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.
Today we are 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.
Today we are 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.
Today we are 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.
Today we are 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.
Today we are 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.
Today 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!
Today 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.
Today 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."
Today 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 future.
Today 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.
Today 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-robot intera
Today 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 approac
Today 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 has it all.
Today 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 can do b
Today 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.
Today 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.
Today 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.
Today 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.
Today 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 memorizatio
Today 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.
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.
• Adi gives us an overview of the evolution of C3 and their platform, along with a breakdown of a few Shell-specific use cases.
Today 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.
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 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.
Today 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.
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 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
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 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.
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.
• What motivates companies that aren’t already heavily invested in platforms, optimization or automation, to do so, and much more!
Today 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!
Today 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!
Today 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.
In 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!
Today 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.
Today 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
Today 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!
Today 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.
Today 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.
Today 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
In 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.
Today 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.
Today 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
Today 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
Today 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.
Today 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 forecasting, a
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.
Today 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 learning
Today 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, XGBoost and Dask.
Today 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.
Today we’re joined by Nicole Nichols, a senior research scientist at the Pacific Northwest National Lab. We discuss her recent presentation at GTC, which was titled “Machine Learning for Security, and Security for Machine Learning.” We explore two use cases, insider threat detection, and software fuzz testing, discussing the effectiveness of standard and bidirectional RNN language models for detecting malicious activity, the augmentation of software fuzzing techniques using deep learning, and much mor
Today we’re joined by Gerald Quon, assistant professor at UC Davis.
Gerald presented his work on Deep Domain Adaptation and Generative Models for Single Cell Genomics at GTC this year, which explores single cell genomics as a means of disease identification for treatment. In our conversation, we discuss how he uses deep learning to generate novel insights across diseases, the different types of data that was used, and the development of ‘nested’ Generative Models for single cell measurement.
Today we’re joined by Yashar Hezaveh, Assistant Professor at the University of Montreal. Yashar and I caught up to discuss his work on gravitational lensing, which is the bending of light from distant sources due to the effects of gravity. In our conversation, Yashar and I discuss how ML can be applied to undistort images, the intertwined roles of simulation and ML in generating images, incorporating other techniques such as domain transfer or GANs, and how he assesses the results of this project.
Today we’re joined by Dan Schrider, assistant professor in the department of genetics at UNC Chapel Hill.
My discussion with Dan starts with an overview of population genomics, looking into his application of ML in the field. We then dig into Dan’s paper “The Unreasonable Effectiveness of Convolutional Neural Networks in Population Genetic Inference,” which examines the idea that CNNs are capable of outperforming expert-derived statistical methods for some key problems in the field.
Today we’re joined by Rob Walker, Vice President of Decision Management at Pegasystems.
Rob joined us back in episode 127 to discuss “Hyperpersonalizing the customer experience.” Today, he’s back for a discussion about the role of empathy in AI systems. In our conversation, we dig into the role empathy plays in consumer-facing human-AI interactions, the differences between empathy and ethics, and a few examples of ways empathy should be considered when enterprise AI systems.
Today we’re joined by Tom Szumowski, Data Scientist at URBN, parent company of Urban Outfitters and other consumer fashion brands. Tom and I caught up to discuss his project “Exploring Custom Vision Services for Automated Fashion Product Attribution.” We look at the process Tom and his team took to build custom attribution models, and the results of their evaluation of various custom vision APIs for this purpose, with a focus on the various roadblocks and lessons he and his team encountered along the
Today we’re joined by Peter Wittek, Assistant Professor at the University of Toronto working on quantum-enhanced machine learning and the application of high-performance learning algorithms.
In our conversation, we discuss the current state of quantum computing, a look ahead to what the next 20 years of quantum computing might hold, and how current quantum computers are flawed. We then dive into our discussion on quantum machine learning, and Peter’s new course on the topic, which debuted in Februar
In this special bonus episode of the podcast, I’m joined by Ewin Tang, a PhD student in the Theoretical Computer Science group at the University of Washington.
In our conversation, Ewin and I dig into her paper “A quantum-inspired classical algorithm for recommendation systems,” which took the quantum computing community by storm last summer. We haven’t called out a Nerd-Alert interview in a long time, but this interview inspired us to dust off that designation, so get your notepad ready!
