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

Latest Episodes:

The Future of Human-Machine Interaction with Dan Bohus and Siddhartha Sen - #499 Jul 08, 2021

Show notes

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.

Vector Quantization for NN Compression with Julieta Martinez - #498 Jul 05, 2021

Show notes

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.

Deep Unsupervised Learning for Climate Informatics with Claire Monteleoni - #497 Jul 01, 2021

Show notes

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.

Skip-Convolutions for Efficient Video Processing with Amir Habibian - #496 Jun 28, 2021

Show notes

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.

Advancing NLP with Project Debater w/ Noam Slonim - #495 Jun 24, 2021

Show notes

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.

Bringing AI Up to Speed with Autonomous Racing w/ Madhur Behl - #494 Jun 21, 2021

Show notes

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.

AI and Society: Past, Present and Future with Eric Horvitz - #493 Jun 17, 2021

Show notes

Today we continue our AI Innovation series joined by Microsoft’s Chief Scientific Officer, Eric Horvitz.

In our conversation with Eric, we explore his tenure as AAAI president and his focus on the future of AI and its ethical implications, the scope of the study on the topic, and how drastically the AI and machine learning landscape has changed since 2009. We also discuss Eric’s role at Microsoft and the Aether committee that has advised the company on issues of responsible AI since 2017.

Finally, we talk through his recent work as a member of the National Security Commission on AI, where he helped commission a 750+ page report on topics including the Future of AI R&D, Building Trustworthy AI systems, civil liberties and privacy, and the challenging area of AI and autonomous weapons.

The complete show notes for this episode can be found at twimlai.com/go/493.

Agile Applied AI Research with Parvez Ahammad - #492 Jun 14, 2021

Show notes

Today we’re joined by Parvez Ahammad, head of data science applied research at LinkedIn.

In our conversation, Parvez shares his interesting take on organizing principles for his organization, starting with how data science teams are broadly organized at LinkedIn. We explore how they ensure time investments on long-term projects are managed, how to identify products that can help in a cross-cutting way across multiple lines of business, quantitative methodologies to identify unintended consequences in experimentation, and navigating the tension between research and applied ML teams in an organization. Finally, we discuss differential privacy, and their recently released GreyKite library, an open-source Python library developed to support forecasting.

The complete show note for this episode can be found at twimlai.com/go/492.

Haptic Intelligence with Katherine J. Kuchenbecker - #491 Jun 10, 2021

Show notes

Today we’re joined Katherine J. Kuchenbecker, director at the Max Planck Institute for Intelligent Systems and of the haptic intelligence department.

In our conversation, we explore Katherine’s research interests, which lie at the intersection of haptics (physical interaction with the world) and machine learning, introducing us to the concept of “haptic intelligence.” We discuss how ML, mainly computer vision, has been integrated to work together with robots, and some of the devices that Katherine’s lab is developing to take advantage of this research.

We also talk about hugging robots, augmented reality in robotic surgery, and the degree to which she studies human-robot interaction. Finally, Katherine shares with us her passion for mentoring and the importance of diversity and inclusion in robotics and machine learning.

The complete show notes for this episode can be found at twimlai.com/go/491.

Data Science on AWS with Chris Fregly and Antje Barth - #490 Jun 07, 2021

Show notes

Today we continue our coverage of the AWS ML Summit joined by Chris Fregly, a principal developer advocate at AWS, and Antje Barth, a senior developer advocate at AWS.

In our conversation with Chris and Antje, we explore their roles as community builders prior to, and since, joining AWS, as well as their recently released book Data Science on AWS. In the book, Chris and Antje demonstrate how to reduce cost and improve performance while successfully building and deploying data science projects.

We also discuss the release of their new Practical Data Science Specialization on Coursera, managing the complexity that comes with building real-world projects, and some of their favorite sessions from the recent ML Summit.

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