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
Deep Reinforcement Learning for Logistics at Instadeep with Karim Beguir - #302
September 25, 2019 12:54 - 43 minutesToday we are joined by Karim Beguir, Co-Founder and CEO of InstaDeep, a company focusing on building advanced decision-making systems for the enterprise. In this episode, we focus on logistical problems that require decision-making in complex environments using deep learning and reinforcement learning. Karim explains the InstaDeep process and mindset, where they get their data sets, the efficiency of RL, heuristic vs learnability approaches and how explainability fits into the model.
Deep Learning with Structured Data w/ Mark Ryan - #301
September 19, 2019 01:43 - 39 minutesToday we're joined by Mark Ryan, author of the upcoming book Deep Learning with Structured Data. Working on the support team at IBM Data and AI, he saw a lack of general structured data sets people could apply their models to. Using the streetcar network in Toronto, Mark gathered an open data set that started the research for his latest book. In this episode, Mark shares the benefits of applying deep learning to structured data, details of his experience with a range of data sets, and details...
Time Series Clustering for Monitoring Fueling Infrastructure Performance with Kalai Ramea - #300
September 18, 2019 02:04 - 30 minutesToday we're joined by Kalai Ramea, Data Scientist at PARC, a Xerox Company. In this episode we discuss her journey buying a hydrogen car and the subsequent journey and paper that followed assessing fueling stations. In her next paper, Kalai looked at fuel consumption at hydrogen stations and used temporal clustering to identify signatures of usage over time. As the number of fueling stations is planned to increase dramatically in the future, building reliability on their performance is crucial.
Swarm AI for Event Outcome Prediction with Gregg Willcox - TWIML Talk #299
September 13, 2019 16:58 - 41 minutesToday we're joined by Gregg Willcox, Director of Research and Development at Unanimous AI. Inspired by the natural phenomenon called 'swarming', which uses the collective intelligence of a group to produce more accurate results than an individual alone, ‘Swarm AI’ was born. A game-like platform that channels the convictions of individuals to come to a consensus and using a behavioral neural network trained on people’s behavior called ‘Conviction’, to further amplify the results.
Rebooting AI: What's Missing, What's Next with Gary Marcus - TWIML Talk #298
September 10, 2019 14:21 - 47 minutesToday we're joined by Gary Marcus, CEO and Founder at Robust.AI, well-known scientist, bestselling author, professor and entrepreneur. Hear Gary discuss his latest book, ‘Rebooting AI: Building Artificial Intelligence We Can Trust’, an extensive look into the current gaps, pitfalls and areas for improvement in the field of machine learning and AI. In this episode, Gary provides insight into what we should be talking and thinking about to make even greater (and safer) strides in AI.
DeepQB: Deep Learning to Quantify Quarterback Decision-Making with Brian Burke - TWIML Talk #297
September 05, 2019 18:11 - 50 minutesToday we're joined by Brian Burke, Analytics Specialist with the Stats & Information Group at ESPN. A former Navy pilot and lifelong football fan, Brian saw the correlation between fighter pilots and quarterbacks in the quick decisions both roles make on a regular basis. In this episode, we discuss his paper: “DeepQB: Deep Learning with Player Tracking to Quantify Quarterback Decision-Making & Performance”, what it means for football, and his excitement for machine learning in sports.
Measuring Performance Under Pressure Using ML with Lotte Bransen - TWIML Talk #296
September 03, 2019 17:30 - 34 minutesToday we're joined by Lotte Bransen, a Scientific Researcher at SciSports. With a background in mathematics, econometrics, and soccer, Lotte has honed her research on analytics of the game and its players, using trained models to understand the impact of mental pressure on a player’s performance. In this episode, Lotte discusses her paper, ‘Choke or Shine? Quantifying Soccer Players' Abilities to Perform Under Mental Pressure’ and the implications of her research in the world of sports.
