The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
717 episodes - English - Latest episode: about 1 month ago - ★★★★★ - 323 ratingsMachine 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, computer science, data science and more.
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Episodes
How AI Predicted the Coronavirus Outbreak with Kamran Khan - #350
February 19, 2020 18:31 - 51 minutesToday we’re joined by Kamran Khan, founder & CEO of BlueDot, and professor of medicine and public health at the University of Toronto. BlueDot has been the recipient of a lot of attention for being the first to publicly warn about the coronavirus that started in Wuhan. How did the company’s system of algorithms and data processing techniques help flag the potential dangers of the disease? In our conversation, Kamran talks us through how the technology works, its limits, and the motivation beh...
Turning Ideas into ML Powered Products with Emmanuel Ameisen - #349
February 17, 2020 22:02 - 42 minutesToday we’re joined by Emmanuel Ameisen, machine learning engineer at Stripe, and author of the recently published book “Building Machine Learning Powered Applications; Going from Idea to Product.” In our conversation, we discuss structuring end-to-end machine learning projects, debugging and explainability in the context of models, the various types of models covered in the book, and the importance of post-deployment monitoring.
Algorithmic Injustices and Relational Ethics with Abeba Birhane - #348
February 13, 2020 20:53 - 41 minutesToday we’re joined by Abeba Birhane, PhD Student at University College Dublin and author of the recent paper Algorithmic Injustices: Towards a Relational Ethics, which was the recipient of the Best Paper award at the 2019 Black in AI Workshop at NeurIPS. In our conversation, break down the paper and the thought process around AI ethics, the “harm of categorization,” how ML generally doesn’t account for the ethics of various scenarios and how relational ethics could solve the issue, and much m...
AI for Agriculture and Global Food Security with Nemo Semret - #347
February 10, 2020 20:29 - 1 hourToday we’re excited to kick off our annual Black in AI Series joined by Nemo Semret, CTO of Gro Intelligence. Gro provides an agricultural data platform dedicated to improving global food security, focused on applying AI at macro scale. In our conversation with Nemo, we discuss Gro’s approach to data acquisition, how they apply machine learning to various problems, and their approach to modeling.
Practical Differential Privacy at LinkedIn with Ryan Rogers - #346
February 07, 2020 19:39 - 33 minutesToday we’re joined by Ryan Rogers, Senior Software Engineer at LinkedIn, to discuss his paper “Practical Differentially Private Top-k Selection with Pay-what-you-get Composition.” In our conversation, we discuss how LinkedIn allows its data scientists to access aggregate user data for exploratory analytics while maintaining its users’ privacy through differential privacy, and the connection between a common algorithm for implementing differential privacy, the exponential mechanism, and Gumbel...
Networking Optimizations for Multi-Node Deep Learning on Kubernetes with Erez Cohen - #345
February 05, 2020 17:33 - 31 minutesToday we conclude the KubeCon ‘19 series joined by Erez Cohen, VP of CloudX & AI at Mellanox, who we caught up with before his talk “Networking Optimizations for Multi-Node Deep Learning on Kubernetes.” In our conversation, we discuss NVIDIA’s recent acquisition of Mellanox, the evolution of technologies like RDMA and GPU Direct, how Mellanox is enabling Kubernetes and other platforms to take advantage of the recent advancements in networking tech, and why we should care about networking in D...
Managing Research Needs at the University of Michigan using Kubernetes w/ Bob Killen - #344
February 03, 2020 16:38 - 25 minutesToday we’re joined by Bob Killen, Research Cloud Administrator at the University of Michigan. In our conversation, we explore how Bob and his group at UM are deploying Kubernetes, the user experience, and how those users are taking advantage of distributed computing. We also discuss if ML/AI focused Kubernetes users should fear that the larger non-ML/AI user base will negatively impact their feature needs, where gaps currently exist in trying to support these ML/AI users’ workloads, and more!
Scalable and Maintainable Workflows at Lyft with Flyte w/ Haytham AbuelFutuh and Ketan Umare - #343
January 30, 2020 19:30 - 45 minutesToday we kick off our KubeCon ‘19 series joined by Haytham AbuelFutuh and Ketan Umare, a pair of software engineers at Lyft. We caught up with Haytham and Ketan at KubeCo, where they were presenting their newly open-sourced, cloud-native ML and data processing platform, Flyte. We discuss what prompted Ketan to undertake this project and his experience building Flyte, the core value proposition, what type systems mean for the user experience, how it relates to Kubeflow and how Flyte is used ac...