Today we're joined by Alfredo Luque, a software engineer on the machine infrastructure team at Airbnb.
If you’re interested in AI Platforms and ML infrastructure, you probably remember my interview with Airbnb’s Atul Kale, in which we discussed their Bighead platform. In my conversation with Alfredo, we dig a bit deeper into Bighead’s support for TensorFlow, discuss a recent image categorization challenge they solved with the framework, and explore what the new 2.0 release means for their users.
Today we’re joined by Elena Nieddu, Phd Student at Roma Tre University, who presented on her project “In Codice Ratio” at the TF Dev Summit.
In our conversation, Elena provides an overview of the project, which aims to annotate and transcribe Vatican secret archive documents via machine learning. We discuss the many challenges associated with transcribing this vast archive of handwritten documents, including overcoming the high cost of data annotation.
Today we're joined by Paige Bailey, TensorFlow developer advocate at Google, to discuss the TensorFlow 2.0 alpha release. Paige and I talk through the latest TensorFlow updates, 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 ML platform, the emphasis on community collaboration with TF 2.0, and more.
Today we’re joined by Andrew Trask, PhD student at the University of Oxford and Leader of the OpenMined Project, an open-source community focused on researching, developing, and promoting tools for secure, privacy-preserving, value-aligned artificial intelligence. We dig into why OpenMined is important, exploring some of the basic research and technologies supporting Private, Decentralized Data Science, including ideas such as Differential Privacy,and Secure Multi-Party Computation.
Today we’re joined by Jos Van Der Westhuizen, PhD student in Engineering at Cambridge University.
Jos’ research focuses on applying LSTMs, or Long Short-Term Memory neural networks, to biological data for various tasks. In our conversation, we discuss his paper "The unreasonable effectiveness of the forget gate," in which he explores the various “gates” that make up an LSTM module and the general impact of getting rid of gates on the computational intensity of training the networks.
Today we’re joined by Andrew Guldman, VP of Product Engineering and R&D at Fluid to discuss Fluid XPS, a user experience built to help the casual shopper decide on the best product choices during online retail interactions. We specifically discuss its origins as a product to assist outerwear retailer The North Face. In our conversation, we discuss their use of heat-sink algorithms and graph databases, challenges associated with staying on top of a constantly changing landscape, and more!
Today we’re joined by Kevin Tran, PhD student at Carnegie Mellon University. In our conversation, we explore the challenges surrounding the creation of renewable energy fuel cells, which is discussed in his recent Nature paper “Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution.”
The AI Conference is returning to New York in April and we have one FREE conference pass for a lucky listener! Visit twimlai.com/ainygiveaway to enter!
Today we’re joined by Aydogan Ozcan, Professor of Electrical and Computer Engineering at UCLA, exploring his group's research into the intersection of deep learning and optics, holography and computational imaging. We specifically look at a really interesting project to create all-optical neural networks which work based on diffraction, where the printed pixels of the network are analogous to neurons. We also explore practical applications for their research and other areas of interest.
Today we’re joined by Hema Raghavan and Scott Meyer of LinkedIn to discuss the graph database and machine learning systems that power LinkedIn features such as “People You May Know” and second-degree connections. Hema shares her insight into the motivations for LinkedIn’s use of graph-based models and some of the challenges surrounding using graphical models at LinkedIn’s scale, while Scott details his work on the software used at the company to support its biggest graph databases.
Today we conclude our Black in AI series with Sicelukwanda Zwane, a masters student at the University of Witwatersrand and graduate research assistant at the CSIR, who presented on “Safer Exploration in Deep Reinforcement Learning using Action Priors” at the workshop. In our conversation, we discuss what “safer exploration” means in this sense, the difference between this work and other techniques like imitation learning, and how this fits in with the goal of “lifelong learning.”
In the inaugural TWiML Live, Sam Charrington is joined by Amanda Askell (OpenAI), Anima Anandkumar (NVIDIA/CalTech), Miles Brundage (OpenAI), Robert Munro (Lilt), and Stephen Merity to discuss the controversial recent release of the OpenAI GPT-2 Language Model.
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.
Today we present the final episode in our AI for the Benefit of Society series, in which we’re joined by Mira Lane, Partner Director for Ethics and Society at Microsoft.