Managing Deep Learning Experiments with Lukas Biewald - TWIML Talk #295
August 29, 2019 18:09 - 42 minutesToday we're joined by Lukas Biewald, CEO and Co-Founder of Weights & Biases. Lukas founded the company after seeing a need for reproducibility in deep learning experiments. In this episode, we discuss his experiment tracking tool, how it works, the components that make it unique, and the collaborative culture that Lukas promotes. Listen in to how he got his start in deep learning and experiment tracking, the current Weights & Biases success strategy, and what his team is working on today.
Re-Architecting Data Science at iRobot with Angela Bassa - TWIML Talk #294
August 26, 2019 18:54 - 48 minutesToday we’re joined by Angela Bassa, Director of Data Science at iRobot. In our conversation, Angela and I discuss: • iRobot's re-architecture, and a look at the evolution of iRobot. • Where iRobot gets its data from and how they taxonomize data science. • The platforms and processes that have been put into place to support delivering models in production. •The role of DevOps in bringing these various platforms together, and much more!
Disentangled Representations & Google Research Football with Olivier Bachem - TWIML Talk #293
August 22, 2019 17:00 - 42 minutesToday we’re joined by Olivier Bachem, a research scientist at Google AI on the Brain team. Olivier joins us to discuss his work on Google’s research football project, their foray into building a novel reinforcement learning environment. Olivier and Sam discuss what makes this environment different than other available RL environments, such as OpenAI Gym and PyGame, what other techniques they explored while using this environment, and what’s on the horizon for their team and Football RLE.
Neural Network Quantization and Compression with Tijmen Blankevoort - TWIML Talk #292
August 19, 2019 18:07 - 50 minutesToday we’re joined by Tijmen Blankevoort, a staff engineer at Qualcomm, who leads their compression and quantization research teams. In our conversation with Tijmen we discuss: • The ins and outs of compression and quantization of ML models, specifically NNs, • How much models can actually be compressed, and the best way to achieve compression, • We also look at a few recent papers including “Lottery Hypothesis."
Identifying New Materials with NLP with Anubhav Jain - TWIML Talk #291
August 15, 2019 18:58 - 39 minutesToday we are joined by Anubhav Jain, Staff Scientist & Chemist at Lawrence Berkeley National Lab. We discuss his latest paper, ‘Unsupervised word embeddings capture latent knowledge from materials science literature’. Anubhav explains the design of a system that takes the literature and uses natural language processing to conceptualize complex material science concepts. He also discusses scientific literature mining and how the method can recommend materials for functional applications in the...
The Problem with Black Boxes with Cynthia Rudin - TWIML Talk #290
August 14, 2019 13:38 - 48 minutesToday we are joined by Cynthia Rudin, Professor of Computer Science, Electrical and Computer Engineering, and Statistical Science at Duke University. In this episode we discuss her paper, ‘Please Stop Explaining Black Box Models for High Stakes Decisions’, and how interpretable models make for more comprehensible decisions - extremely important when dealing with human lives. Cynthia explains black box and interpretable models, their development, use cases, and her future plans in the field.
Human-Robot Interaction and Empathy with Kate Darling - TWIML Talk #289
August 08, 2019 16:42 - 43 minutesToday we’re joined by Dr. Kate Darling, Research Specialist at the MIT Media Lab. Kate’s focus is on robot ethics, the social implication of how people treat robots and the purposeful design of robots in our daily lives. We discuss measuring empathy, the impact of robot treatment on kids behavior, the correlation between animals and robots, and why 'effective' robots aren’t always humanoid. Kate combines a wealth of knowledge with an analytical mind that questions the why and how of human-rob...
Automated ML for RNA Design with Danny Stoll - TWIML Talk #288
August 05, 2019 17:31 - 37 minutesToday we’re joined by Danny Stoll, Research Assistant at the University of Freiburg. Danny’s current research can be encapsulated in his latest paper, ‘Learning to Design RNA’. In this episode, Danny explains the design process through reverse engineering and how his team’s deep learning algorithm is applied to train and design sequences. We discuss transfer learning, multitask learning, ablation studies, hyperparameter optimization and the difference between chemical and statistical based ap...
