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 18, 2019 • 30min
Time Series Clustering for Monitoring Fueling Infrastructure Performance with Kalai Ramea - #300
Kalai Ramea, a data scientist at PARC, specializes in analyzing hydrogen fueling infrastructure. She shares her journey of purchasing a hydrogen car and the crucial research assessing fueling stations. Kalai discusses using temporal clustering to identify usage patterns at these stations, emphasizing the importance of reliability as their numbers grow. Her insights reveal how machine learning can improve station performance and inform policymakers on enhancing the adoption of zero-emission vehicles while addressing the intersection of transportation and energy systems.
Sep 13, 2019 • 41min
Swarm AI for Event Outcome Prediction with Gregg Willcox - TWIML Talk #299
Gregg Willcox, Director of Research and Development at Unanimous AI, specializes in Swarm AI, which draws on collective intelligence for enhanced decision-making. In this discussion, he reveals how a game-like platform can predict sports outcomes more accurately than traditional methods, including a notable success in March Madness predictions. The conversation also touches on the role of empathy in group voting and the algorithms driving swarm technology, showcasing how it can empower decision-making in various fields while addressing ethical considerations.
Sep 10, 2019 • 48min
Rebooting AI: What's Missing, What's Next with Gary Marcus - TWIML Talk #298
Gary Marcus, CEO of Robust.AI and an influential voice in AI and cognitive science, dives into the gaps in today's AI landscape. He discusses the importance of integrating cognitive principles, pointing out how infants' instinctive language skills can inspire better machine learning. Marcus highlights the limitations of reinforcement learning, advocating for a foundation in knowledge to enhance AI. He also critiques the narrow focus of current AI, emphasizing the need for trust and safety in autonomous systems for future advancements.
Sep 5, 2019 • 51min
DeepQB: Deep Learning to Quantify Quarterback Decision-Making with Brian Burke - TWIML Talk #297
Brian Burke, an Analytics Specialist at ESPN and former Navy pilot, connects the worlds of aviation and football through an analysis of quarterback decision-making. He discusses his innovative model, DeepQB, which leverages player tracking data to evaluate performance. Burke shares insights on the evolution of football analytics, the intricate challenges of quantifying decisions under pressure, and how machine learning can transform coaching strategies. His unique journey from jet pilot to sports analyst showcases the power of data-driven decision-making in football.
Sep 3, 2019 • 35min
Measuring Performance Under Pressure Using ML with Lotte Bransen - TWIML Talk #296
Lotte Bransen, a Scientific Researcher at SciSports specializing in soccer analytics, joins the discussion on how mental pressure impacts player performance. She delves into her research, 'Choke or Shine?', revealing insights into how players respond under stress. The conversation highlights the merging of mathematics with sports, the complexities of measuring player contributions, and the development of models to quantify pressure levels. Coaches can benefit from understanding which players excel when the stakes are high, shaping team strategies and player recruitment.
Aug 29, 2019 • 42min
Managing Deep Learning Experiments with Lukas Biewald - TWIML Talk #295
Lukas Biewald, CEO and co-founder of Weights & Biases, dives into the world of deep learning experiments. He shares how his company addresses the need for reproducibility and effective evaluation frameworks. The conversation covers the chaos of machine learning management and the importance of collaboration in improving workflows. Lukas also teases an upcoming ebook on data management and highlights the significance of version control in community-driven projects. Learn how his tools are reshaping the experimentation landscape in AI!
Aug 26, 2019 • 49min
Re-Architecting Data Science at iRobot with Angela Bassa - TWIML Talk #294
Angela Bassa, Director of Data Science at iRobot, shares her journey from academia to robotics, revealing how she built a data-driven culture at iRobot. She discusses the company's re-architecture and the innovative ways they taxonomize data science. Angela highlights the transition to Python, enhancing team collaboration and product development. The conversation also covers the crucial role of DevOps in integrating hardware, software, and cloud solutions, and the importance of rigorous testing for new technologies like SLAM in robots.
Aug 22, 2019 • 43min
Disentangled Representations & Google Research Football with Olivier Bachem - TWIML Talk #293
Olivier Bachem, a research scientist at Google AI's Brain team, dives into exciting advancements in reinforcement learning. He shares insights on Google Research Football, a unique environment designed for AI training that outshines traditional platforms like OpenAI Gym. Olivier discusses the intricacies of disentangled representations in high-dimensional data and the innovative challenges of developing soccer simulations. He also touches on recent updates, emphasizing collaborative learning and realistic gameplay dynamics in this pioneering research project.
Aug 19, 2019 • 50min
Neural Network Quantization and Compression with Tijmen Blankevoort - TWIML Talk #292
In this discussion, Tijmen Blankevoort, a staff engineer at Qualcomm, delves into the fascinating world of neural network compression and quantization. He explains how much ML models can be compressed without losing efficiency and outlines the best strategies for achieving this. The conversation also touches on the lottery ticket hypothesis, exploring how feature selection can optimize neural networks. Tijmen reveals challenges in automating compression, like error propagation, and introduces innovative data-free quantization techniques that enhance performance across various models.
Aug 15, 2019 • 40min
Identifying New Materials with NLP with Anubhav Jain - TWIML Talk #291
Join Anubhav Jain, a Staff Scientist at Lawrence Berkeley National Lab and leader of the Hacking Materials Research Group, as he dives into the intersection of materials science and natural language processing. He discusses his groundbreaking paper on using unsupervised word embeddings to analyze scientific literature, enabling advanced material discovery. Anubhav highlights innovative predictive methods and the significant role of NLP in identifying new functional materials, as well as the exciting potential of validating predictions with real experimental data.
