Archive

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

2020

50.   Ram ShankarSecuring machine learning applications

49.   Marco RibeiroTesting Natural Language Models

48.   Xiyin ZhouDetecting Fake News

47.   Neil ThompsonThe Computational Limits of Deep Learning

46.   Piero MolinoMaking deep learning accessible

45.   Mayank KejriwalBuilding and deploying knowledge graphs

44.   Murat ÖzbayoğluFinancial Time Series Forecasting with Deep Learning

43.   Viral ShahA programming language for scientific machine learning and differentiable programming

42.   Kira RadinskyUsing machine learning to modernize medical triage and monitoring systems

41.   Max PumperlaConnecting Reinforcement Learning to Simulation Software

40.   Weifeng ZhongUsing machine learning to detect shifts in government policy

39.   Ofer RazonWhat is AI Assurance?

38.   Alan NicholBest practices for building conversational AI applications

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

36.   Joel GrusFrom Python beginner to seasoned software engineer

35.   Bruno GonçalvesAssessing Models and Simulations of Epidemic Infectious Diseases

34.   Karthik Ramasamy and Arun KejariwalImproving the hiring pipeline for software engineers

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

32.   Ameet TalwalkarDemocratizing Machine Learning

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

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

29.   Christopher NguyenDesigning machine learning models for both consumer and industrial applications

28.   Matthew HonnibalBuilding open source developer tools for language applications

27.   Chris WigginsViewing machine learning and data science applications as sociotechnical systems

26.   Andrew BurtIdentifying 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 DoddiUnderstanding machine learning model governance

23.   Wes McKinneyImproving performance and scalability of data science libraries

22.   Pete WardenWhy TinyML will be huge

21.   Evan SparksAn open source platform for training deep learning models

20.   Kenneth StanleyAlgorithms that continually invent both problems and solutions

19.   Bruno GonçalvesComputational Models and Simulations of Epidemic Infectious Diseases

18.   Robert MunroHuman-in-the-loop machine learning

17.   Chris NicholsonNext-generation simulation software will incorporate deep reinforcement learning

16.   Solmaz ShahalizadehBusiness at the speed of AI: Lessons from Shopify

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

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

13.   Edmon BegoliHyperscaling natural language processing

12.   Krishna GadeWhat businesses need to know about model explainability

11.   Dean WamplerScalable Machine Learning, Scalable Python, For Everyone

10.  Dafna ShahafComputational humanness, analogy and innovation, and soft concepts

9.    David TalbyBuilding domain specific natural language applications

8.    Morten DahlThe state of privacy-preserving machine learning

7.     Sijie GuoTaking messaging and data ingestion systems to the next level

6.    Bahman BahmaniBusiness at the speed of AI: Lessons from Rakuten

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

2019

4.    Ben and Mikio BraunKey AI and Data Trends for 2020

3.    Rajat MongaThe evolution of TensorFlow and of machine learning infrastructure

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

1.    Paco NathanTaking stock of foundational tools for analytics and machine learning