Past episodes listed in reverse chronological order. We have high-quality transcripts for a few episodes (see here).


Neil ThompsonThe Computational Limits of Deep Learning

Piero MolinoMaking deep learning accessible

Mayank KejriwalBuilding and deploying knowledge graphs

Murat ÖzbayoğluFinancial Time Series Forecasting with Deep Learning

Viral ShahA programming language for scientific machine learning and differentiable programming

Kira RadinskyUsing machine learning to modernize medical triage and monitoring systems

Max PumperlaConnecting Reinforcement Learning to Simulation Software

Weifeng ZhongUsing machine learning to detect shifts in government policy

Ofer RazonWhat is AI Assurance?

Alan NicholBest practices for building conversational AI applications

Paco Nathan and Ben Lorica in conversation with Jenn WebbTools for scaling machine learning

Joel GrusFrom Python beginner to seasoned software engineer

Bruno GonçalvesAssessing Models and Simulations of Epidemic Infectious Diseases

Karthik Ramasamy and Arun KejariwalImproving the hiring pipeline for software engineers

Lauren KunzeHow to build state-of-the-art chatbots

Ameet TalwalkarDemocratizing Machine Learning

Denise GosnellHow graph technologies are being used to solve complex business problems

Amy HeineikeMachines for unlocking the deluge of COVID-19 papers, articles, and conversations

Christopher NguyenDesigning machine learning models for both consumer and industrial applications

Matthew HonnibalBuilding open source developer tools for language applications

Chris WigginsViewing machine learning and data science applications as sociotechnical systems

Andrew BurtIdentifying and mitigating liabilities and risks associated with AI

Arun Verma (in conversation with Jenn Webb) → How machine learning is being used in quantitative finance

Harish DoddiUnderstanding machine learning model governance

Wes McKinneyImproving performance and scalability of data science libraries

Pete WardenWhy TinyML will be huge

Evan SparksAn open source platform for training deep learning models

Kenneth StanleyAlgorithms that continually invent both problems and solutions

Bruno GonçalvesComputational Models and Simulations of Epidemic Infectious Diseases

Robert MunroHuman-in-the-loop machine learning

Chris NicholsonNext-generation simulation software will incorporate deep reinforcement learning

Solmaz ShahalizadehBusiness at the speed of AI: Lessons from Shopify

Edo LibertyHow deep learning is being used in search and information retrieval

Alejandro SaucedoThe responsible development, deployment and operation of machine learning systems

Edmon BegoliHyperscaling natural language processing

Krishna GadeWhat businesses need to know about model explainability

Dean WamplerScalable Machine Learning, Scalable Python, For Everyone

Dafna ShahafComputational humanness, analogy and innovation, and soft concepts

David Talby → Building domain specific natural language applications

Morten DahlThe state of privacy-preserving machine learning

Sijie GuoTaking messaging and data ingestion systems to the next level

Bahman BahmaniBusiness at the speed of AI: Lessons from Rakuten

Nir ShavitThe combination of the right software and commodity hardware will prove capable of handling most machine learning tasks


Ben and Mikio BraunKey AI and Data Trends for 2020

Rajat MongaThe evolution of TensorFlow and of machine learning infrastructure

Reza ZadehBuilding large-scale, real-time computer vision applications

Paco NathanTaking stock of foundational tools for analytics and machine learning