Causality 101 with Robert Osazuwa Ness - #342
January 27, 2020 20:30 - 39 minutesToday Robert Osazuwa Ness, ML Research Engineer at Gamalon and Instructor at Northeastern University joins us to discuss Causality, what it means, and how that meaning changes across domains and users, and our upcoming study group based around his new course sequence, “Causal Modeling in Machine Learning," for which you can find details at twimlai.com/community.
PaccMann^RL: Designing Anticancer Drugs with Reinforcement Learning w/ Jannis Born - #341
January 23, 2020 17:06 - 42 minutesToday we’re joined by Jannis Born, Ph.D. student at ETH & IBM Research Zurich, to discuss his “PaccMann^RL” research. Jannis details how his background in computational neuroscience applies to this research, how RL fits into the goal of anticancer drug discovery, the effect DL has had on his research, and of course, a step-by-step walkthrough of how the framework works to predict the sensitivity of cancer drugs on a cell and then discover new anticancer drugs.
Social Intelligence with Blaise Aguera y Arcas - #340
January 20, 2020 19:56 - 47 minutesToday we’re joined by Blaise Aguera y Arcas, a distinguished scientist at Google. We had the pleasure of catching up with Blaise at NeurIPS last month, where he was invited to speak on “Social Intelligence.” In our conversation, we discuss his role at Google, and his team’s approach to machine learning, and of course his presentation, in which he touches discussing today’s ML landscape, the gap between AI and ML/DS, the difference between intelligent systems and true intelligence, and much more.
Music & AI Plus a Geometric Perspective on Reinforcement Learning with Pablo Samuel Castro - #339
January 16, 2020 19:27 - 44 minutesToday we’re joined by Pablo Samuel Castro, Staff Research Software Developer at Google. We cover a lot of ground in our conversation, including his love for music, and how that has guided his work on the Lyric AI project, and a few of his papers including “A Geometric Perspective on Optimal Representations for Reinforcement Learning” and “Estimating Policy Functions in Payments Systems using Deep Reinforcement Learning.”
Trends in Computer Vision with Amir Zamir - #338
January 13, 2020 23:10 - 1 hourToday we close out AI Rewind 2019 joined by Amir Zamir, who recently began his tenure as an Assistant Professor of Computer Science at the Swiss Federal Institute of Technology. Amir joined us back in 2018 to discuss his CVPR Best Paper winner, and in today’s conversation, we continue with the thread of Computer Vision. In our conversation, we discuss quite a few topics, including Vision-for-Robotics, the expansion of the field of 3D Vision, Self-Supervised Learning for CV Tasks, and much more!
Trends in Natural Language Processing with Nasrin Mostafazadeh - #337
January 09, 2020 22:33 - 1 hourToday we continue the AI Rewind 2019 joined by friend-of-the-show Nasrin Mostafazadeh, Senior AI Research Scientist at Elemental Cognition. We caught up with Nasrin to discuss the latest and greatest developments and trends in Natural Language Processing, including Interpretability, Ethics, and Bias in NLP, how large pre-trained models have transformed NLP research, and top tools and frameworks in the space.
Trends in Fairness and AI Ethics with Timnit Gebru - #336
January 06, 2020 20:02 - 49 minutesToday we keep the 2019 AI Rewind series rolling with friend-of-the-show Timnit Gebru, a research scientist on the Ethical AI team at Google. A few weeks ago at NeurIPS, Timnit joined us to discuss the ethics and fairness landscape in 2019. In our conversation, we discuss diversification of NeurIPS, with groups like Black in AI, WiML and others taking huge steps forward, trends in the fairness community, quite a few papers, and much more.
Trends in Reinforcement Learning with Chelsea Finn - #335
January 02, 2020 19:59 - 1 hourToday we continue to review the year that was 2019 via our AI Rewind series, and do so with friend of the show Chelsea Finn, Assistant Professor in the CS Department at Stanford University. Chelsea’s research focuses on Reinforcement Learning, so we couldn’t think of a better person to join us to discuss the topic. In this conversation, we cover topics like Model-based RL, solving hard exploration problems, along with RL libraries and environments that Chelsea thought moved the needle last year.
Trends in Machine Learning & Deep Learning with Zack Lipton - #334
December 30, 2019 19:23 - 1 hourToday we kick off our 2019 AI Rewind Series joined by Zack Lipton, Professor at CMU. You might remember Zack from our conversation earlier this year, “Fairwashing” and the Folly of ML Solutionism. In today's conversation, Zack recaps advancements across the vast fields of Machine Learning and Deep Learning, including trends, tools, research papers and more. We want to hear from you! Send your thoughts on the year that was 2019 below in the comments, or via Twitter @samcharrington or @twimlai.