Developing a brain atlas using deep learning with Theofanis Karayannis - TWIML Talk #287
August 01, 2019 16:33 - 37 minutesToday we’re joined by Theofanis Karayannis, Assistant Professor at the Brain Research Institute of the University of Zurich. Theo’s research is focused on brain circuit development and uses Deep Learning methods to segment the brain regions, then detect the connections around each region. He then looks at the distribution of connections that make neurological decisions in both animals and humans every day. From the way images of the brain are collected to genetic trackability, this episode ha...
Environmental Impact of Large-Scale NLP Model Training with Emma Strubell - TWIML Talk #286
July 29, 2019 18:26 - 37 minutesToday we’re joined by Emma Strubell, currently a visiting scientist at Facebook AI Research. Emma’s focus is bringing state of the art NLP systems to practitioners by developing efficient and robust machine learning models. Her paper, Energy and Policy Considerations for Deep Learning in NLP, reviews carbon emissions of training neural networks despite an increase in accuracy. In this episode, we discuss Emma’s research methods, how companies are reacting to environmental concerns, and how we...
“Fairwashing” and the Folly of ML Solutionism with Zachary Lipton - TWIML Talk #285
July 25, 2019 15:47 - 1 hourToday we’re joined by Zachary Lipton, Assistant Professor in the Tepper School of Business. With a theme of data interpretation, Zachary’s research is focused on machine learning in healthcare, with the goal of assisting physicians through the diagnosis and treatment process. We discuss supervised learning in the medical field, robustness under distribution shifts, ethics in machine learning systems across industries, the concept of ‘fairwashing, and more.
Retinal Image Generation for Disease Discovery with Stephen Odaibo - TWIML Talk #284
July 22, 2019 16:05 - 41 minutesToday we’re joined by Dr. Stephen Odaibo, Founder and CEO of RETINA-AI Health Inc. Stephen’s journey to machine learning and AI includes degrees in math, medicine and computer science, which led him to an ophthalmology practice before becoming an entrepreneur. In this episode we discuss his expertise in ophthalmology and engineering along with the current state of both industries that lead him to build autonomous systems that diagnose and treat retinal diseases.
Real world model explainability with Rayid Ghani - TWiML Talk #283
July 18, 2019 16:00 - 50 minutesToday we’re joined by Rayid Ghani, Director of the Center for Data Science and Public Policy at the University of Chicago. Drawing on his range of experience, Rayid saw that while automated predictions can be helpful, they don’t always paint a full picture. The key is the relevant context when making tough decisions involving humans and their lives. We delve into the world of explainability methods, necessary human involvement, machine feedback loop and more.
Inspiring New Machine Learning Platforms w/ Bioelectric Computation with Michael Levin - TWiML Talk #282
July 15, 2019 16:38 - 25 minutesToday we’re joined by Michael Levin, Director of the Allen Discovery Institute at Tufts University. In our conversation, we talk about synthetic living machines, novel AI architectures and brain-body plasticity. Michael explains how our DNA doesn’t control everything and how the behavior of cells in living organisms can be modified and adapted. Using research on biological systems dynamic remodeling, Michael discusses the future of developmental biology and regenerative medicine.
Simulation and Synthetic Data for Computer Vision with Batu Arisoy - TWiML Talk #281
July 09, 2019 17:38 - 41 minutesToday we’re joined by Batu Arisoy, Research Manager with the Vision Technologies & Solutions team at Siemens Corporate Technology. Batu’s research focus is solving limited-data computer vision problems, providing R&D for business units throughout the company. In our conversation, Batu details his group's ongoing projects, like an activity recognition project with the ONR, and their many CVPR submissions, which include an emulation of a teacher teaching students information without the use of ...
Spiking Neural Nets and ML as a Systems Challenge with Jeff Gehlhaar - TWIML Talk #280
July 08, 2019 19:07 - 52 minutesToday we’re joined by Jeff Gehlhaar, VP of Technology and Head of AI Software Platforms at Qualcomm. Qualcomm has a hand in tons of machine learning research and hardware, and in our conversation with Jeff 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 much more.
