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
Aug 16, 2021 • 42min
Applications of Variational Autoencoders and Bayesian Optimization with José Miguel Hernández Lobato - #510
José Miguel Hernández Lobato, a machine learning lecturer at the University of Cambridge, shares insights on the fusion of Bayesian learning and deep learning in molecular design. He discusses innovative methods for predicting chemical reactions and explores the challenges of sample efficiency in reinforcement learning. José elaborates on deep generative models, their role in molecular property prediction, and strategies for enhancing the robustness of machine learning through invariant risk minimization. His research reveals exciting pathways in optimizing molecule discovery.
19 snips
Aug 12, 2021 • 47min
Codex, OpenAI’s Automated Code Generation API with Greg Brockman - #509
Greg Brockman, co-founder and CTO of OpenAI, dives into the innovative Codex API, which extends the capabilities of GPT-3 for coding tasks. He discusses the key differences in performance between Codex and GPT-3, emphasizing Codex's reliability with programming instructions. The potential of Codex as an educational tool is highlighted, alongside its implications for job automation and fairness in AI. Brockman also details the Copilot collaboration with GitHub and the exciting rollout strategies for engaging users with this groundbreaking technology.
Aug 9, 2021 • 32min
Spatiotemporal Data Analysis with Rose Yu - #508
In this engaging discussion, Rose Yu, an assistant professor at UC San Diego, delves into her groundbreaking work on machine learning for spatiotemporal data. She explains how integrating physical principles and symmetry enhances neural network architectures. The conversation covers innovative approaches in climate modeling, including turbulent prediction and the application of Physics Guided AI. Rose also addresses uncertainty quantification in models, crucial for applications like COVID-19 forecasting, showcasing the importance of confidence in predictions.
8 snips
Aug 5, 2021 • 51min
Parallelism and Acceleration for Large Language Models with Bryan Catanzaro - #507
In this engaging discussion, Bryan Catanzaro, VP of Applied Deep Learning Research at NVIDIA, delves into high-performance computing's intersection with AI. He reveals insights about the Megatron framework for training large language models and the three parallelism types that enhance model efficiency. Bryan also highlights the challenges in supercomputing, the pioneering Deep Learning Super Sampling technology for gaming graphics, and innovative methods for generating high-resolution synthetic data to improve image quality in AI applications.
Aug 2, 2021 • 54min
Applying the Causal Roadmap to Optimal Dynamic Treatment Rules with Lina Montoya - #506
Join Lina Montoya, a postdoctoral researcher at UNC Chapel Hill focused on causal inference in precision medicine. She dives into her innovative work on Optimal Dynamic Treatment rules, particularly in the U.S. criminal justice system. Lina discusses the critical role of neglected assumptions in causal inference, the super learner algorithm's impact on predicting treatment effectiveness, and future research directions aimed at optimizing therapy delivery in resource-constrained settings like rural Kenya. This engaging discussion highlights the intersection of AI, healthcare, and justice.
Jul 29, 2021 • 51min
Constraint Active Search for Human-in-the-Loop Optimization with Gustavo Malkomes - #505
Gustavo Malkomes, a research engineer at Intel with expertise in active learning and multi-objective optimization, dives into an innovative algorithm for multiobjective experimental design. He discusses how his work allows teams to explore multiple metrics simultaneously and efficiently, enhancing human-in-the-loop optimization. The conversation covers the balance between competing goals, the significance of stable solutions, and the fascinating applications of his research in real-world scenarios, such as optimization and drug discovery.
Jul 26, 2021 • 37min
Fairness and Robustness in Federated Learning with Virginia Smith -#504
Virginia Smith, an assistant professor at Carnegie Mellon University, delves into her innovative work on federated learning. She discusses her research on fairness and robustness, highlighting the challenges of maintaining model performance across diverse data inputs. The conversation touches on her findings from the paper 'Ditto', exploring the trade-offs in AI ethics. Additionally, she shares insights on leveraging data heterogeneity in federated clustering to enhance model effectiveness and the balance between privacy and robust learning.
Jul 22, 2021 • 41min
Scaling AI at H&M Group with Errol Koolmeister - #503
Errol Koolmeister, head of AI Foundation at H&M Group, shares insights on the fashion retail giant's transformative AI journey. He discusses implementing AI for fashion forecasting and pricing, emphasizing the significance of data accessibility and stakeholder engagement. Highlighting the challenges of scaling AI, Errol explains the importance of balancing simplicity with complexity in modeling. He also addresses managing AI initiatives within a large organization, focusing on building a robust infrastructure and fostering an 'AI-first' culture.
Jul 19, 2021 • 49min
Evolving AI Systems Gracefully with Stefano Soatto - #502
Stefano Soatto, VP of AI Application Science at AWS and a professor at UCLA, dives into the fascinating world of Graceful AI. He discusses the challenges of evolving AI in real-world applications while avoiding the pitfalls of constant retraining. Topics include the critical timing of regularization in deep learning, the parallels between model compression and material science, and the intricacies of model reliability. Stefano also unpacks innovations like focal distillation and their potential to enhance lifelong learning in AI systems.
Jul 15, 2021 • 45min
ML Innovation in Healthcare with Suchi Saria - #501
In this engaging discussion, Suchi Saria, Founder and CEO of Bayesian Health and an esteemed professor at Johns Hopkins University, shares her journey at the intersection of machine learning and healthcare. She highlights the slow acceptance of AI in medical practice and discusses pockets of success in the field. Saria elaborates on groundbreaking advancements in sepsis detection and the challenges of integrating ML tools into clinical workflows. Finally, she envisions a future where improved data accessibility drives better patient outcomes in healthcare.
