Past episodes listed in reverse chronological order. We have high-quality transcripts for a few episodes (see here).
2026
370. Joel Hron → Why Build Your Own AI Model When Frontier Models Already Exist?
369. Piero Molino → Why AI Makes Games Worse—and How to Make Them Better
368. Zuzanna Stamirowska → What Comes After Transformers?
367. Richard Ho → Inside OpenAI’s First Chip
366. Ben Lorica and Evangelos Simoudis → Navier-Stokes and AI generated work: If You Can’t Explain It, Did You Really Do It?
365. David Fattal → The Missing Data for Spatial Intelligence
364. Abi Aryan → Your AI Agent Is Costing You More Than You Think
363. Andrew Dai → Reasoning Doesn’t Start With Language
362. Maarten Grootendorst → An Agent Is Just an LLM in a For-Loop
361. Steve Hou → The Bloomberg Terminal for AI Compute
360. Ben Lorica and Evangelos Simoudis → Can China Do to Robotaxis What It Did to Solar?
359. Yoon Kim → Why Video Is AI’s Next Great Frontier
358. Andrew Burt → Your AI Safety Tests Are Lying to You
357. Denise Teng → Enterprise AI Is Moving Slower Than You Think
356. Ameet Talwalkar → Why Observability May Be AI’s Next Frontier
355. Ben Lorica and Evangelos Simoudis → AI Is Producing More Code, but Is It Producing More Value?
354. Manos Koukoumidis → Stop Renting Generic Intelligence for Your Business
353. Andrew Moore → The Data Layer Enterprise AI Has Been Missing
352. Helen Gu → The Hidden Failure Modes of AI Agents
351. Chang She → The Data Stack Wasn’t Built for AI — Here’s What Comes Next
350. Ben Lorica and Evangelos Simoudis → The SaaSpocalypse Is Coming — But Don’t Count Out the Incumbents
349. Hamza Tahir → AI Agents Are Implemented, Not Adopted
348. Zhou Yu → Why Your AI Agent Isn’t Ready to Ship (And How to Know When It Is)
347. Doris Xin and Moustafa Abdelbaky → Why Foundation Models Haven’t Replaced Classical Machine Learning
346. Terrence Lee-St. John → When “Garbage In, Garbage Out” Gets It Wrong
345. Evan Marshall → As Code Generation Speeds Up, Who Tests the Output?
344. Richard Garris and Barry Dauber → The Gap Between AI Hype and Enterprise Reality
343. Nick Vasiloglou → Reading the Tea Leaves: What the World’s Top AI Researchers Are Really Working On
342. Changan Chen → From Web Video to Real-World Robots
341. Ben Lorica and Evangelos Simoudis → Why Your AI Committee Might Be Your Biggest AI Problem
340. Tudor Achim → Building Mathematical Superintelligence
339. Kay Zhu → Your First AI Employee Is Already Clocking In
338. Arun Kumar (of UCSD and RapiFire AI) → Are Multi-Agent Systems More Complex Than They Need to Be?
337. Mikio Braun → Coding Agents Meet Data Science
336. Jeff Hawke → World Models Are Here—But It’s Still the GPT-2 Phase
335. Chen Goldberg → The Hidden Challenges of Running AI at Scale in Production
334. Ben Lorica and Evangelos Simoudis → What No One Tells You About Staying Employable in the AI Era
333. Sudip Roy → Adaptation: The Missing Layer Between Apps and Foundation Models
332. Jason Martin (of HiddenLayer) → Securing the “YOLO” Era of AI Agents
331. Umur Cubukcu → Building the Open Source Alternative to AWS
330. Sid Sheth → Breaking the Memory Wall in the Age of Inference
329. Ben Lorica and Evangelos Simoudis → Is Waymo Actually Profitable? The Real Cost of the Robotaxi Revolution
328. Ethan Ouyang → Beyond Vibe Coding: Building Your Entire Business with AI
327. Jason Martin (of Permiso) → The Rise of the Machine Identity: Securing the AI Workforce and AI Agents
326. Lior Gavish → Why Traditional Observability Falls Short for AI Agents
325. Ben Luria → Teaching AI How to Forget
324. Ben Lorica and Evangelos Simoudis → The Humanoid Hype Cycle: Separating “Shiny Objects” from Real Utility
323. Matthew Glickman → The Junior Data Engineer is Now an AI Agent
2025
322. Samuel Colvin (Pydantic), Aparna Dhinakaran (Arize AI), Adam Jones (Anthropic), and Jerry Liu (LlamaIndex) → The Truth About Agents in Production