Transforming Oil & Gas with AI with Adi Bhashyam and Daniel Jeavons - TWIML Talk #279
July 01, 2019 18:33 - 46 minutesToday 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. • Adi gives us an overview of the evolution of C3 and their platform, along with a breakdown of a few Shell-specific use cases.
Fast Radio Burst Pulse Detection with Gerry Zhang - TWIML Talk #278
June 27, 2019 18:18 - 38 minutesToday we’re joined by Yunfan Gerry Zhang, a PhD student 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.
Tracking CO2 Emissions with Machine Learning with Laurence Watson - TWIML Talk #277
June 24, 2019 19:29 - 41 minutesToday 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 are 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.
Topic Modeling for Customer Insights at USAA with William Fehlman - TWIML Talk #276
June 20, 2019 19:26 - 44 minutesToday we’re joined by William Fehlman, director of data science at USAA, to discuss: • His work on topic modeling, which USAA uses in various scenarios, including member chat channels. • 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.
Phronesis of AI in Radiology with Judy Gichoya - TWIML Talk #275
June 18, 2019 20:46 - 43 minutesToday 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 on the paper “Phronesis of AI in Radiology: Superhuman meets Natural Stupidy,” reviewing the claims of “superhuman” AI performance in radiology. • 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 c
The Ethics of AI-Enabled Surveillance with Karen Levy - TWIML Talk #274
June 14, 2019 19:31 - 43 minutesToday 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 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 surv...
Supporting Rapid Model Development at Two Sigma with Matt Adereth & Scott Clark - TWIML Talk #273
June 11, 2019 17:16 - 46 minutesToday 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. • What motivates companies that aren’t already heavily invested in platforms, optimization or automation, to do so, and much more!
Scaling Model Training with Kubernetes at Stripe with Kelley Rivoire - TWIML Talk #272
June 06, 2019 16:34 - 42 minutesToday we’re joined by Kelley Rivoire, engineering manager working on machine learning infrastructure at Stripe. Kelley and I caught up at a recent Strata Data conference to discuss: • Her talk "Scaling model training: From flexible training APIs to resource management with Kubernetes." • Stripe’s machine learning infrastructure journey, including their start from a production focus. • Internal tools used at Stripe, including Railyard, an API built to manage model training at scale & more!
Productizing ML at Scale at Twitter with Yi Zhuang - TWIML Talk #271
June 03, 2019 18:05 - 46 minutesToday we continue our AI Platforms series joined by Yi Zhuang, Senior Staff Engineer at Twitter. In our conversation, we cover: • The machine learning landscape at Twitter, including with the history of the Cortex team • Deepbird v2, which is used for model training and evaluation solutions, and it's integration with Tensorflow 2.0. • The newly assembled “Meta” team, that is tasked with exploring the bias, fairness, and accountability of their machine learning models, and much more!
Snorkel: A System for Fast Training Data Creation with Alex Ratner - TWiML Talk #270
May 30, 2019 18:35 - 43 minutesToday we’re joined by Alex Ratner, Ph.D. student at Stanford, to discuss: • Snorkel, the open source framework that is the successor to Stanford's Deep Dive project. • How Snorkel is used as a framework for creating training data with weak supervised learning techniques. • Multiple use cases for Snorkel, including how it is used by companies like Google. The complete show notes can be found at twimlai.com/talk/270. Follow along with AI Platforms Vol. 2 at twimlai.com/aiplatforms2.
Advancing Autonomous Vehicle Development Using Distributed Deep Learning with Adrien Gaidon - TWiML Talk #269
May 28, 2019 18:26 - 48 minutesIn this, the kickoff episode of AI Platforms Vol. 2, we're joined by Adrien Gaidon, Machine Learning Lead at Toyota Research Institute. Adrien and I caught up to discuss his team’s work on deploying distributed deep learning in the cloud, at scale. In our conversation, we discuss: • The beginning and gradual scaling up of TRI's platform. • Their distributed deep learning methods, including their use of stock Pytorch, and much more!