Mira and I focus our conversation on the role of culture and human-centered design in AI. We discuss how Mira defines human-centered design, its connections to culture and responsible innovation, and how these ideas can be scalably implemented across large engineering organizations.
Today we’re joined by Hanna Wallach, a Principal Researcher at Microsoft Research.
Hanna and I really dig into how bias and a lack of interpretability and transparency show up across ML. We discuss the role that human biases, even those that are inadvertent, play in tainting data, and whether deployment of “fair” ML models can actually be achieved in practice, and much more. Hanna points us to a TON of resources to further explore the topic of fairness in ML, which you’ll find at twimlai.com/talk
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 back at Microsoft Ignite, where he gave me some really interesting takes on AI development in China, which is linked in the show notes. This conversation centers around impact areas Peter sees for AI in healthcare, namely diagnostics and therapeutics, tools, and the future of precision medicine.
Today, we're joined by Justice Amoh Jr., a Ph.D. student at Dartmouth’s Thayer School of Engineering.
Justice presented his work on “An Optimized Recurrent Unit for Ultra-Low Power Acoustic Event Detection.” In our conversation, we discuss his goal of bringing low cost, high-efficiency wearables to market for monitoring asthma. We explore the challenges of using classical machine learning models on microcontrollers, and how he went about developing models optimized for constrained hardware environm
Today, we continue our Black in AI series with Alvin Grissom II, Assistant Professor of Computer Science at Ursinus College. In our conversation, we dive into the paper he presented at the workshop, “Pathologies of Neural Models Make Interpretations Difficult.” We talk through some of the “pathological behaviors” he identified in the paper, how we can better understand the overconfidence of trained deep learning models in certain settings, and how we can improve model training with entropy regulariz
Today we’re joined by Lucas Joppa, Chief Environmental Officer at Microsoft and Zach Parisa, Co-founder and president of Silvia Terra, a Microsoft AI for Earth grantee.
In our conversation, we explore the ways that ML & AI can be used to advance our understanding of forests and other ecosystems, supporting conservation efforts. We discuss how Silvia Terra uses computer vision and data from a wide array of sensors, combined with AI, to yield more detailed estimates of the various species in our forests.
Today we’re joined by Wendy Chisholm, a principal accessibility architect at Microsoft, and one of the chief proponents of the AI for Accessibility program, which extends grants to AI-powered accessibility projects the areas of Employment, Daily Life, and Communication & Connection. In our conversation, we discuss the intersection of AI and accessibility, the lasting impact that innovation in AI can have for people with disabilities and society as a whole, and the importance of projects in this area.
Today we're joined by Justin Spelhaug, General Manager of Technology for Social Impact at Microsoft.
In our conversation, we discuss the company’s efforts in AI for Humanitarian Action, covering Microsoft’s overall approach to technology for social impact, how his group helps mission-driven organizations best leverage technologies like AI, and how AI is being used at places like the World Bank, Operation Smile, and Mission Measurement to create greater impact.
Today, in the first episode of our Black in AI series, we’re joined by Randi Williams, PhD student at the MIT Media Lab.
At the Black in AI workshop Randi presented her research on Popbots: A Early Childhood AI Curriculum, which is geared towards teaching preschoolers the fundamentals of artificial intelligence. In our conversation, we discuss the origins of the project, the three AI concepts that are taught in the program, and the goals that Randi hopes to accomplish with her work.
Today we’re joined by Tim Jurka, Head of Feed AI at LinkedIn. In our conversation, Tim describes the holistic optimization of the feed and we discuss some of the interesting technical and business challenges associated with trying to do this. We talk through some of the specific techniques used at LinkedIn like Multi-arm Bandits and Content Embeddings, and also jump into a really interesting discussion about organizing for machine learning at scale.
Today we’re joined by Gary Brotman, Senior Director of Product Management at Qualcomm Technologies, Inc.
Gary, who got his start in AI through music, now leads strategy and product planning for the company’s AI and ML technologies, including those that make up the Qualcomm Snapdragon mobile platforms. In our conversation, we discuss AI on mobile devices and at the edge, including popular use cases, and explore some of the various acceleration technologies offered by Qualcomm and others that enable th
A few weeks ago, I made the trek to Las Vegas for the world’s biggest electronics conference, CES. In this special visual only episode, we’re going to check out some of the interesting examples of machine learning and AI that I found at the event.
