The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Sam Charrington
Machine 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.
Episodes
Mentioned books
Sep 14, 2020 • 43min
That's a VIBE: ML for Human Pose and Shape Estimation with Nikos Athanasiou, Muhammed Kocabas, Michael Black - #409
Join PhD students Nikos Athanasiou and Muhammed Kocabas, alongside Michael Black, the director of the Max Planck Institute for Intelligent Systems, as they unveil their groundbreaking VIBE research. They discuss the innovative adversarial learning framework for human pose and shape estimation and the significance of the AMASS dataset. The trio also dives into advancements in transforming sparse motion capture data into detailed 3D models and leveraging models with self-attention for enhanced accuracy in human motion understanding. A must-listen for AI enthusiasts!
Sep 10, 2020 • 35min
3D Deep Learning with PyTorch 3D w/ Georgia Gkioxari - #408
Georgia Gkioxari, a research scientist at Facebook AI Research, shares her insights on the groundbreaking open-source library PyTorch3D. She discusses her journey from traditional object recognition to deep learning innovations and the evolution of 3D understanding. Georgia highlights the user experience and modularity of PyTorch3D, revealing its role in enhancing machine learning capabilities. She also reflects on her responsibilities as co-chair for CVPR 2021 and the need for modernizing academic peer review processes to adapt to growing challenges in research.
9 snips
Sep 7, 2020 • 57min
What are the Implications of Algorithmic Thinking? with Michael I. Jordan - #407
In a fascinating discussion, Michael I. Jordan, a Distinguished Professor at UC Berkeley and a leader in AI and machine learning, shares insights from his diverse academic journey. He delves into how philosophy and cognitive sciences shaped his understanding of uncertainty in tech. The conversation touches on the valuation of data, empowering young artists through AI, and creating meaningful economic markets. Michael also emphasizes the need for equitable representation in AI development and the risks of unregulated algorithmic systems.
Sep 3, 2020 • 42min
Beyond Accuracy: Behavioral Testing of NLP Models with Sameer Singh - #406
Sameer Singh, an assistant professor at UC Irvine, specializes in interpretable machine learning for NLP. He discusses the groundbreaking CheckList tool for robust behavioral testing of NLP models, stressing the importance of understanding model limitations beyond mere accuracy. Sameer reflects on the evolving landscape of AI, the relevance of his co-authored LIME paper in model explainability, and the potential of embodied AI in enhancing our understanding of complex machine learning systems. It's a thoughtful dive into the future of AI evaluation methods.
Aug 31, 2020 • 43min
How Machine Learning Powers On-Demand Logistics at Doordash with Gary Ren - #405
Gary Ren, a machine learning engineer at DoorDash, dives into the transformative role of machine learning in logistics. He shares insights on optimizing route planning and balancing the three-sided marketplace of consumers, dashers, and merchants. The conversation highlights the integration of predictive modeling for delivery timings and the innovative use of reinforcement learning to boost efficiency. Plus, Ren discusses challenges in unpredictable environments, including how to adapt to real-time conditions to improve customer satisfaction.
8 snips
Aug 27, 2020 • 45min
Machine Learning as a Software Engineering Discipline with Dillon Erb - #404
Dillon Erb, Co-founder and CEO of Paperspace, discusses how to tackle the challenges of building scalable machine learning workflows. He emphasizes the importance of integrating software engineering principles into machine learning, focusing on reproducibility and effective workflow management. Dillon also highlights how MLOps platforms can assist developers, and explores the relationship between Gradient and Kubeflow. Additionally, he touches on innovations in machine learning visualization that enhance practical applications across various industries.
Aug 24, 2020 • 53min
AI and the Responsible Data Economy with Dawn Song - #403
Dawn Song, a Professor of Computer Science at UC Berkeley and CEO of Oasis Labs, dives into the future of data privacy and security. She discusses creating a responsible data economy that empowers users while leveraging techniques like blockchain and differential privacy. The conversation highlights the risks of adversarial attacks on AI, and how innovations in homomorphic encryption can secure sensitive data. Dawn also shares insights on balancing privacy with effective COVID-19 contact tracing, emphasizing the importance of technology in protecting personal information.
Aug 20, 2020 • 41min
Relational, Object-Centric Agents for Completing Simulated Household Tasks with Wilka Carvalho - #402
In a fascinating discussion, Wilka Carvalho, a PhD student at the University of Michigan, delves into his research on AI agents designed for completing household tasks. He explores the challenges of object interaction and how his Relational Object Model Learning Agent (ROMA) tackles these issues. Carvalho emphasizes the importance of representation learning and real-world data in creating effective agents. He also shares insights on achieving high sample efficiency and the methodologies for training robotic agents in complex environments.
Aug 17, 2020 • 1h 27min
Model Explainability Forum - #401
Join a stellar panel featuring Raid Ghani from Carnegie Mellon, Solon Barrokas of Cornell and Microsoft, IBM's Kush Varshney, startup CEO Alyssa Labgenova, and Harvard's Hima Lakaraju. They tackle pressing issues in the realm of model explainability. Discussions dive into stakeholder-driven approaches, counterfactual explanations, and the impact of legal frameworks on automated decision-making. The experts also highlight vulnerabilities in AI explanations and the critical need for trust and fairness, emphasizing collaboration to enhance understanding and improve outcomes.
Aug 13, 2020 • 59min
What NLP Tells Us About COVID-19 and Mental Health with Johannes Eichstaedt - #400
In this conversation with Johannes Eichstaedt, an Assistant Professor of Psychology at Stanford University, they explore how he blends physics and psychology to analyze mental health trends using big data. Johannes discusses the fascinating use of Twitter data to uncover psychological impacts during COVID-19, revealing insights into societal behavior changes and mental health fluctuations. He also highlights the challenges of capturing nuanced language variations across different communities, shedding light on the dynamic nature of social norms during the pandemic.
