Past episodes listed in reverse chronological order. We have high-quality transcripts for a few episodes (see here).
2021
104. Che Sharma → Modern Experimentation Platforms
103. Nic Hohn and Max Pumperla → Reinforcement Learning in Real-World Applications
102. Nikhil Muralidhar → MLOps Anti-Patterns
101. Pardhu Gunnam and Mars Lan → Why You Need a Modern Metadata Platform
100. Yoav Shoham → Making Large Language Models Smarter
99. Jeremy Stanley → AI Begins With Data Quality
98. Michel Tricot → Modernizing Data Integration
97. Hamel Husain → Deploying Machine Learning Models Safely and Systematically
96. Bob Friday → Large-scale machine learning and AI on multi-modal data
95. Viviana Acquaviva → Machine Learning in Astronomy and Physics
94. Viral Shah → The Unreasonable Effectiveness of Multiple Dispatch
93. Jike Chong and Yue Cathy Chang in conversation with Jenn Webb and Ben Lorica → How To Lead In Data Science
92. Paco Nathan in conversation with Jenn Webb and Ben Lorica → Why interest in graph databases and graph analytics are growing
91. Tara Kelly in conversation with Jenn Webb and Ben Lorica → The State of Data Journalism
90. Rayid Ghani and Andrew Burt → Auditing machine learning models for discrimination, bias, and other risks
89. Charles Martin → An oscilloscope for deep learning
88. Jesse Anderson in conversation with Jenn Webb and Ben Lorica → What’s new in data engineering
87. Sean Taylor in conversation with Jenn Webb and Ben Lorica → Changes to the data science role and to data science tools
86. Steven Feng and Eduard Hovy → Data Augmentation in Natural Language Processing
85. Brad King → Storage Technologies for a Multi-cloud World
84. Chris White in conversation with Jenn Webb and Ben Lorica → Towards a next-generation dataflow orchestration and automation system
83. Reza Hosseini and Albert Chen → Building a flexible, intuitive, and fast forecasting library
82. Sercan Arik → Neural Models for Tabular Data
81. Connor Leahy → Training and Sharing Large Language Models
80. Paolo Cremonesi and Maurizio Ferrari Dacrema → Questioning the Efficacy of Neural Recommendation Systems
79. Hyun Kim → Automation in Data Management and Data Labeling
78. Nicolas Hohn → Reinforcement Learning For the Win
77. Andrew Burt → How Companies Are Investing in AI Risk and Liability Minimization
76. Travis Addair → The Future of Machine Learning Lies in Better Abstractions
75. Yonatan Geifman and Ran El-Yaniv → Why You Should Optimize Your Deep Learning Inference Platform
74. Jerry Overton in conversation with Jenn Webb and Ben Lorica → AI Beyond Automation
73. Steve Touw → Injecting Software Engineering Practices and Rigor into Data Governance
72. Davit Buniatyan → Building a data store for unstructured data and deep learning applications
71. Zhe Zhang → How Technology Companies Are Using Ray
70. Abe Gong → Data quality is key to great AI products and services
69. Parisa Rashidi → Machine Learning in Healthcare
68. Simon Rodriguez in conversation with Jenn Webb and Ben Lorica → Measuring the Impact of AI and Machine Learning Research
67. Ryan Wisnesky → The Mathematics of Data Integration and Data Quality
66. Jian Pei → Pricing Data Products
65. Sharon Zhou in conversation with Jenn Webb and Ben Lorica → Challenges, Opportunities, and Trends in EdTech
64. Alex Wong and Sheldon Fernandez → Towards Simple, Interpretable, and Trustworthy AI
63. Assaf Araki and Ben Lorica in conversation with Jenn Webb → The Rise of Metadata Management Systems
62. Michael Mahoney → Tools for building robust, state-of-the-art machine learning models
61. Sonal Goyal and Ben Lorica in conversation with Jenn Webb → Creating Master Data at Scale with AI
60. Bruno Fernandez-Ruiz → Bringing AI and computing closer to data sources
59. Bharath Ramsundar → Deep Learning in the Sciences
58. Ira Cohen → Taking business intelligence and analyst tools to the next level
57. Omer Dror → Data exchanges and their applications in healthcare and the life sciences
2020
56. Ben Lorica and Mikio Braun in conversation with Jenn Webb → Key AI and Data Trends for 2021
55. Jesse Anderson and Ben Lorica in conversation with Jenn Webb → A Unified Management Model for Successful Data-Focused Teams