Are We Being Honest About How Difficult AI Really Is? w/ David Ferrucci - TWiML Talk #268
May 23, 2019 22:31 - 50 minutesToday we’re joined by David Ferrucci, Founder, CEO, and Chief Scientist at Elemental Cognition, a company focused on building natural learning systems that understand the world the way people do, to discuss: • The role of “understanding” in the context of AI systems, and the types of commitments and investments needed to achieve even modest levels of understanding. • His thoughts on the power of deep learning, what the path to AGI looks like, and the need for hybrid systems to get there.
Gauge Equivariant CNNs, Generative Models, and the Future of AI with Max Welling - TWiML Talk #267
May 20, 2019 19:58 - 1 hourToday we’re joined by Max Welling, research chair in machine learning at the University of Amsterdam, and VP of Technologies at Qualcomm, to discuss: • Max’s research at Qualcomm AI Research and the University of Amsterdam, including his work on Bayesian deep learning, Graph CNNs and Gauge Equivariant CNNs, power efficiency for AI via compression, quantization, and compilation. • Max’s thoughts on the future of the AI industry, in particular, the relative importance of models, data and com
Can We Trust Scientific Discoveries Made Using Machine Learning? with Genevera Allen - TWiML Talk #266
May 16, 2019 16:48 - 42 minutesToday we’re joined by Genevera Allen, associate professor of statistics in the EECS Department at Rice University. Genevera caused quite the stir at the American Association for the Advancement of Science meeting earlier this year with her presentation “Can We Trust Data-Driven Discoveries?" In our conversation, we discuss the goal of Genevera's talk, the issues surrounding reproducibility in Machine Learning, and much more!
Creative Adversarial Networks for Art Generation with Ahmed Elgammal - TWiML Talk #265
May 13, 2019 18:25 - 38 minutesToday we’re joined by Ahmed Elgammal, a professor in the department of computer science at Rutgers, and director of The Art and Artificial Intelligence Lab. We discuss his work on AICAN, a creative adversarial network that produces original portraits, trained with over 500 years of European canonical art. The complete show notes for this episode can be found at twimlai.com/talk/265.
Diagnostic Visualization for Machine Learning with YellowBrick w/ Rebecca Bilbro - TWiML Talk #264
May 10, 2019 16:22 - 41 minutesToday we close out our PyDataSci series joined by Rebecca Bilbro, head of data science at ICX media and co-creator of the popular open-source visualization library YellowBrick. In our conversation, Rebecca details: • Her relationship with toolmaking, which led to the eventual creation of YellowBrick. • Popular tools within YellowBrick, including a summary of their unit testing approach. • Interesting use cases that she’s seen over time.
Librosa: Audio and Music Processing in Python with Brian McFee - TWiML Talk #263
May 09, 2019 18:13 - 38 minutesToday we continue our PyDataSci series joined by Brian McFee, assistant professor of music technology and data science at NYU, and creator of LibROSA, a python package for music and audio analysis. Brian walks us through his experience building LibROSA, including: • Detailing the core functions provided in the library • His experience working in Jupyter Notebook • We explore a typical LibROSA workflow & more! The complete show notes for this episode can be found at twimlai.com/talk/26
Practical Natural Language Processing with spaCy and Prodigy w/ Ines Montani - TWiML Talk #262
May 07, 2019 19:48 - 48 minutesIn this episode of PyDataSci, we’re joined by Ines Montani, Cofounder of Explosion, Co-developer of SpaCy and lead developer of Prodigy. Ines and I caught up to discuss her various projects, including the aforementioned SpaCy, an open-source NLP library built with a focus on industry and production use cases. The complete show notes for this episode can be found at twimlai.com/talk/262. Check out the rest of the PyDataSci series at twimlai.com/pydatasci.
Scaling Jupyter Notebooks with Luciano Resende - TWiML Talk #261
May 06, 2019 17:11 - 33 minutesToday we're joined by Luciano Resende, an Open Source AI Platform Architect at IBM, to discuss his work on Jupyter Enterprise Gateway. In our conversation, we address challenges that arise while using Jupyter Notebooks at scale and the role of open source projects like Jupyter Hub and Enterprise Gateway. We also explore some common requests like tighter integration with git repositories, as well as the python-centricity of the vast Jupyter ecosystem.
