All Episodes
Towards Data Science — 130 episodes
130. Edouard Harris - New Research: Advanced AI may tend to seek power *by default*
129. Amber Teng - Building apps with a new generation of language models
128. David Hirko - AI observability and data as a cybersecurity weakness
127. Matthew Stewart - The emerging world of ML sensors
126. JR King - Does the brain run on deep learning?
125. Ryan Fedasiuk - Can the U.S. and China collaborate on AI safety?
124. Alex Watson - Synthetic data could change everything
123. Ala Shaabana and Jacob Steeves - AI on the blockchain (it actually might just make sense)
122. Sadie St. Lawrence - Trends in data science
121. Alexei Baevski - data2vec and the future of multimodal learning
120. Liam Fedus and Barrett Zoph - AI scaling with mixture of expert models
119. Jaime Sevilla - Projecting AI progress from compute trends
118. Angela Fan - Generating Wikipedia articles with AI
117. Beena Ammanath - Defining trustworthy AI
116. Katya Sedova - AI-powered disinformation, present and future
115. Irina Rish - Out-of-distribution generalization
114. Sam Bowman - Are we *under-hyping* AI?
113. Yaron Singer - Catching edge cases in AI
112. Tali Raveh - AI, single cell genomics, and the new era of computational biology
111. Mo Gawdat - Scary Smart: A former Google exec’s perspective on AI risk
110. Alex Turner - Will powerful AIs tend to seek power?
109. Danijar Hafner - Gaming our way to AGI
108. Last Week In AI — 2021: The (full) year in review
107. Kevin Hu - Data observability and why it matters
106. Yang Gao - Sample-efficient AI
105. Yannic Kilcher - A 10,000-foot view of AI
104. Ken Stanley - AI without objectives
103. Gillian Hadfield - How to create explainable AI regulations that actually make sense
102. Wendy Foster - AI ethics as a user experience challenge
101. Ayanna Howard - AI and the trust problem
100. Max Jaderberg - Open-ended learning at DeepMind
99. Margaret Mitchell - (Practical) AI ethics
98. Mike Tung - Are knowledge graphs AI’s next big thing?
97. Anthony Habayeb - The present and future of AI regulation
96. Jan Leike - AI alignment at OpenAI
95. Francesca Rossi - Thinking, fast and slow: AI edition
94. Divya Siddarth - Are we thinking about AI wrong?
93. 2021: A year in AI (so far) - Reviewing the biggest AI stories of 2021 with our friends at the Let’s Talk AI podcast