FaciesNet & Machine Learning Applications in Energy with Mohamed Sidahmed - #333
December 27, 2019 20:08 - 39 minutesToday we close out our 2019 NeurIPS series with Mohamed Sidahmed, Machine Learning and Artificial Intelligence R&D Manager at Shell. In our conversation, we discuss two papers Mohamed and his team submitted to the conference this year, Accelerating Least Squares Imaging Using Deep Learning Techniques, and FaciesNet: Machine Learning Applications for Facies Classification in Well Logs. The show notes for this episode can be found at twimlai.com/talk/333/, where you’ll find links to both of the...
Machine Learning: A New Approach to Drug Discovery with Daphne Koller - #332
December 26, 2019 18:41 - 43 minutesToday we’re joined by Daphne Koller, co-Founder and former co-CEO of Coursera and Founder and CEO of Insitro. In our conversation, discuss the current landscape of pharmaceutical drugs and drug discovery, including the current pricing of drugs, and an overview of Insitro’s goal of using ML as a “compass” in drug discovery. We also explore how Insitro functions as a company, their focus on the biology of drug discovery and the landscape of ML techniques being used, Daphne’s thoughts on AutoML,...
Sensory Prediction Error Signals in the Neocortex with Blake Richards - #331
December 24, 2019 18:55 - 40 minutesToday we continue our 2019 NeurIPS coverage, this time around joined by Blake Richards, Assistant Professor at McGill University and a Core Faculty Member at Mila. Blake was an invited speaker at the Neuro-AI Workshop, and presented his research on “Sensory Prediction Error Signals in the Neocortex.” In our conversation, we discuss a series of recent studies on two-photon calcium imaging. We talk predictive coding, hierarchical inference, and Blake’s recent work on memory systems for reinforc...
How to Know with Celeste Kidd - #330
December 23, 2019 18:46 - 53 minutesToday we’re joined by Celeste Kidd, Assistant Professor at UC Berkeley, to discuss her invited talk “How to Know” which details her lab’s research about the core cognitive systems people use to guide their learning about the world. We explore why people are curious about some things but not others, and how past experiences and existing knowledge shape future interests, why people believe what they believe, and how these beliefs are influenced, and how machine learning figures into the equation.
Using Deep Learning to Predict Wildfires with Feng Yan - #329
December 20, 2019 22:17 - 51 minutesToday we’re joined by Feng Yan, Assistant Professor at the University of Nevada, Reno to discuss ALERTWildfire, a camera-based network infrastructure that captures satellite imagery of wildfires. In our conversation, Feng details the development of the machine learning models and surrounding infrastructure. We also talk through problem formulation, challenges with using camera and satellite data in this use case, and how he has combined the use of IaaS and FaaS tools for cost-effectiveness an...
Advancing Machine Learning at Capital One with Dave Castillo - #328
December 19, 2019 16:56 - 47 minutesToday we’re joined by Dave Castillo, Managing VP for ML at Capital One and head of their Center for Machine Learning. In our conversation, we explore Capital One’s transition from “lab-based” ML to enterprise-wide adoption and support of ML, surprising ML use cases, their current platform ecosystem, their design vision in building this into a larger, all-encompassing platform, pain points in building this platform, and much more.
Helping Fish Farmers Feed the World with Deep Learning w/ Bryton Shang - #327
December 17, 2019 17:00 - 37 minutesToday we’re joined by Bryton Shang, Founder & CEO at Aquabyte, a company focused on the application of computer vision to various fish farming use cases. In our conversation, we discuss how Bryton identified the various problems associated with mass fish farming, challenges developing computer algorithms that can measure the height and weight of fish, assess issues like sea lice, and how they’re developing interesting new features such as facial recognition for fish!
Metaflow, a Human-Centric Framework for Data Science with Ville Tuulos - #326
December 13, 2019 20:56 - 56 minutesToday we kick off our re:Invent 2019 series with Ville Tuulos, Machine Learning Infrastructure Manager at Netflix. At re:Invent, Netflix announced the open-sourcing of Metaflow, their “human-centric framework for data science.” In our conversation, we discuss all things Metaflow, including features, user experience, tooling, supported libraries, and much more. If you’re interested in checking out a Metaflow democast with Villa, reach out at twimlai.com/contact!
Single Headed Attention RNN: Stop Thinking With Your Head with Stephen Merity - #325
December 12, 2019 19:04 - 59 minutesToday we’re joined by Stephen Merity, an independent researcher focused on NLP and Deep Learning. In our conversation, we discuss Stephens latest paper, Single Headed Attention RNN: Stop Thinking With Your Head, detailing his primary motivations behind the paper, the decision to use SHA-RNNs for this research, how he built and trained the model, his approach to benchmarking, and finally his goals for the research in the broader research community.