Fighting Fake News and Deep Fakes with Machine Learning w/ Delip Rao - TWiML Talk #260
May 03, 2019 18:47 - 58 minutesToday we’re joined by Delip Rao, vice president of research at the AI Foundation, co-author of the book Natural Language Processing with PyTorch, and creator of the Fake News Challenge. In our conversation, we discuss the generation and detection of artificial content, including “fake news” and “deep fakes,” the state of generation and detection for text, video, and audio, the key challenges in each of these modalities, the role of GANs on both sides of the equation, and other potential solutio
Maintaining Human Control of Artificial Intelligence with Joanna Bryson - TWiML Talk #259
May 01, 2019 19:25 - 38 minutesToday we’re joined by Joanna Bryson, Reader at the University of Bath. I was fortunate to catch up with Joanna at the conference, where she presented on “Maintaining Human Control of Artificial Intelligence." In our conversation, we explore our current understanding of “natural intelligence” and how it can inform the development of AI, the context in which she uses the term “human control” and its implications, and the meaning of and need to apply “DevOps” principles when developing AI sy
Intelligent Infrastructure Management with Pankaj Goyal & Rochna Dhand - TWiML Talk #258
April 29, 2019 17:58 - 44 minutesToday we're joined by Pankaj Goyal and Rochna Dhand, to discuss HPE InfoSight. In our conversation, Pankaj gives a look into how HPE as a company views AI, from their customers to the future of AI at HPE through investment. Rocha details the role of HPE’s Infosight in deploying AI operations at an enterprise level, including a look at where it fits into the infrastructure for their current customer base, along with a walkthrough of how InfoSight is deployed in a real-world use case.
Organizing for Successful Data Science at Stitch Fix with Eric Colson - TWiML Talk #257
April 26, 2019 16:26 - 52 minutesToday we’re joined by Eric Colson, Chief Algorithms Officer at Stitch Fix, whose presentation at the Strata Data conference explored “How to make fewer bad decisions.” Our discussion focuses in on the three key organizational principles for data science teams that he’s developed while at Stitch Fix. Along the way, we also talk through various roles data science plays, exploring a few of the 800+ algorithms in use at the company spanning recommendations, inventory management, demand forecasti...
End-to-End Data Science to Drive Business Decisions at LinkedIn with Burcu Baran - TWiML Talk #256
April 24, 2019 17:45 - 48 minutesIn 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.
Learning with Limited Labeled Data with Shioulin Sam - TWiML Talk #255
April 22, 2019 22:11 - 44 minutesToday we’re joined by Shioulin Sam, Research Engineer with Cloudera Fast Forward Labs. Shioulin and I caught up to discuss the newest report to come out of CFFL, “Learning with Limited Label Data,” which explores active learning as a means to build applications requiring only a relatively small set of labeled data. We start our conversation with a review of active learning and some of the reasons why it’s recently become an interesting technology for folks building systems based on deep lea...
cuDF, cuML & RAPIDS: GPU Accelerated Data Science with Paul Mahler - TWiML Talk #254
April 19, 2019 17:33 - 38 minutesToday we're joined by Paul Mahler, senior data scientist and technical product manager for ML at NVIDIA. In our conversation, Paul and I discuss NVIDIA's RAPIDS open source project, which aims to bring GPU acceleration to traditional data science workflows and ML tasks. We dig into the various subprojects like cuDF and cuML that make up the RAPIDS ecosystem, as well as the role of lower-level libraries like mlprims and the relationship to other open-source projects like Scikit-learn, XGBoos...
Edge AI for Smart Manufacturing with Trista Chen - TWiML Talk #253
April 18, 2019 17:26 - 38 minutesToday we’re joined by Trista Chen, chief scientist of machine learning at Inventec, who spoke on “Edge AI in Smart Manufacturing: Defect Detection and Beyond” at GTC. In our conversation, we discuss the challenges that Industry 4.0 initiatives aim to address and dig into a few of the various use cases she’s worked on, such as the deployment of ML in an industrial setting to perform various tasks. We also discuss the challenges associated with estimating the ROI of industrial AI projects.