The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Today we’re joined by Noam Slonim, the principal investigator of Project Debater at IBM Research. In our conversation with Noam, we explore the history of Project Debater, the first AI system that can “debate” humans on complex topics. We also dig into the evolution of the project, which is the culmination of 7 years and over 50 research papers, and eventually becoming a Nature cover paper, “An Autonomous Debating System,” which details the system in its entirety. Finally, Noam details many of the underlying capabilities of Debater, including the relationship between systems preparation and training, evidence detection, detecting the quality of arguments, narrative generation, the use of conventional NLP methods like entity linking, and much more.The complete show notes for this episode can be found at twimlai.com/go/495.

Madhur Behl, an Assistant Professor at the University of Virginia, dives into the thrilling world of autonomous racing and AI. He shares insights on how training AI for high-speed environments differs from traditional driving tasks. The conversation covers the unique challenges of perception and planning in racing, as well as the innovative techniques of sensor fusion versus vision-only approaches. Behl also hints at their upcoming race at the Indianapolis Motor Speedway, where his team aims for a million-dollar prize with their fully autonomous vehicle.

Eric Horvitz, Chief Scientific Officer at Microsoft, delves into the future of AI and its ethical implications. He shares insights from his time as AAAI president and discusses the transformation of AI since 2009. The conversation highlights the critical role of responsible AI development, particularly through the Aether committee. Horvitz also addresses the National Security Commission on AI's comprehensive report covering AI R&D, trustworthy systems, and the ethical ramifications of technology like facial recognition in law enforcement.

Parvez Ahammad, Head of Data Science Applied Research at LinkedIn, shares his insights on organizing data science teams for success. He discusses the balance of long-term project investments while navigating the challenges of experimentation. Parvez also delves into the impact of differential privacy on member data and the ambitious launch of the GreyKite forecasting library. The conversation highlights the dynamic relationship between applied research and engineering in AI, emphasizing the need for strategic alignment and effective team dynamics in driving innovation.

Join Katherine J. Kuchenbecker, director at the Max Planck Institute for Intelligent Systems, as she dives into the fascinating world of haptic intelligence. Discover how she merges haptics with machine learning to enhance human-robot interactions, including a robotic finger that feels and sees! Hear about her quirky HuggyBot, designed for personalized hugs. Katherine also shares her passion for mentoring and the crucial need for diversity in robotics, revealing inspiring insights from her journey in this evolving field.

Joining the conversation are Chris Fregly, Principal Developer Advocate at AWS and co-author of 'Data Science on AWS,' and Antje Barth, Senior Developer Advocate and co-author as well. They dive into their new book, revealing strategies for reducing costs and improving performance in data science projects. The duo also discusses their Practical Data Science Specialization on Coursera, community-building initiatives, and innovative approaches like multi-armed bandit strategies for model optimization. Insights from the recent ML Summit showcase advancements in making AI more accessible.

Ziad Asghar, Vice President of Product Management at Qualcomm, discusses the dynamic interplay of AI and 5G, showcasing how Qualcomm's Snapdragon platform fuels mobile AI advancements. He dives into the evolution of the Cloud AI 100 and its impact on smart city technologies. Ziad also highlights the significance of federated learning and the importance of privacy in data security. Additionally, he explores AI innovations in automotive technology, including collaborations with NASA to enhance vehicle autonomy and safety.

Nir Bar-Lev, co-founder and CEO of ClearML, shares insights from his extensive tech background including time at Google. He discusses the evolving landscape of machine learning platforms and the critical decision between building versus buying solutions. Nir emphasizes the importance of effective experiment management, the risks of relying solely on cloud vendors, and the balance needed to combat overfitting. He also touches on advancements in federated learning and how ClearML integrates innovative techniques to empower businesses in their AI journeys.

In this engaging discussion, Alex Smola, Vice President and Distinguished Scientist at AWS AI, explores cutting-edge AI research. He delves into deep learning on graphs, highlighting its role in enhancing data interpretation and applications like fraud detection. Alex also discusses the significance of AutoML, designed to make machine learning more accessible. He introduces Granger causality in causal modeling and shares insights about the growing AWS ML Summit, showcasing speaker highlights and exciting trends in AI.

Sean Taylor, Staff Data Scientist at Lyft Rideshare Labs, shares his journey from lab director to hands-on innovator. He dives into the moonshot approaches his team takes towards marketplace experimentation and forecasting. The conversation highlights the significance of causality in their modeling efforts and the challenges of balancing supply and demand. Moreover, he discusses the application of neural networks for decision-making, emphasizing collaboration and the transformation of traditional statistical methods to drive business insights.