Automated Model Tuning with SigOpt - #324
December 09, 2019 20:43 - 46 minutesIn 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.
Platform Optimization with SigOpt - #324
December 09, 2019 20:43 - 46 minutes - 63.5 MBIn 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.
Automated Machine Learning with Erez Barak - #323
December 06, 2019 16:32 - 42 minutesToday we’re joined by Erez Barak, Partner Group Manager of Azure ML at Microsoft. In our conversation, Erez gives us a full breakdown of his AutoML philosophy, and his take on the AutoML space, its role, and its importance. We also discuss 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. We also discuss post-deployment AutoML use cases, and ...
Responsible AI in Practice with Sarah Bird - #322
December 04, 2019 16:10 - 38 minutesToday we continue our Azure ML at Microsoft Ignite series joined by Sarah Bird, Principal Program Manager at Microsoft. At Ignite, Microsoft released new tools focused on responsible machine learning, which fall under the umbrella of the Azure ML 'Machine Learning Interpretability Toolkit.’ In our conversation, Sarah walks us this toolkit, detailing use cases and the user experience. We also discuss her work in differential privacy, and in the broader ML community, in particular, the MLSys co...
Enterprise Readiness, MLOps and Lifecycle Management with Jordan Edwards - #321
December 02, 2019 16:24 - 39 minutesToday 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, which enables data scientists to collaborate with IT teams to increase the pace of model development and deployment. We discuss various problems associated with generalizing ML at scale at Microsoft, what exactly MLOps is, the “four phases” along the journey of customer implementation of MLOps, a...
DevOps for ML with Dotscience - #320
November 26, 2019 00:44 - 46 minutesToday 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!
Building an Autonomous Knowledge Graph with Mike Tung - #319
November 21, 2019 20:27 - 44 minutesToday we’re joined by Mike Tung, Founder, and CEO of Diffbot. In our conversation, we discuss Diffbot’s Knowledge Graph, including how it differs from more mainstream use cases like Google Search and MSFT Bing. We also discuss the developer experience with the knowledge graph and other tools, like Extraction API and Crawlbot, challenges like knowledge fusion, balancing being a research company that is also commercially viable, and how they approach their role in the research community.
The Next Generation of Self-Driving Engineers with Aaron Ma - Talk #318
November 18, 2019 21:13 - 47 minutesToday 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 in reinforcement learning and self-driving cars, his journey from programmer to ML engineer, his experiences participating in kaggle competitions, and how he balances his passion for ML with day-t...
Spiking Neural Networks: A Primer with Dr. Terrence Sejnowski - #317
November 14, 2019 17:46 - 49 minutes - 56.9 MBOn today’s episode, we’re joined by Dr. 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 archite...
Spiking Neural Networks: A Primer with Terrence Sejnowski - #317
November 14, 2019 17:46 - 49 minutesOn today’s episode, we’re joined by Terrence Sejnowski, to discuss the ins and outs of spiking neural networks, including brain architecture, the relationship between neuroscience and 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.
Bridging the Patient-Physician Gap with ML and Expert Systems w/ Xavier Amatriain - #316
November 11, 2019 22:05 - 39 minutesToday we’re joined by return guest Xavier Amatriain, Co-founder and CTO of Curai, whose goal is to make healthcare accessible and scaleable while bringing down costs. In our conversation, we touch on the shortcomings of traditional primary care, and how Curai fills that role, and some of the unique challenges his team faces in applying ML in the healthcare space. We also discuss the use of expert systems, how they train them, and how NLP projects like BERT and GPT-2 fit into what they’re buil...
What Does it Mean for a Machine to "Understand"? with Thomas Dietterich - #315
November 07, 2019 19:50 - 38 minutesToday we have the pleasure of being joined by Tom Dietterich, Distinguished Professor Emeritus at Oregon State University. Tom recently wrote a blog post titled "What does it mean for a machine to “understand”, and in our conversation, he goes into great detail on his thoughts. We cover a lot of ground, including Tom’s position in the debate, his thoughts on the role of systems like deep learning in potentially getting us to AGI, the “hype engine” around AI advancements, and so much more.
Scaling TensorFlow at LinkedIn with Jonathan Hung - #314
November 04, 2019 19:46 - 35 minutesToday we’re joined by Jonathan Hung, Sr. Software Engineer at LinkedIn. Jonathan presented at TensorFlow world last week, titled Scaling TensorFlow at LinkedIn. In our conversation, we discuss 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, and their foray into using Kubernetes for rese...