Check out the video at https://twimlai.com/ces2019, and be sure to hit the like and subscribe buttons and let us know how you like the show via a comment!
For the show notes, visit https://twimlai.com/talk/222.
Today we’re joined by Vladimir Bychkovsky, Engineering Manager at Facebook, to discuss Spiral, a system they’ve developed for self-tuning high-performance infrastructure services at scale, using real-time machine learning. In our conversation, we explore how the system works, how it was developed, and how infrastructure teams at Facebook can use it to replace hand-tuned parameters set using heuristics with services that automatically optimize themselves in minutes rather than in weeks.
Today we’re joined by JJ Espinoza, former Director of Data Science at 20th Century Fox.
In this talk we dig into JJ and his team’s experience building and deploying a content recommendation system from the ground up. In our conversation, we explore the design of a couple of key components of their system, the first of which processes movie scripts to make recommendations about which movies the studio should make, and the second processes trailers to determine which should be recommended to users.
Today we’re joined by Solon Barocas, Assistant Professor of Information Science at Cornell University.
Solon and I caught up to discuss his work on model interpretability and the legal and policy implications of the use of machine learning models. In our conversation, we explore the gap between law, policy, and ML, and how to build the bridge between them, including formalizing ethical frameworks for machine learning. We also look at his paper ”The Intuitive Appeal of Explainable Machines.”
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.
In this episode of our AI Rewind series, we introduce a new friend of the show, Simon Osindero, Staff Research Scientist at DeepMind.
We discuss trends in Deep Reinforcement Learning in 2018 and beyond. We’ve packed a bunch into this show, as Simon walks us through many of the important papers and developments seen this year in areas like Imitation Learning, Unsupervised RL, Meta-learning, and more.
The complete show notes for this episode can be found at https://twimlai.com/talk/217.
In this episode of our AI Rewind series, we’ve brought back recent guest Sebastian Ruder, PhD Student at the National University of Ireland and Research Scientist at Aylien, to discuss trends in Natural Language Processing in 2018 and beyond.
In our conversation we cover a bunch of interesting papers spanning topics such as pre-trained language models, common sense inference datasets and large document reasoning and more, and talk through Sebastian’s predictions for the new year.
In this episode of our AI Rewind series, we’re back with Anima Anandkumar, Bren Professor at Caltech and now Director of Machine Learning Research at NVIDIA.
Anima joins us to discuss her take on trends in the broader Machine Learning field in 2018 and beyond. In our conversation, we cover not only technical breakthroughs in the field but also those around inclusivity and diversity.
For this episode's complete show notes, visit twimlai.com/talk/215.
In this episode of our AI Rewind series, we’re bringing back one of your favorite guests of the year, Jeremy Howard, founder and researcher at Fast.ai.
Jeremy joins us to discuss trends in Deep Learning in 2018 and beyond. We cover many of the papers, tools and techniques that have contributed to making deep learning more accessible than ever to so many developers and data scientists.
Today we close out both our NeurIPS series joined by Nando de Freitas, Team Lead & Principal Scientist at Deepmind. In our conversation, we explore his interest in understanding the brain and working towards artificial general intelligence. In particular, we dig into a couple of his team’s NeurIPS papers: “Playing hard exploration games by watching YouTube,” and “One-Shot high-fidelity imitation: Training large-scale deep nets with RL.”
Today we’re joined by David Spiegelhalter, Chair of Winton Center for Risk and Evidence Communication at Cambridge University and President of the Royal Statistical Society. David, an invited speaker at NeurIPS, presented on “Making Algorithms Trustworthy: What Can Statistical Science Contribute to Transparency, Explanation and Validation?”. In our conversation, we explore the nuanced difference between being trusted and being trustworthy, and its implications for those building AI systems.
Today we’re joined by Kunle Olukotun, Professor in the department of EE and CS at Stanford University, and Chief Technologist at Sambanova Systems. Kunle was an invited speaker at NeurIPS this year, presenting on “Designing Computer Systems for Software 2.0.” In our conversation, we discuss various aspects of designing hardware systems for machine and deep learning, touching on multicore processor design, domain specific languages, and graph-based hardware. This was a fun one!