321. Ben Lorica → The best books we read this year 📚
320. Steve Wilson → The Developer’s Guide to LLM Security
319. Ben Lorica and Evangelos Simoudis → Is AI a Utility? Defining Usability and Public Trust
318. Stefania Druga → How to Build AI Copilots That Teach Rather Than Automate
317. Jure Leskovec → The AI Revolution Finally Comes to Structured Data
316. Philip Rathle → Building the Knowledge Layer Your Agents Need
315. Emmanuel Ameisen → How Language Models Actually Think
314. Ben Lorica and Evangelos Simoudis → How AI Is Reshaping Jobs, Budgets, and Data Centers
313. Ciro Greco → Making Data Engineering Safe for Automation and Agents
312. Mike Freedman and Ajay Kulkarni → Is Your Database Ready for an Army of AI Agents?
311. Nick Schrock → Beyond the Dashboard: Collaborative Analytics in Slack
310. Ben Lorica and Evangelos Simoudis → Stop Piloting, Start Shipping: A Playbook for Measurable AI
309. Luke Wroblewski → Databases for Machines, Not People
308. Heiko Hotz and Sokratis Kartakis → When AI Agents Need to Talk: Inside the A2A Protocol
307. Zhen Lu → The Infrastructure for Production AI
306. Yoni Leitersdorf → How to Make Your Data Truly AI-Ready
305. Ben Lorica and Evangelos Simoudis → Beyond the Agent Hype
304. Jakub Zavrel → How to Build and Optimize AI Research Agents
303. Andrew Rabinovich → Why Digital Work is the Perfect Training Ground for AI Agents
302. Jay Alammar → Beyond the Chatbot: What Actually Works in Enterprise AI
301. Ben Lorica and Evangelos Simoudis → Why China’s Engineering Culture Gives Them an AI Advantage
300. Anant Bhardwaj → Predictability Beats Accuracy in Enterprise AI
299. Ben Lorica and David Talby → 2025 AI Governance Survey
298. Kostas Paralis → The Fenic Approach to Production-Ready Data Processing
297. Ben Lorica and Evangelos Simoudis → When AI Eats the Bottom Rung of the Career Ladder
296. Raiza Martin → From NotebookLM to Audio Companions: Why Google’s AI Team Went Startup
295. Akshay Agrawal → The AI-Native Notebook That Thinks Like a Spreadsheet
294. Josh Pantony → How Agentic AI is Transforming Wall Street
293. Jennifer Prendki → The Quantum Advantage Is Real—But Where’s the Infrastructure?
292. Sagar Batchu → From Human-Readable to Machine-Usable: The New API Stack
291. Yishay Carmiel and Roy Zanbel → Why Voice Security Is Your Next Big Problem
290. Shreya Shankar → Unlocking Unstructured Data with LLMs
289. Douwe Kiela → Building Production-Grade RAG at Scale
288. Zach Lloyd → Unlocking AI Superpowers in Your Terminal
287. Jackie Brosamer and Brad Axen → From Vibe Coding to Autonomous Agents
286. Manos Koukoumidis → How a Public-Benefit Startup Plans to Make Open Source the Default for Serious AI
285. Dan Schwarz → The Highly Uncertain Future of OpenAI’s Dominance
284. Jason Martin (of HiddenLayer) → Beyond Guardrails: Defending LLMs Against Sophisticated Attacks
283. Evangelos Simoudis → Navigating the Generative AI Maze in Business
282. Lin Qiao → The Practical Realities of AI Development
281. Hamel Husain → Beyond the Demo: Building AI Systems That Actually Work
280. Steve Yegge → Vibe Coding and the Rise of AI Agents: The Future of Software Development is Here
279. Nestor Maslej → 2025 Artificial Intelligence Index
278. Kian Katanforoosh → How AI is Transforming Talent Development
277. David Hughes → Prompts as Functions: The BAML Revolution in AI Engineering
276. Chi Wang → Building the Operating System for AI Agents
275. Ilan Kadar → Bridging the AI Agent Prototype-to-Production Chasm
274. Travis Addair → The Evolution of Reinforcement Fine-Tuning in AI
273. Hagay Lupesko → Beyond GPUs: Cerebras’ Wafer-Scale Engine for Lightning-Fast AI Inference
272. Ben Lorica and Paco Nathan → Monthly Roundup: Regulation, Foundation Models & User Experience
271. The AI Agent Rundown: 10 Things to Know Now
270. Tom Smoker → Why ‘Structure’ Is All You Need: A Deep Dive into Next-Gen AI Retrieval
269. Andrew Burt → Why Legal Hurdles Are the Biggest Barrier to AI Adoption
