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
22 snips
Mar 7, 2022 • 51min
100x Improvements in Deep Learning Performance with Sparsity, w/ Subutai Ahmad - #562
Subutai Ahmad, VP of research at Numenta, dives into the fascinating intersection of neuroscience and machine learning. He discusses the significance of cortical columns and how mimicking their structures can enhance AI. Ahmad explores the revolutionary concept of sparsity, highlighting how sparse networks can drive efficiency in deep learning, including large language models. The conversation also touches on the importance of 3D understanding and sensory-motor capabilities in models, offering insights that could reshape the future of intelligent systems.
Feb 28, 2022 • 44min
Scaling BERT and GPT for Financial Services with Jennifer Glore - #561
Jennifer Glore, VP of Customer Engineering at SambaNova Systems, dives into the innovative world of AI in finance. She discusses how SambaNova has created a GPT model tailored for financial services, highlighting the industry's exciting yet challenging journey with transformers. Jennifer shares insights on hardware-software integration and the hurdles organizations face in deploying advanced AI models. Additionally, they explore data sourcing dilemmas and the importance of ongoing model adaptation to ensure accuracy and governance in AI applications.
Feb 21, 2022 • 1h 18min
Trends in Deep Reinforcement Learning with Kamyar Azizzadenesheli - #560
Kamyar Azizzadenesheli, an Assistant Professor at Purdue University and an expert in deep reinforcement learning, dives deep into the evolution of the field. He discusses the interplay between reinforcement learning, robotics, and control theory, and highlights the importance of stable controllers for real-world applications. Kamyar predicts trends like self-supervised learning's rise and emphasizes the need for specialized algorithms. The conversation touches on risk-sensitive reinforcement learning and the innovations transforming decision-making in high-stakes environments.
Feb 14, 2022 • 52min
Deep Reinforcement Learning at the Edge of the Statistical Precipice with Rishabh Agarwal - #559
Rishabh Agarwal, a research scientist at Google Brain in Montreal, dives into his award-winning paper on deep reinforcement learning. The discussion reveals how traditional performance evaluations can lead to misleading conclusions due to random seed variability. Rishabh highlights the challenges of current benchmarking methods, advocating for better reporting practices. With insights on the importance of uncertainty in results, he calls for a shift in academic standards to improve research integrity. Open-source tools aim to enhance evaluation methods, fostering greater transparency in the field.
Feb 7, 2022 • 53min
Designing New Energy Materials with Machine Learning with Rafael Gomez-Bombarelli - #558
Rafael Gomez-Bombarelli, an MIT assistant professor in material science, dives into the fusion of machine learning and atomistic simulations for energy materials. He discusses virtual screening and inverse design techniques, sharing insights on their unique challenges. The conversation highlights generative models and the crucial role of training data in simulations. Rafael also explains how simulation results inform modeling efforts and the significance of hyperparameter optimization in making predictive models more effective for material design.
Jan 31, 2022 • 34min
Differentiable Programming for Oceanography with Patrick Heimbach - #557
Patrick Heimbach, a professor at the University of Texas, dives deep into the intersection of machine learning and oceanography. He discusses the challenges of simulating ocean circulation and how machine learning can significantly improve model accuracy. The importance of differentiable programming in integrating observational data with physical models is highlighted. Heimbach also explores modular oceanographic modeling and how machine learning assists in analyzing ice sheet dynamics and calving processes, showcasing a bright future for these technologies.
19 snips
Jan 27, 2022 • 1h 9min
Trends in Machine Learning & Deep Learning with Zachary Lipton - #556
Zachary Lipton, an assistant professor at Carnegie Mellon University and AI expert, dives into the evolving landscape of machine learning and deep learning. He discusses how NLP is dominating AI, highlights breakthroughs like DeepMind's AlphaFold for protein folding, and critiques the current peer-review system. Lipton emphasizes the significance of fairness and causal insights in AI, addressing challenges in incorporating ethical considerations. He reflects on the need for innovation amidst established techniques, revealing exciting opportunities for 2022 and beyond.
Jan 24, 2022 • 36min
Solving the Cocktail Party Problem with Machine Learning, w/ Jonathan Le Roux - #555
Jonathan Le Roux, a Senior Principal Research Scientist at Mitsubishi Electric Research Laboratories, dives into the fascinating world of the cocktail party problem, where he tackles the challenge of separating speech from noise and other voices. He discusses his innovative paper on the 'cocktail fork problem,' which categorizes audio into speech, music, and sound effects. Le Roux explores the evolution of machine learning techniques in audio processing and reveals insights on how advanced models can enhance clarity in noisy environments.
Jan 20, 2022 • 36min
Machine Learning for Earthquake Seismology with Karianne Bergen - #554
In this engaging discussion, Karianne Bergen, an assistant professor at Brown University specializing in earthquake seismology and machine learning, delves into her innovative research. She shares insights on using machine learning to detect weak seismic signals and the challenges of distinguishing real earthquakes from noise. Karianne also emphasizes the need for tailored machine learning solutions in seismology and highlights the shifting landscape of scientists' understanding of machine learning, advocating for stronger educational frameworks in the field.
11 snips
Jan 17, 2022 • 46min
The New DBfication of ML/AI with Arun Kumar - #553
In this engaging conversation, Arun Kumar, an associate professor at UC San Diego known for his work on Cerebro and SortingHat, discusses the exciting concept of 'DBfication' in machine learning. He emphasizes how merging ML and database technologies can enhance efficiency and scalability. Arun shares insights on his innovative tools, Cerebro for optimal model selection and SortingHat for automating data prep. Their integration could significantly improve machine learning workflows, showcasing the future potential of MLOps and collaborative efforts in both fields.
