loader from loading.io

Adversarial Examples Are Not Bugs, They Are Features with Aleksander Madry - #369

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

Release Date: 04/27/2020

Bridging The Gap Between Machine Learning and the Life Sciences with Artur Yakimovich - #411 show art Bridging The Gap Between Machine Learning and the Life Sciences with Artur Yakimovich - #411

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

Today we’re joined by Artur Yakimovich, Co-Founder at Artificial Intelligence for Life Sciences and a visiting scientist in the Lab for Molecular Cell Biology at University College London. In our conversation with Artur, we explore the gulf that exists between life science researchers and the tools and applications used by computer scientists.  While Artur’s background is in viral chemistry, he has since transitioned to a career in computational biology to “see where chemistry stopped, and biology started.” We discuss his work in that middle ground, looking at quite a few of his...

info_outline
Understanding Cultural Style Trends with Computer Vision w/ Kavita Bala - #410 show art Understanding Cultural Style Trends with Computer Vision w/ Kavita Bala - #410

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

Today we’re joined by Kavita Bala, the Dean of Computing and Information Science at Cornell University.  Kavita, whose research explores the overlap of computer vision and computer graphics, joined us to discuss a few of her projects, including GrokStyle, a startup that was recently acquired by Facebook and is currently being deployed across their Marketplace features. We also talk about StreetStyle/GeoStyle, projects focused on using social media data to find style clusters across the globe.  Kavita shares her thoughts on the privacy and security implications, progress with...

info_outline
That's a VIBE: ML for Human Pose and Shape Estimation with Nikos Athanasiou, Muhammed Kocabas, Michael Black - #409 show art That's a VIBE: ML for Human Pose and Shape Estimation with Nikos Athanasiou, Muhammed Kocabas, Michael Black - #409

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

Today we’re joined by Nikos Athanasiou, Muhammed Kocabas, Ph.D. students, and Michael Black, Director of the Max Planck Institute for Intelligent Systems.  We caught up with the group to explore their paper VIBE: Video Inference for Human Body Pose and Shape Estimation, which they submitted to CVPR 2020. In our conversation, we explore the problem that they’re trying to solve through an adversarial learning framework, the datasets (AMASS) that they’re building upon, the core elements that separate this work from its predecessors in this area of research, and the results they’ve...

info_outline
3D Deep Learning with PyTorch 3D w/ Georgia Gkioxari - #408 show art 3D Deep Learning with PyTorch 3D w/ Georgia Gkioxari - #408

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

Today we’re joined by Georgia Gkioxari, a research scientist at Facebook AI Research.  Georgia was hand-picked by the TWIML community to discuss her work on the recently released open-source library PyTorch3D. In our conversation, Georgia describes her experiences as a computer vision researcher prior to the 2012 deep learning explosion, and how the entire landscape has changed since then.  Georgia walks us through the user experience of PyTorch3D, while also detailing who the target audience is, why the library is useful, and how it fits in the broad goal of giving computers...

info_outline
What are the Implications of Algorithmic Thinking? with Michael I. Jordan - #407 show art What are the Implications of Algorithmic Thinking? with Michael I. Jordan - #407

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

Today we’re joined by the legendary Michael I. Jordan, Distinguished Professor in the Departments of EECS and Statistics at UC Berkeley.  Michael was gracious enough to connect us all the way from Italy after being named recipient. In our conversation with Michael, we explore his career path, and how his influence from other fields like philosophy shaped his path.  We spend quite a bit of time discussing his current exploration into the intersection of economics and AI, and how machine learning systems could be used to create value and empowerment across many industries through...

info_outline
Beyond Accuracy: Behavioral Testing of NLP Models with Sameer Singh - #406 show art Beyond Accuracy: Behavioral Testing of NLP Models with Sameer Singh - #406

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

Today we’re joined by Sameer Singh, an assistant professor in the department of computer science at UC Irvine.  Sameer’s work centers on large-scale and interpretable machine learning applied to information extraction and natural language processing. We caught up with Sameer right after he was awarded the best paper award at ACL 2020 for his work on Beyond Accuracy: Behavioral Testing of NLP Models with CheckList. In our conversation, we explore CheckLists, the task-agnostic methodology for testing NLP models introduced in the paper. We also discuss how well we understand the cause of...

info_outline
How Machine Learning Powers On-Demand Logistics at Doordash with Gary Ren - #405 show art How Machine Learning Powers On-Demand Logistics at Doordash with Gary Ren - #405

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

Today we’re joined by Gary Ren, a machine learning engineer for the logistics team at DoorDash.  In our conversation, we explore how machine learning powers the entire logistics ecosystem. We discuss the stages of their “marketplace,” and how using ML for optimized route planning and matching affects consumers, dashers, and merchants. We also talk through how they use traditional mathematics, classical machine learning, potential use cases for reinforcement learning frameworks, and challenges to implementing these explorations.   The complete show notes for this episode...

info_outline
Machine Learning as a Software Engineering Discipline with Dillon Erb - #404 show art Machine Learning as a Software Engineering Discipline with Dillon Erb - #404

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

Today we’re joined by Dillon Erb, Co-founder & CEO of Paperspace. We’ve followed Paperspace since their origins offering GPU-enabled compute resources to data scientists and machine learning developers, to the release of their Jupyter-based Gradient service. Our conversation with Dillon centered on the challenges that organizations face building and scaling repeatable machine learning workflows, and how they’ve done this in their own platform by applying time-tested software engineering practices.  We also discuss the importance of reproducibility in production machine learning...

info_outline
AI and the Responsible Data Economy with Dawn Song - #403 show art AI and the Responsible Data Economy with Dawn Song - #403

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

Today we’re joined by Professor of Computer Science at UC Berkeley, Dawn Song. Dawn’s research is centered at the intersection of AI, deep learning, security, and privacy. She’s currently focused on bringing these disciplines together with her startup, Oasis Labs.  In our conversation, we explore their goals of building a ‘platform for a responsible data economy,’ which would combine techniques like differential privacy, blockchain, and homomorphic encryption. The platform would give consumers more control of their data, and enable businesses to better utilize data in a...

info_outline
Relational, Object-Centric Agents for Completing Simulated Household Tasks with Wilka Carvalho - #402 show art Relational, Object-Centric Agents for Completing Simulated Household Tasks with Wilka Carvalho - #402

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

Today we’re joined by Wilka Carvalho, a PhD student at the University of Michigan, Ann Arbor. In our conversation, we focus on his paper ‘ROMA: A Relational, Object-Model Learning Agent for Sample-Efficient Reinforcement Learning.’ In the paper, Wilka explores the challenge of object interaction tasks, focusing on every day, in-home functions. We discuss how he’s addressing the challenge of ‘object-interaction’ tasks, the biggest obstacles he’s run into along the way.

info_outline
 
More Episodes

Today we’re joined by Aleksander Madry, Faculty in the MIT EECS Department, a member of CSAIL and of the Theory of Computation group. Aleksander, whose work is more on the theoretical side of machine learning research, walks us through his paper “Adversarial Examples Are Not Bugs, They Are Features,” which was published previously presented at last year’s NeurIPS conference. 

In our conversation, we explore the idea of adversarial examples in machine learning systems being features, with results that might be undesirable, but still working as designed. We talk through what we expect these systems to do, vs what they’re actually doing, if we’re able to characterize these patterns, and what makes them compelling, and if the insights from the paper will inform opinions on either side of the deep learning debate.

The complete show notes for this can be found at twimlai.com/talk/369.