268. Hjalmar Gislason → Unlocking Spreadsheet Intelligence with AI
267. Ben Lorica and Paco Nathan → Monthly Roundup: Deregulation, Hardware, and Inference Scaling
266. What AI Teams Need to Know for 2025
265. AI Unlocked: The Data Bottleneck
264. Robert Nishihara → The Data-Centric Shift in AI: Challenges, Opportunities, and Tools
2024
263. Qian Li and Peter Kraft → Breaking the Cloud Barrier: How DBOS Transforms Application Development
262. Shreya Rajpal → The Essential Guide to AI Guardrails
261. Deepti Srivastava → Beyond ETL: How Snow Leopard Connects AI, Agents, and Live Data
260. Ben Lorica and David Talby → 2024 Generative AI in Healthcare Survey Results
259. Ben Lorica and Paco Nathan → Monthly Roundup: BAML, Tencent’s Hunyuan Model, AI & Kubernetes, and the Future of Voice AI
258. Vasant Dhar → Building the Future of Finance: Inside AI Valuation Bots
257. Vaibhav Gupta → Unleashing the Power of BAML in LLM Applications
256. Tim Persons → Cracking the Code: How Enterprises Are Adopting Generative AI
255. Ben Lorica and Paco Nathan → Monthly Roundup: Ray Compiled Graphs, Llama 3.2 and Multimodal AI, and Structured Data for RAG
254. Matt Welsh → Reimagining Code: The AI-Driven Transformation of Programming and Data Analytics
253. Mars Lan → The Security Debate: How Safe is Open-Source Software?
252. Yishay Carmiel → Generative AI in Voice Technology
251. Aurimas Griciūnas → Building An Experiment Tracker for Foundation Model Training
250. Ben Lorica and Paco Nathan → Monthly Roundup: AI Regulations, GenAI for Analysts, Inference Services, and Military Applications
249. Petros Zerfos and Hima Patel → Unlocking the Power of LLMs with Data Prep Kit
248. Andrew Ng → Advancing AI: Scaling, Data, Agents, Testing, and Ethical Considerations
247. Jay Dawani → Bridging the Hardware-Software Divide in AI
246. Ben Lorica and Paco Nathan → Monthly Roundup: The Economic Realities of Large Language Models
245. Evangelos Simoudis → From Hype to Reality: The Current State of Enterprise Generative AI Adoption
244. Shuveb Hussain → Automating Unstructured Data Extraction with LLMs
243. Alfred Spector → Generative AI in Context: Hybrid Intelligence and Responsible Development
242. Ben Lorica and Paco Nathan → Monthly Roundup: Navigating the Peaks and Valleys of Generative AI Technology
241. Andrew Burt → From Preparation to Recovery: Mastering AI Incident Response
240. Chang She → Unlocking the Power of Unstructured Data
239. Ajay Kulkarni and Mike Freedman → Postgres: The Swiss Army Knife of Databases
238. Philip Rathle → Supercharging AI with Graphs
237. Ben Lorica and Paco Nathan → Monthly Roundup: SB 1047, GraphRAG, and AI Avatars in the Workplace
236. Jiwoo Hong and Noah Lee → Integrating Fine-tuning and Preference Alignment in a Single Streamlined Process
235. Pete Warden → TinyML, Sensor-Driven AI, and Advances in Large Language Models
234. Ken Liu → Machine Unlearning: Techniques, Challenges, and Future Directions
233. Joao Moura → Unleashing the Power of AI Agents
232. Ben Lorica and Paco Nathan → Monthly Roundup: Llama 3, Agents, Evaluation Metrics, Cyc, TikTok, and more
231. Gunther Hagleither → LLMs for Data Access – Unlocking Insights with Text-to-SQL
230. Nestor Maslej → 2024 Artificial Intelligence Index
229. Hagay Lupesko → DBRX and the Future of Open LLMs
228. Ben Lorica and Paco Nathan → Monthly Roundup – New LLMs, GTC 2024, Constraint-Driven Innovation, Model Safety, and GraphRAG
227. Steve Pike → Automating Software Upgrades – How to Combine AI and Expert Developers
226. Chetan Gupta → Generative AI in the Industrial Sphere
225. Semih Salihoglu → The Intersection of LLMs, Knowledge Graphs, and Query Generation
224. Sadegh Riazi → Unlocking the Potential of Private Data Collaboration
223. Ben Lorica and Paco Nathan → Frontiers of AI – From Text-to-Video Models to Knowledge Graphs
222. Jerry Kaplan → Where AI Systems Are Heading Next
221. 2024 Themes and Trends in AI
220. Bryan Cantrill → The AI Infrastructure Revolution: From Cloud Computing to Data Center Design
