Past episodes listed in reverse chronological order. We have high-quality transcripts for a few episodes (see here).
2020
Neil Thompson → The Computational Limits of Deep Learning
Piero Molino → Making deep learning accessible
Mayank Kejriwal → Building and deploying knowledge graphs
Murat Özbayoğlu → Financial Time Series Forecasting with Deep Learning
Viral Shah → A programming language for scientific machine learning and differentiable programming
Kira Radinsky → Using machine learning to modernize medical triage and monitoring systems
Max Pumperla → Connecting Reinforcement Learning to Simulation Software
Weifeng Zhong → Using machine learning to detect shifts in government policy
Ofer Razon → What is AI Assurance?
Alan Nichol → Best practices for building conversational AI applications
Paco Nathan and Ben Lorica in conversation with Jenn Webb → Tools for scaling machine learning
Joel Grus → From Python beginner to seasoned software engineer
Bruno Gonçalves → Assessing Models and Simulations of Epidemic Infectious Diseases
Karthik Ramasamy and Arun Kejariwal → Improving the hiring pipeline for software engineers
Lauren Kunze → How to build state-of-the-art chatbots
Ameet Talwalkar → Democratizing Machine Learning
Denise Gosnell → How graph technologies are being used to solve complex business problems
Amy Heineike → Machines for unlocking the deluge of COVID-19 papers, articles, and conversations
Christopher Nguyen → Designing machine learning models for both consumer and industrial applications
Matthew Honnibal → Building open source developer tools for language applications
Chris Wiggins → Viewing machine learning and data science applications as sociotechnical systems
Andrew Burt → Identifying 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 Doddi → Understanding machine learning model governance
Wes McKinney → Improving performance and scalability of data science libraries
Pete Warden → Why TinyML will be huge
Evan Sparks → An open source platform for training deep learning models
Kenneth Stanley → Algorithms that continually invent both problems and solutions
Bruno Gonçalves → Computational Models and Simulations of Epidemic Infectious Diseases
Robert Munro → Human-in-the-loop machine learning
Chris Nicholson → Next-generation simulation software will incorporate deep reinforcement learning
Solmaz Shahalizadeh → Business at the speed of AI: Lessons from Shopify
Edo Liberty → How deep learning is being used in search and information retrieval
Alejandro Saucedo → The responsible development, deployment and operation of machine learning systems
Edmon Begoli → Hyperscaling natural language processing
Krishna Gade → What businesses need to know about model explainability
Dean Wampler → Scalable Machine Learning, Scalable Python, For Everyone
Dafna Shahaf → Computational humanness, analogy and innovation, and soft concepts
David Talby → Building domain specific natural language applications
Morten Dahl → The state of privacy-preserving machine learning
Sijie Guo → Taking messaging and data ingestion systems to the next level
Bahman Bahmani → Business at the speed of AI: Lessons from Rakuten
2019
Ben and Mikio Braun → Key AI and Data Trends for 2020
Rajat Monga → The evolution of TensorFlow and of machine learning infrastructure
Reza Zadeh → Building large-scale, real-time computer vision applications
Paco Nathan → Taking stock of foundational tools for analytics and machine learning