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

Author: Sam Charrington

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This Week in Machine Learning & AI is the most popular podcast of its kind, catering to a highly-targeted audience of machine learning & AI enthusiasts. They are data scientists, developers, founders, CTOs, engineers, architects, IT & product leaders, as well as tech-savvy business leaders. These creators, builders, makers and influencers value TWiML as an authentic, trusted and insightful guide to all that’s interesting and important in the world of machine learning and AI.
Technologies covered include: machine learning, artificial intelligence, deep learning, natural language processing, neural networks, analytics, deep learning and more.
297 Episodes
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Today we’re joined by Michael Levin, Director of the Allen Discovery Institute at Tufts University. Michael joined us back at NeurIPS to discuss his invited talk “What Bodies Think About: Bioelectric Computation Beyond the Nervous System as Inspiration for New Machine Learning Platforms.” In our conversation, we talk about: Synthetic living machines, novel AI architectures and brain-body plasticity How our DNA doesn’t control everything like we thought and how the behavior of cells in living organisms can be modified and adapted Biological systems dynamic remodeling in the future of developmental biology and regenerative medicine...and more! The complete show notes for this episode can be found at twimlai.com/talk/282.  Register for TWIMLcon: AI Platforms now at twimlcon.com!
Today we’re joined by Batu Arisoy, Research Manager with the Vision Technologies & Solutions team at Siemens Corporate Technology. Currently, Batu’s research focus is solving limited data computer vision problems, providing R&D for many of the business units throughout the company. In our conversation we discuss: An emulation of a teacher teaching students information without the use of memorization Discerning which parts of our neural network are required to make decisions An activity recognition project with the Office of Naval Research that keeps ‘humans in the loop’ and more.  The complete show notes for this episode can be found at twimlai.com/talk/281.  Register for TWIMLcon: AI Platforms now at twimlcon.com! Thanks to Siemens for their sponsorship of today's episode! Check out what they’re up to, visit twimlai.com/siemens.
Today we’re joined by Jeff Gehlhaar, VP of Technology and Head of AI Software Platforms at Qualcomm. As we’ve explored in our conversations with both Gary Brotman and Max Welling, Qualcomm has a hand in tons of machine learning research and hardware, and our conversation with Jeff is no different. We discuss: • How the various training frameworks fit into the developer experience when working with their chipsets. • Examples of federated learning in the wild. • The role inference will play in data center devices and more. The complete show notes for this episode can be found at twimlai.com/talk/280.  Register for TWIMLcon now at twimlcon.com. Thanks to Qualcomm for their sponsorship of today's episode! Check out what they're up to at twimlai.com/qualcomm.
Today we’re joined by return guest Daniel Jeavons, GM of Data Science at Shell, and Adi Bhashyam, GM of Data Science at C3, who we had the pleasure of speaking to at this years C3 Transform Conference. In our conversation, we discuss: • The progress that Dan and his team has made since our last conversation, including an overview of their data platform. • We explore the various types of users of the platform, and how those users informed the decision to use C3’s out-of-the-box platform solution instead of building their own internal platform. • Adi gives us an overview of the evolution of C3 and their platform, along with a breakdown of a few Shell-specific use cases.  The complete show notes can be found at twimlai.com/talk/279. Visit twimlcon.com to learn more about the TWIMLcon: AI Platforms conference! Early-bird registration has been extended until this Wednesday, 7/3, register today for the lowest possible price!!
Today we’re joined by Yunfan Gerry Zhang, a PhD student in the Department of Astrophysics at UC Berkely, and an affiliate of Berkeley’s SETI research center. In our conversation, we discuss:  • Gerry's research on applying machine learning techniques to astrophysics and astronomy. • His paper “Fast Radio Burst 121102 Pulse Detection and Periodicity: A Machine Learning Approach”. • We explore the types of data sources used for this project, challenges Gerry encountered along the way, the role of GANs and much more. The complete show notes can be found at twimlai.com/talk/278. Visit twimlcon.com to learn more about the TWIMLcon: AI Platforms conference! Early-bird registration ends TOMORROW 6/28! Register now!
Today we’re joined by Laurence Watson, Co-Founder and CTO of Plentiful Energy and a former data scientist at Carbon Tracker. In our conversation, we discuss: • Carbon Tracker's goals, and their report “Nowhere to hide: Using satellite imagery to estimate the utilisation of fossil fuel power plants”. • How they're using computer vision to process satellite images of coal plants, including how the images are labeled •Various challenges with the scope and scale of this project, including dealing with varied time zones and imbalanced training classes. The complete show notes can be found at twimlai.com/talk/277. Visit twimlcon.com to learn more about the TWIMLcon: AI Platforms conference! Early-bird registration ends on 6/28!
Today we’re joined by William Fehlman, director of data science at USAA. We caught up with William a while back to discuss: His work on topic modeling, which USAA uses in various scenarios, including chat channels with members via mobile and desktop interfaces. How their datasets are generated. Explored methodologies of topic modeling, including latent semantic indexing, latent Dirichlet allocation, and non-negative matrix factorization. We also explore how terms are represented via a document-term matrix, and how they are scored based on coherence. The complete show notes can be found at twimlai.com/talk/276. Visit twimlcon.com to learn more about the TWIMLcon: AI Platforms conference! Early-bird registration ends on 6/28!
Today we’re joined by Judy Gichoya an interventional radiology fellow at the Dotter Institute at Oregon Health and Science University. In our conversation, we discuss: • Judy's research in “Phronesis of AI in Radiology: Superhuman meets Natural Stupidy,” reviewing the claims of “superhuman” AI performance in radiology. • We explore potential roles in which AI can have success in radiology, along with some of the different types of biases that can manifest themselves across multiple use cases. • We look at the CheXNet paper, which details how human and AI performance can complement and improve each other's performance for detecting pneumonia in chest X-rays. The complete show notes can be found at twimlai.com/talk/275. Visit twimlcon.com to learn more about the TWIMLcon: AI Platforms conference! 
Today we’re joined by Karen Levy, assistant professor in the department of information science at Cornell University. Karen’s research focuses on how rules and technologies interact to regulate behavior, especially the legal, organizational, and social aspects of surveillance and monitoring. In our conversation we discuss: • Examples of how data tracking and surveillance can be used in ways that can be abusive to various marginalized groups, including detailing her extensive research into truck driver surveillance. • Her thoughts on how the broader society will react to the increase in surveillance, • The unintended consequences of surveillant systems, questions surrounding hybridization of jobs and systems, and more! The complete show notes can be found at twimlai.com/talk/274. Visit twimlcon.com to learn more about the TWIMLcon: AI Platforms conference! 
Today we’re joined by Matt Adereth, managing director of investments at Two Sigma, and return guest Scott Clark, co-founder and CEO of SigOpt, to discuss: • The end to end modeling platform at Two Sigma, who it serves, and challenges faced in production and modeling. • How Two Sigma has attacked the experimentation challenge with their platform. • The relationship between the optimization and infrastructure teams at SigOpt. • What motivates companies that aren’t already heavily invested in platforms, optimization or automation, to do so. The complete show notes can be found at twimlai.com/talk/273. Visit twimlcon.com to learn more about the TWIMLcon: AI Platforms conference! The first 10 listeners who register get their ticket for 75% off using the discount code TWIMLFIRST! Follow along with the entire AI Platforms Vol 2 series at twimlai.com/aiplatforms2. Thanks to SigOpt for their continued support of the podcast, and their sponsorship of this episode! Check out their machine learning experimentation and optimization suite, and get a free trial at twimlai.com/sigopt.
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Comments (9)

