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
Oct 8, 2019 • 34min
Live from TWIMLcon! Operationalizing ML at Scale with Hussein Mehanna - #306
Join Hussein Mehanna, Head of ML and AI at Cruise, as he shares his journey from Facebook to Cruise, detailing the evolution of machine learning platforms. He dives into the challenges of scaling ML with innovation, showcasing his insights on optimizing workflows and collaboration in tech giants. Hussein also discusses the complexities of autonomous vehicles and predicts the future of machine learning in enterprises, underscoring the role of cloud services and collaborative tools like Kubeflow.
Oct 4, 2019 • 32min
Live from TWIMLcon! Encoding Company Culture in Applied AI Systems - #305
Deepak Agarwal, VP of Engineering at LinkedIn, dives into the synergy between company culture and applied AI systems. He explains how standardizing processes boosts productivity and ML ROI. The conversation highlights the Pro-ML initiative, which focuses on scaling machine learning systems by aligning tools with innovation. Agarwal also emphasizes the importance of a strong business case for tech transitions and the significance of thoughtful experimentation in driving meaningful insights within a centralized AI organization.
Oct 1, 2019 • 34min
Live from TWIMLcon! Overcoming the Barriers to Deep Learning in Production with Andrew Ng - #304
Andrew Ng, the Founder and CEO of Landing AI and a key figure behind Google Brain, delves into the intricate landscape of AI adoption in various industries. He shares insights on overcoming challenges in large enterprises and emphasizes the importance of education in navigating machine learning's complexities. The discussion touches on managing risks during model deployment and the necessity for collaborative tools in data management. Ng's thoughts on integrating AI across manufacturing, agriculture, and healthcare highlight the transformative potential of technology.
Sep 27, 2019 • 44min
The Future of Mixed-Autonomy Traffic with Alexandre Bayen - #303
Join Alexandre Bayen, Director of the Institute for Transportation Studies at UC Berkeley, as he dives into the future of mixed-autonomy traffic. He discusses the two major revolutions expected in the next 10-15 years surrounding AI's transformative role in traffic management. Discover how individual driving behaviors impact congestion, and learn about swarming strategies that self-driving cars can leverage. Bayen emphasizes the balance between innovation and safety, highlighting advancements in reinforcement learning for real-time traffic solutions.
Sep 25, 2019 • 44min
Deep Reinforcement Learning for Logistics at Instadeep with Karim Beguir - #302
Karim Beguir, Co-founder and CEO of InstaDeep, shares his journey from a small Tunisian town to leading innovations in AI for logistics. He discusses how deep reinforcement learning is revolutionizing decision-making in logistics, improving efficiency and cost-effectiveness. The conversation touches on the use of synthetic datasets for model training and the complexities of enhancing passenger experiences in ride-sharing. Karim emphasizes the significance of adaptive reward functions and the balance between learning-based and heuristic approaches to optimize outcomes.
Sep 19, 2019 • 40min
Deep Learning with Structured Data w/ Mark Ryan - #301
Mark Ryan, author of 'Deep Learning with Structured Data' and a member of IBM's Data and AI support team, shares insights on applying deep learning to structured data. He discusses creating predictive models using the Toronto streetcar network dataset, addressing challenges like data preparation and metadata integration. Mark emphasizes that deep learning doesn't always require massive datasets and highlights its potential in various sectors. He also details the interactive feedback process in his book's development and the advantages of collaborative learning in deep learning courses.
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.
