete show notes can be found at twimlai.com/talk/309.

Oct 18 2019

33mins

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Live from TWIMLcon! Culture & Organization for Effective ML at Scale (Panel) - #308

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TWIMLcon brought together so many in the ML/AI community to discuss the unique challenges to building and scaling machine learning platforms. In this episode, hear from a diverse set of panelists including: Pardis Noorzad, Data Science Manager at Twitter, Eric Colson, Chief Algorithms Officer Emeritus at Stitch Fix, and Jennifer Prendki, Founder & CEO at Alectio, moderated by Maribel Lopez, Founder & Principal Analyst at Lopez Research:

  • How to approach changing the way companies think about machine learning
  • Engaging different groups to work together effectively - i.e. c-suite, marketing, sales, engineering, etc. 
  • The importance of clear communication about ML lifecycle management
  • How full stack roles can provide immense value
  • Tips and tricks to work faster, more efficiently, and create an org-wide culture that holds machine learning as a valued priority

The complete show notes can be found at twimlai.com/talk/308.

Oct 15 2019

27mins

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Live from TWIMLcon! Use-Case Driven ML Platforms with Franziska Bell - #307

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Franziska Bell, Ph.D., is the Director of Data Science Platforms at Uber, and joined Sam on stage at TWIMLcon last week to discuss all things platform at Uber. With the goal of providing cutting edge data science company-wide at the push of a button, Fran has developed a portfolio of platforms, ranging from forecasting to anomaly detection to conversational AI. In this episode, we discuss:

  • Through strategic use cases, Fran’s team of data scientists works closely with teams across the organization at every stage to solve problems and build infrastructure
  • The evolving working relationship between her team and Michelangelo (Uber’s ML Platform), including the challenges and benefits that such a platform provides
  • Insight into Uber’s development methodology and how the data science team is organized from start to finish to create a culture of learning and expertise that results in fast results and reduced risk
  • Fran’s take on the future of ML platforms and more!

Check out the complete show notes at twimlai.com/talk/307

Oct 10 2019

32mins

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Live from TWIMLcon! Operationalizing ML at Scale with Hussein Mehanna - #306

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The live interviews from TWIMLcon continue with Hussein Mehanna, Head of Machine Learning and Artificial Intelligence at Cruise. From his start at Facebook and then Google and now to Cruise, leading the trend of autonomous vehicles, Hussein has seen first hand what it takes to scale and sustain machine learning programs. In this episode, hear him and Sam discuss:

  • At Facebook, a few early wins in the realm of infrastructure building set the stage for scaling via faster algorithms and soon the entire Facebook organization could achieve a new level of ML scaling with all workflows shareable, reusable and discoverable through a search interface
  • Cruise’s unique focus on the interplay between applied research problems and the underlying tools and platforms
  • The immense capacity that the industry of autonomous vehicles has to push ML and AI to new limits of depth and scale
  • The challenges (and joys) of working in the industry and his insight into analyzing scale when innovation is happening in parallel with the development
  • Hussein’s experiences at Facebook, Google, and Cruise, along with his thoughts on productivity being a "usability" vs "modeling" challenge and his prediction for the future of ML platforms!

The complete show notes can be found at twimlai.com/talk/306.

Oct 08 2019

33mins

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