Glory Dey

I love this channel and all the great podcasts. The topics are very relevant and the speakers are well informed experts so the episodes are very educative. Only request, please change the opening music note of the podcast. It is very unpleasant tune sets a jarring effect right at the beginning. Otherwise all these episodes are very interesting in the field of innovations in Artificial Intelligence and Machine Learning! Regards!

Jun 25th
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Billy Bloomer

so smart you can smell it

Jun 14th
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raqueeb shaikh

great podcast

May 31st
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Loza Boza

Phenomenal discussion. Thank you! Particularly enjoyed the parts on generative models and the link to Daniel Kahneman.

May 20th
Reply

simon abdou

Horrible Audio

May 9th
Reply

Özgür Yüksel

This is a very realistic and proper episode which explains quantum computing even as alone.

Apr 9th
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Naadodi

Hello all, Thanks for podcast Can we combine the two agent learnings from same environment to find the best actions Thanks

Mar 14th
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Bhavul Gauri

notes : * Data scientists are not trained to think of money optimisations. plotting cpu usage vs accuracy gives an idea about it. if u increase data 4x as much just to gain 1% increase in accuracy that may not be great because you're using 4 times as much CPU power * a team just decicated to monitoring. i. monitor inputs : should not go beyond a certain range for each feature that you are supposed to have. Nulls ratio shouldn't change by a lot. ii. monitor both business and model metrics. sometimes even if model metrics get better ur business metrics could go low....and this could be the case like better autocompletion makes for low performance spell check OR it could also depend upon other things that have changed. or seasonality. * Data scientists and ML engineers in pairs. ML Engineers get to learn about the model while Data Scientists come up with it. both use same language. ML Engineers make sure it gets scaled up and deployed to production. * Which parameters are somewhat stable no matter how many times you retrain vs what parameters are volatile. the volatile ones could cause drastic changes. so u can reverse engineer this way.

Mar 11th
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Khaled Zamer

Super.. very informative. Thanks

Aug 26th
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