Machine Learning at GitHub with Omoju Miller - #313
October 31, 2019 19:43 - 43 minutesToday 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.”
Using AI to Diagnose and Treat Neurological Disorders with Archana Venkataraman - #312
October 28, 2019 21:43 - 46 minutesToday we’re joined by Archana Venkataraman, John C. Malone Assistant Professor of Electrical and Computer Engineering at Johns Hopkins University. 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. We explore her work applying machine learning to these problems, including biomarker discovery, disorder severity predict...
Deep Learning for Earthquake Aftershock Patterns with Phoebe DeVries & Brendan Meade - #311
October 25, 2019 17:35 - 36 minutesToday we are joined by Phoebe DeVries, Postdoctoral Fellow in the Department of Earth and Planetary Sciences at Harvard and Brendan Meade, Professor of Earth and Planetary Sciences at Harvard. Phoebe and Brendan’s work is focused on discovering as much as possible about earthquakes before they happen, and by measuring how the earth’s surface moves, predicting future movement location, as seen in their paper: ‘Deep learning of aftershock patterns following large earthquakes'.
Live from TWIMLcon! Operationalizing Responsible AI - #310
October 22, 2019 13:59 - 30 minutesAn 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.
Live from TWIMLcon! Scaling ML in the Traditional Enterprise - #309
October 18, 2019 14:58 - 33 minutesMachine learning and AI is finding a place in the traditional enterprise - although the path to get there is different. In this episode, our panel analyzes the state and future of larger, more established brands. 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.
Live from TWIMLcon! Culture & Organization for Effective ML at Scale (Panel) - #308
October 15, 2019 18:51 - 27 minutesTWIMLcon 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 about changing the way companies think about machine learning 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 Resea...
Live from TWIMLcon! Use-Case Driven ML Platforms with Franziska Bell - #307
October 10, 2019 17:47 - 32 minutesToday we're Franziska Bell, Ph.D., the Director of Data Science Platforms at Uber, who joined Sam on stage at TWIMLcon last week. Fran provided a look into the cutting edge data science available company-wide at the push of a button. Since joining Uber, Fran has developed a portfolio of platforms, ranging from forecasting to conversational AI. Hear how use cases can strategically guide platform development, the evolving relationship between her team and Michelangelo (Uber’s ML Platform) and m...
Live from TWIMLcon! Operationalizing ML at Scale with Hussein Mehanna - #306
October 08, 2019 15:56 - 33 minutesThe live interviews from TWIMLcon continue with Hussein Mehanna, Head of ML and AI at Cruise. From his start at Facebook to his current work at Cruise, Hussein has seen first hand what it takes to scale and sustain machine learning programs. Hear him discuss the challenges (and joys) of working in the industry, his insight into analyzing scale when innovation is happening in parallel with development, his experiences at Facebook, Google, and Cruise, and his predictions for the future of ML pl...
Live from TWIMLcon! Encoding Company Culture in Applied AI Systems - #305
October 04, 2019 09:00 - 32 minutesIn this episode, Sam is joined by Deepak Agarwal, VP of Engineering at LinkedIn, who graced the stage at TWIMLcon: AI Platforms for a keynote interview. Deepak shares the impact that standardizing processes and tools have on a company’s culture and productivity levels, and best practices to increasing ML ROI. He also details the Pro-ML initiative for delivering machine learning systems at scale, specifically looking at aligning improvement of tooling and infrastructure with the pace of innova...
Live from TWIMLcon! Overcoming the Barriers to Deep Learning in Production with Andrew Ng - #304
October 01, 2019 18:55 - 34 minutesEarlier today, Andrew Ng joined us onstage at TWIMLcon - as the Founder and CEO of Landing AI and founding lead of Google Brain, Andrew is no stranger to knowing what it takes for AI and machine learning to be successful. Hear about the work that Landing AI is doing to help organizations adopt modern AI, his experience in overcoming challenges for large companies, how enterprises can get the most value for their ML investment as well as addressing the ‘essential complexity’ of software engine...
The Future of Mixed-Autonomy Traffic with Alexandre Bayen - #303
September 27, 2019 18:29 - 44 minutesToday we are joined by Alexandre Bayen, Director of the Institute for Transportation Studies and Professor at UC Berkeley. Alex's current research is in mixed-autonomy traffic to understand how the growing automation in self-driving vehicles can be used to improve mobility and flow of traffic. At the AWS re:Invent conference last year, Alex presented on the future of mixed-autonomy traffic and the two major revolutions he predicts will take place in the next 10-15 years.