54. Dan Geer and Andrew Burt → Security and privacy for the disoriented
53. Rumman Chowdury → The State of Responsible AI
52. Jack Morris → Improving the robustness of natural language applications
51. Yishay Carmiel → End-to-end deep learning models for speech applications
50. Ram Shankar → Securing machine learning applications
49. Marco Ribeiro → Testing Natural Language Models
48. Xiyin Zhou → Detecting Fake News
47. Neil Thompson → The Computational Limits of Deep Learning
46. Piero Molino → Making deep learning accessible
45. Mayank Kejriwal → Building and deploying knowledge graphs
44. Murat Özbayoğlu → Financial Time Series Forecasting with Deep Learning
43. Viral Shah → A programming language for scientific machine learning and differentiable programming
42. Kira Radinsky → Using machine learning to modernize medical triage and monitoring systems
41. Max Pumperla → Connecting Reinforcement Learning to Simulation Software
40. Weifeng Zhong → Using machine learning to detect shifts in government policy
39. Ofer Razon → What is AI Assurance?
38. Alan Nichol → Best practices for building conversational AI applications
37. Paco Nathan and Ben Lorica in conversation with Jenn Webb → Tools for scaling machine learning
36. Joel Grus → From Python beginner to seasoned software engineer
35. Bruno Gonçalves → Assessing Models and Simulations of Epidemic Infectious Diseases
34. Karthik Ramasamy and Arun Kejariwal → Improving the hiring pipeline for software engineers
33. Lauren Kunze → How to build state-of-the-art chatbots
32. Ameet Talwalkar → Democratizing Machine Learning
31. Denise Gosnell → How graph technologies are being used to solve complex business problems
30. Amy Heineike → Machines for unlocking the deluge of COVID-19 papers, articles, and conversations
29. Christopher Nguyen → Designing machine learning models for both consumer and industrial applications
28. Matthew Honnibal → Building open source developer tools for language applications
27. Chris Wiggins → Viewing machine learning and data science applications as sociotechnical systems
26. Andrew Burt → Identifying and mitigating liabilities and risks associated with AI
25. Arun Verma (in conversation with Jenn Webb) → How machine learning is being used in quantitative finance
24. Harish Doddi → Understanding machine learning model governance
23. Wes McKinney → Improving performance and scalability of data science libraries
22. Pete Warden → Why TinyML will be huge
21. Evan Sparks → An open source platform for training deep learning models
20. Kenneth Stanley → Algorithms that continually invent both problems and solutions
19. Bruno Gonçalves → Computational Models and Simulations of Epidemic Infectious Diseases
18. Robert Munro → Human-in-the-loop machine learning
17. Chris Nicholson → Next-generation simulation software will incorporate deep reinforcement learning
16. Solmaz Shahalizadeh → Business at the speed of AI: Lessons from Shopify
15. Edo Liberty → How deep learning is being used in search and information retrieval
14. Alejandro Saucedo → The responsible development, deployment and operation of machine learning systems
13. Edmon Begoli → Hyperscaling natural language processing
12. Krishna Gade → What businesses need to know about model explainability
11. Dean Wampler → Scalable Machine Learning, Scalable Python, For Everyone
10. Dafna Shahaf → Computational humanness, analogy and innovation, and soft concepts
9. David Talby → Building domain specific natural language applications
8. Morten Dahl → The state of privacy-preserving machine learning
7. Sijie Guo → Taking messaging and data ingestion systems to the next level
6. Bahman Bahmani → Business at the speed of AI: Lessons from Rakuten
5. Nir Shavit → The combination of the right software and commodity hardware will prove capable of handling most machine learning tasks
2019
4. Ben and Mikio Braun → Key AI and Data Trends for 2020
3. Rajat Monga → The evolution of TensorFlow and of machine learning infrastructure
2. Reza Zadeh → Building large-scale, real-time computer vision applications
1. Paco Nathan → Taking stock of foundational tools for analytics and machine learning