92. Daniel Filan - Peering into neural nets for AI safety
91. Peter Gao - Self-driving cars: Past, present and future
90. Jeffrey Ding - China’s AI ambitions and why they matter
89. Pointing AI in the right direction - A cross-over episode with the Banana Data podcast!
88. Oren Etzioni - The case against (worrying about) existential risk from AI
87. Evan Hubinger - The Inner Alignment Problem
86. Andy Jones - AI Safety and the Scaling Hypothesis
85. Brian Christian - The Alignment Problem
84. Eliano Marques - The (evolving) world of AI privacy and data security
83. Rosie Campbell - Should all AI research be published?
82. Jakob Foerster - The high cost of automated weapons
81. Nicolas Miailhe - AI risk is a global problem
80. Yan Li - The Surprising Challenges of Global AI Philanthropy
79. Ryan Carey - What does your AI want?
78. Melanie Mitchell - Existential risk from AI: A skeptical perspective
77. Josh Fairfield - AI advances, but can the law keep up?
76. Stuart Armstrong - AI: Humanity's Endgame?
75. Georg Northoff - Consciousness and AI
74. Ethan Perez - Making AI safe through debate
73. David Roodman - Economic history and the road to the singularity
72. Margot Gerritsen - Does AI have to be understandable to be ethical?
71. Ben Garfinkel - Superhuman AI and the future of democracy and government
70. Sarah Williams - What does ethical AI even mean?
69. Anders Sandberg - Answering the Fermi Question: Is AI our Great Filter?
68. Silvia Milano - Ethical problems with recommender systems
67. Joaquin Quiñonero-Candela - Responsible AI at Facebook
66. Owain Evans - Predicting the future of AI
65. Helen Toner - The strategic and security implications of AI
64. David Krueger - Managing the incentives of AI
63. Geordie Rose - Will AGI need to be embodied?
62. Nicolai Baldin - AI meets the law: Bias, fairness, privacy and regulation
61. Ben Goertzel - The unorthodox path to AGI
60. Rob Miles - Why should I care about AI safety?
59. Matthew Stewart - Tiny ML and the future of on-device AI
58. David Duvenaud - Using generative models for explainable AI
57. Dylan Hadfield-Menell - Humans in the loop
56. Annette Zimmermann - The ethics of AI
55. Rohin Shah - Effective altruism, AI safety, and learning human preferences from the state of the world
54. Tim Rocktäschel - Deep reinforcement learning, symbolic learning and the road to AGI
53. Edouard Harris - Emerging problems in machine learning: making AI "good"
52. Sanyam Bhutani - Networking like a pro in data science
51. Adrien Treuille and Tim Conkling - Streamlit Is All You Need
50. Ken Jee - Building your brand in data science
49. Catherine Zhou - The data science of learning
48. Emmanuel Ameisen - Beyond the jupyter notebook: how to build data science products
47. Goku Mohandas - Industry research and how to show off your projects
46. Ihab Ilyas - Data cleaning is finally being automated
45. Kenny Ning - Is data science merging with data engineering?
44. Jakob Foerster - Multi-agent reinforcement learning and the future of AI
43. Ian Scott - Data science at Deloitte
42. Will Grathwohl - Energy-based models and the future of generative algorithms
41. Solmaz Shahalizadeh - Data science in high-growth companies
40. David Meza - Data science at NASA
39. Nick Pogrebnyakov - Data science at Reuters, and the remote work after the coronavirus
38. Matthew Stewart - Data privacy and machine learning in environmental science
37. Sean Knapp - The brave new world of data engineering
36. Max Welling - The future of machine learning
35. Rubén Harris - Learning and looking for jobs in quarantine
34. Denise Gosnell and Matthias Broecheler - You should really learn about graph databases. Here’s why.
33. Roland Memisevic - Machines that can see and hear
32. Bahador Khaleghi - Explainable AI and AI interpretability
31. Russell Pollari - Building habits and breaking into data science
30. Interviewing the Medium data science team
29. Cameron Davidson-Pillon - Data science at Shopify
28. Emily Robinson - Building a Career in Data Science
27. Alayna Kennedy - AI safety, AI ethics and the AGI debate
26. Jeremy Howard - Coronavirus: the data behind the disease
25. Chris Parmer - Plotly founder on what data science is, and where it's going
24. Xander Steenbrugge - Machine learning as a creative tool, and the quest for artificial general intelligence
23. Iain Harlow - Leaving academia for industry and optimizing how you learn
22. Luke Marsden - Data Science Infrastructure and MLOps
21. Adam Waksman - Data science is becoming software engineering
20. Chanchal Chatterjee - Real Talk with AI Leader at Google
19. Will Koehrsen - Self-Learning Data Science and Sharing the Knowledge on Medium
18. Edouard Harris - Mastering the data science job hunt
17. Nate Nichols - Product instinct and data storytelling
16. Helen Ngo - Real Talk with Machine Learning Engineer
15. Ian Xiao - Why Machine Learning Is More Boring Than You May Think
14. Jeremie Harris - Building a Data Science Startup & Getting Into Data Science
13. Jessica Li - Predicting Snowmelt Patterns with Deep Learning and Satellite Imagery
12. Rachael Tatman - Data science at Kaggle
11. Sanjeev Sharma - DataOps and data science at enterprise scale
10. Sanyam Bhutani - Data science beyond the classroom
9. Ben Lorica - Trends in data science with O'Reilly Media's Chief Data Scientist
8. George Hayward: comedian, lawyer and data scientist
7. Serkan Piantino - From Facebook to startups: data science is becoming an engineering problem
6. Jay Feng - Data science in the startup world
5. Rocio Ng - Data science and product management at LinkedIn
4. Akshay Singh - The thin line between data science and data engineering
Susan Holcomb - Nontechnical career skills for data scientists
Tan Vachiramon - Choosing the right algorithm for your real-world problem
Joel Grus - The case against the jupyter notebook