219. Evangelos Simoudis → AI in Depth: Transforming Transportation, Enterprise, and Policy
218. Sharon Zhou and Greg Diamos → Software Meets Hardware – Enabling AMD for Large Language Models
217. Uri Gneezy → Incentives are Superpowers – Mastering Motivation in the AI Era
216. Dmitriy Ryaboy → The Convergence of Biology and AI
215. Jian Zhang → AI Co-Pilots in Action – Transforming Function Calling in Cybersecurity
214. Sarmad Qadri → Tools and Techniques to Make AI Development More Accessible
213. Nir Shavit → LLMs on CPUs, Period
2023
212. Chirag Yagnik → Democratizing Wealth Management With AI
211. Juan Sequeda and Dean Allemang → Knowledge Graphs: Contextualizing Enterprise Data for More Accurate LLMs
210. Max Mergenthaler and Azul Garza Ramirez → TimeGPT: Machine Learning for Time Series, Made Accessible
209. Waleed Kadous → Best Practices for Building LLM-Backed Applications
208. Kieren James-Lubin → The Evolution of Crypto, Blockchain, and Web3
207. Ben Lorica on the Open||Source||Data podcast → Open Source Data and AI: Past, Present, Future
206. Malte Pietsch → Orchestration for LLM and RAG applications
205. Paco Nathan and Ben Lorica → Reflections from the First AI Conference in San Francisco
204. Semih Salihoglu → Kùzu – A simple, extremely fast, and embeddable graph database
203. Philipp Moritz and Goku Mohandas → Navigating the Nuances of Retrieval Augmented Generation
202. Bill Marcellino and Nathan Beauchamp-Mustafaga → The Rise of Generative AI-Powered Social Media Manipulation
201. Yucheng Low → Versioning and MLOps for Generative AI
200. Christopher Nguyen → Navigating the Generative AI Landscape
199. Sudhir Hasbe → Trends in Data Management: From Source to BI and Generative AI
198. Yishay Carmiel → AI and the Future of Speech Technologies
197. Casey Ellis → The Future of Cybersecurity – Generative AI and its Implications
196. Daniel Lenton → Ivy – The One-Stop Interface for AI Model Deployment and Development
195. Andrew Burt → Navigating the Risk Landscape – A Deep Dive into Generative AI
194. Michele Catasta → Software Development with AI and LLMs
193. Alex Chao → A Lightweight SDK for Integrating AI Models and Plugins
192. Steve Hsu → Using LLMs to Build AI Co-pilots for Knowledge Workers
191. Brian Raymond → ETL for LLMs
190. Emil Eifrem → The Future of Graph Databases
189. David Talby → Delivering Safe and Effective LLM and NLP Applications with LangTest
188. Jeff Jonas → Using Data and AI to Democratize Entity Resolution and Master Data Management
187. Jerry Liu → An Open Source Data Framework for LLMs
186. Tim Davis → Redefining AI Infrastructure
185. Andrew Feldman → The Rise of Custom Foundation Models
184. Louis Brandy → The Future of Vector Databases and the Rise of Instant Updates
183. Amin Ahmad → LLMs Are the Key to Unlocking the Next Generation of Search
182. Jonas Andrulis → Building and Deploying Foundation Models for Enterprises
181. Alex Remedios → Building Robust AI Infrastructure for Critical Solutions
180. Patrick Hall and Agus Sudjianto → Machine Learning for High-Risk Applications
179. Omar Maher → Boosting Perception With Synthetic Data
178. Simon Chan → Revolutionizing B2B: Unleashing the Power of AI and Data
177. Gev Sogomonian → AI Metadata
176. Raymond Perrault → 2023 AI Index
175. Hagay Lupesko → Custom Foundation Models
174. Jakub Zavrel → Uncovering and Highlighting AI Trends
173. Chris Wiggins → How Data and AI Happened
172. Paras Jain and Sarah Wooders → Blazing fast bulk data transfers between any cloud
171. Pablo Villalobos → Exhaustion of High-Quality Data Could Slow Down AI Progress in Coming Decades
170. Jinsung Yoon and Sercan Arik → Generating high-fidelity and privacy-preserving synthetic data
169. Brandon Jenkins → How technology is disrupting the venture capital industry
168. Zongheng Yang → 2023 Running Machine Learning Workloads On Any Cloud
167. Jesse Anderson, Evan Chan, and Ben Lorica → 2023 Trends in Data Engineering and Infrastructure