Today we conclude our Trust in AI series with this conversation with Kathryn Hume, VP of Strategy at Integrate AI. We discuss her newly released white paper “Responsible AI in the Consumer Enterprise,” which details a framework for ethical AI deployment in e-commerce companies and other consumer-facing enterprises. We look at the structure of the ethical framework she proposes, and some of the many questions that need to be considered when deploying AI in an ethical manner.
Today we continue our exploration of Trust in AI with this interview with Richard Zemel, Professor in the department of Computer Science at the University of Toronto and Research Director at Vector Institute.
In our conversation, Rich describes some of his work on fairness in machine learning algorithms, including how he defines both group and individual fairness and his group’s recent NeurIPS poster, “Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer.”
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.
In the final episode of our re:Invent series, we're joined by Thorsten Joachims, Professor in the Department of Computer Science at Cornell University. We discuss his presentation “Unbiased Learning from Biased User Feedback,” looking at some of the inherent and introduced biases in recommender systems, and the ways to avoid them. We also discuss how inference techniques can be used to make learning algorithms more robust to bias, and how these can be enabled with the correct type of logging policies.
Today we’re joined by Jinho Choi, assistant professor of computer science at Emory University.
Jinho presented at the conference on ELIT, their cloud-based NLP platform. In our conversation, we discuss some of the key NLP challenges that Jinho and his group are tackling, including language parsing and character mining. We also discuss their vision for ELIT, which is to make it easy for researchers to develop, access, and deploying cutting-edge NLP tools models on the cloud.
I’m excited to present our second annual re:Invent Roundtable Roundup. This year I’m joined by Dave McCrory, VP of Software Engineering at Wise.io at GE Digital, and Val Bercovici, Founder and CEO of Pencil Data. If you missed the news coming out of re:Invent, we cover all of AWS’ most important ML and AI announcements, including SageMaker Ground Truth, Reinforcement Learning, DeepRacer, Inferentia and Elastic Inference, ML Marketplace and much more.
For the show notes visit https://twimlai.com/ta
Today we’re joined by Marisa Boston, Director of Cognitive Technology in KPMG’s Cognitive Automation Lab. We caught up to discuss some of the ways that KPMG is using AI to build tools that help augment the knowledge of their teams of professionals. We discuss knowledge graphs and how they can be used to map out and relate various concepts and how they use these in conjunction with NLP tools to create insight engines. We also look at tools that curate and contextualize news and other text-based data sour
Today, we’re joined by Stuart Reid, Chief Scientist at NMRQL Research.
NMRQL is an investment management firm that uses ML algorithms to make adaptive, unbiased, scalable, and testable trading decisions for its funds. In our conversation, Stuart and I dig into the way NMRQL uses ML and DL models to support the firm’s investment decisions. We focus on techniques for modeling non-stationary time-series, stationary vs non-stationary time-series, and challenges of building models using financial data.
In this episode of our AI Platforms series, we’re joined by Daniel Jeavons, General Manager of Data Science at Shell.
In our conversation, we explore the evolution of analytics and data science at Shell, discussing IoT-related applications and issues, such as inference at the edge, federated ML, and digital twins, all key considerations for the way they apply ML. We also talk about the data science process at Shell and the importance of platform technologies to the company as a whole.
In this episode of our AI Platforms series, we’re joined by Leemay Nassery, Senior Engineering Manager and head of the recommendations team at Comcast. In our conversation, Leemay and I discuss just how she and her team resurrected the Xfinity X1 recommendations platform, including the rebuilding the data pipeline, the machine learning process, and the deployment and training of their updated models. We also touch on the importance of A-B testing and maintaining their rebuilt infrastructure.
In this episode of our AI Platforms series, we’re joined by Bee-Chung Chen, Principal Staff Engineer and Applied Researcher at LinkedIn. Bee-Chung and I caught up to discuss LinkedIn’s internal AI automation platform, Pro-ML. Bee-Chung breaks down some of the major pieces of the pipeline, LinkedIn’s experience bringing Pro-ML to the company's developers and the role the LinkedIn AI Academy plays in helping them get up to speed.
For the complete show notes, visit https://twimlai.com/talk/200.