Differential Privacy at Bluecore with Zahi Karam - TWiML Talk #133

Differential Privacy at Bluecore with Zahi Karam - TWiML Talk #133

In this episode of our Differential Privacy series, I'm joined by Zahi Karam, Director of Data Science at Bluecore, whose retail marketing platform specializes in personalized email marketing. I sat down with Zahi at the Georgian Partners portfolio conference last year, where he gave me my initial exposure to the field of differential privacy, ultimately leading to this series. Zahi shared his insights into how differential privacy can be deployed in the real world and some of the technical and cultural challenges to doing so. We discuss the Bluecore use case in depth, including why and for whom they build differentially private machine learning models. The notes for this show can be found at twimlai.com/talk/133

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ML Innovation in Healthcare with Suchi Saria - #501

ML Innovation in Healthcare with Suchi Saria - #501

Today we’re joined by Suchi Saria, the founder and CEO of Bayesian Health, the John C. Malone associate professor of computer science, statistics, and health policy, and the director of the machine learning and healthcare lab at Johns Hopkins University.  Suchi shares a bit about her journey to working in the intersection of machine learning and healthcare, and how her research has spanned across both medical policy and discovery. We discuss why it has taken so long for machine learning to become accepted and adopted by the healthcare infrastructure and where exactly we stand in the adoption process, where there have been “pockets” of tangible success.  Finally, we explore the state of healthcare data, and of course, we talk about Suchi’s recently announced startup Bayesian Health and their goals in the healthcare space, and an accompanying study that looks at real-time ML inference in an EMR setting. The complete show notes for this episode can be found at twimlai.com/go/501.

15 Juli 202145min

Cross-Device AI Acceleration, Compilation & Execution with Jeff Gehlhaar - #500

Cross-Device AI Acceleration, Compilation & Execution with Jeff Gehlhaar - #500

Today we’re joined by a friend of the show Jeff Gehlhaar, VP of technology and the head of AI software platforms at Qualcomm.  In our conversation with Jeff, we cover a ton of ground, starting with a bit of exploration around ML compilers, what they are, and their role in solving issues of parallelism. We also dig into the latest additions to the Snapdragon platform, AI Engine Direct, and how it works as a bridge to bring more capabilities across their platform, how benchmarking works in the context of the platform, how the work of other researchers we’ve spoken to on compression and quantization finds its way from research to product, and much more!  After you check out this interview, you can look below for some of the other conversations with researchers mentioned.  The complete show notes for this episode can be found at twimlai.com/go/500.

12 Juli 202141min

The Future of Human-Machine Interaction with Dan Bohus and Siddhartha Sen - #499

The Future of Human-Machine Interaction with Dan Bohus and Siddhartha Sen - #499

Today we continue our AI in Innovation series joined by Dan Bohus, senior principal researcher at Microsoft Research, and Siddhartha Sen, a principal researcher at Microsoft Research.  In this conversation, we use a pair of research projects, Maia Chess and Situated Interaction, to springboard us into a conversation about the evolution of human-AI interaction. We discuss both of these projects individually, as well as the commonalities they have, how themes like understanding the human experience appear in their work, the types of models being used, the various types of data, and the complexity of each of their setups.  We explore some of the challenges associated with getting computers to better understand human behavior and interact in ways that are more fluid. Finally, we touch on what excites both Dan and Sid about their respective projects, and what they’re excited about for the future.   The complete show notes for this episode can be found at https://twimlai.com/go/499.

8 Juli 202148min

Vector Quantization for NN Compression with Julieta Martinez - #498

Vector Quantization for NN Compression with Julieta Martinez - #498

Today we’re joined by Julieta Martinez, a senior research scientist at recently announced startup Waabi.  Julieta was a keynote speaker at the recent LatinX in AI workshop at CVPR, and our conversation focuses on her talk “What do Large-Scale Visual Search and Neural Network Compression have in Common,” which shows that multiple ideas from large-scale visual search can be used to achieve state-of-the-art neural network compression. We explore the commonality between large databases and dealing with high dimensional, many-parameter neural networks, the advantages of using product quantization, and how that plays out when using it to compress a neural network.  We also dig into another paper Julieta presented at the conference, Deep Multi-Task Learning for Joint Localization, Perception, and Prediction, which details an architecture that is able to reuse computation between the three tasks, and is thus able to correct localization errors efficiently. The complete show notes for this episode can be found at twimlai.com/go/498.

5 Juli 202141min

Deep Unsupervised Learning for Climate Informatics with Claire Monteleoni - #497

Deep Unsupervised Learning for Climate Informatics with Claire Monteleoni - #497

Today we continue our CVPR 2021 coverage joined by Claire Monteleoni, an associate professor at the University of Colorado Boulder.  We cover quite a bit of ground in our conversation with Claire, including her journey down the path from environmental activist to one of the leading climate informatics researchers in the world. We explore her current research interests, and the available opportunities in applying machine learning to climate informatics, including the interesting position of doing ML from a data-rich environment.  Finally, we dig into the evolution of climate science-focused events and conferences, as well as the Keynote Claire gave at the EarthVision workshop at CVPR “Deep Unsupervised Learning for Climate Informatics,” which focused on semi- and unsupervised deep learning approaches to studying rare and extreme climate events. The complete show notes for this episode can be found at twimlai.com/go/497.

1 Juli 202142min

Skip-Convolutions for Efficient Video Processing with Amir Habibian - #496

Skip-Convolutions for Efficient Video Processing with Amir Habibian - #496

Today we kick off our CVPR coverage joined by Amir Habibian, a senior staff engineer manager at Qualcomm Technologies.  In our conversation with Amir, whose research primarily focuses on video perception, we discuss a few papers they presented at the event. We explore the paper Skip-Convolutions for Efficient Video Processing, which looks at training discrete variables to end to end into visual neural networks. We also discuss his work on his FrameExit paper, which proposes a conditional early exiting framework for efficient video recognition.  The complete show notes for this episode can be found at twimlai.com/go/496.

28 Juni 202147min

Advancing NLP with Project Debater w/ Noam Slonim - #495

Advancing NLP with Project Debater w/ Noam Slonim - #495

Today we’re joined by Noam Slonim, the principal investigator of Project Debater at IBM Research.  In our conversation with Noam, we explore the history of Project Debater, the first AI system that can “debate” humans on complex topics. We also dig into the evolution of the project, which is the culmination of 7 years and over 50 research papers, and eventually becoming a Nature cover paper, “An Autonomous Debating System,” which details the system in its entirety.  Finally, Noam details many of the underlying capabilities of Debater, including the relationship between systems preparation and training, evidence detection, detecting the quality of arguments, narrative generation, the use of conventional NLP methods like entity linking, and much more. The complete show notes for this episode can be found at twimlai.com/go/495.

24 Juni 202151min

Bringing AI Up to Speed with Autonomous Racing w/ Madhur Behl - #494

Bringing AI Up to Speed with Autonomous Racing w/ Madhur Behl - #494

Today we’re joined by Madhur Behl, an Assistant Professor in the department of computer science at the University of Virginia.  In our conversation with Madhur, we explore the super interesting work he’s doing at the intersection of autonomous driving, ML/AI, and Motorsports, where he’s teaching self-driving cars how to drive in an agile manner. We talk through the differences between traditional self-driving problems and those encountered in a racing environment, the challenges in solving planning, perception, control.  We also discuss their upcoming race at the Indianapolis Motor Speedway, where Madhur and his students will compete for 1 million dollars in the world’s first head-to-head fully autonomous race, and how they’re preparing for it.

21 Juni 202151min

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