166. Gabriela Zanfir-Fortuna and Andrew Burt → Preparing for the Implementation of the EU AI Act and Other AI Regulations
165. Dylan Patel → The Open Source Stack Unleashing a Game-Changing AI Hardware Shift
164. Peter Norvig and Alfred Spector → Data Science and AI in Context
163. Percy Liang → Evaluating Language Models
162. Ben Lorica, Mikio Braun, and Jenn Webb → 2023 Opportunities and Trends – Data, Machine Learning, and AI
161. Mark Chen → Exploring DALL·E 2
2022
160. Wendy Foster and Olivia Liao → Data Science at Shopify and Stitch Fix
159. Shayan Mohanty → Building a data management system for unstructured data
158. Frank Liu → A Cloud Native Vector Database Management System
157. Ira Cohen → What’s Next for Machine Learning in Time Series
156. Roy Schwartz → Efficient Methods for Natural Language Processing
155. Andrew Burt and Bob Friday → Responsible and Trustworthy AI (Thanksgiving holiday episode)
154. Hung Bui → Building a premier industrial AI research and product group
153. Bob van Luijt → An open source, production grade vector search engine
152. Federico Garza and Max Mergenthaler Canseco → A comprehensive suite of open source tools for time series modeling
151. Christopher Nguyen → Building Safe and Reliable AI applications
150. Ram Sriharsha → A new storage engine for vectors
149. Karthik Ramasamy → Project Lightspeed: Next-generation Spark Streaming
148. Piotr Żelasko → The Unreasonable Effectiveness of Speech Data
147. Yaron Singer → Machine Learning Integrity
146. Yashar Behzadi → Synthetic data technologies can enable more capable and ethical AI
145. Sadegh Riazi → Confidential Computing for Machine Learning
144. John Bohannon → Applied NLP Research at Primer
143. Jon Udell → Using SQL to Retrieve Data from APIs and Web Services
142. Aadyot Bhatnagar → Machine Learning for Time Series Intelligence
141. Maarten Grootendorst → Unleashing the power of large language models
140. Hamza Tahir and Adam Probst → Building production-ready machine learning pipelines
139. Omri Allouche → Machine Learning at Gong
138. Danny Bickson and Amir Alush → Data Infrastructure for Computer Vision
137. Mark Chen → How DALL·E works
136. Jules Damji and Richard Liaw → Scalable, end-to-end machine learning, for everyone
135. Rick Lamers → Orchestration and Pipelines for Data Scientists
134. Devin Petersohn → Dataframes at scale
133. Nick Schrock → Software-defined Assets
132. Edmon Begoli → Adversarial Machine Learning
131. Haytham Abuelfutuh → Orchestrating Machine Learning Applications
130. Hilary Mason → Narrative AI
129. Oren Razon → Machine Learning Model Observability
128. Jeremiah Lowin → Dataflow Automation
127. Sebastian Raschka → Practical Machine Learning and Deep learning
126. Ade Fajemisin and Donato Maragno → Machine Learning for Optimization
125. Barret Zoph and Liam Fedus → Efficient Scaling of Language Models
124. Olivia Liao → Data Science at Stitch Fix
123. Jack Clark → The 2022 AI Index
122. Ajay Kulkarni and Mike Freedman → Why You Need A Time-Series Database
121. Wendy Foster → Data Science at Shopify
120. Elham Tabassi and Andrew Burt → An AI Risk Management Framework
119. Amit Sharma and Emre Kiciman → An open source and end-to-end library for causal inference
118. Leo Meyerovich → The Graph Intelligence Stack
117. Dia Trambitas-Miron and David Talby → NLP and Language Models in Healthcare and the Life Sciences
116. Simon Crosby → Delivering Continuous Intelligence at Scale
115. Nicholas Boucher → Imperceptible NLP Attacks
114. Anjali Samani → Evolving Data Science Training Programs
113. Savin Goyal → Building Machine Learning Infrastructure at Netflix and beyond
112. Moshe Wasserblat → Democratizing NLP
111. Gaurav Chakravorty → Machine Learning at Discord
110. Mike Tung → Applications of Knowledge Graphs
109. Ben Lorica and Mikio Braun in conversation with Jenn Webb → Key AI and Data Trends for 2022
2021
108. Connor Leahy and Yoav Shoham → Large Language Models
107. Azeem Ahmed → Data and Machine Learning Platforms at Shopify
106. Christopher Nguyen → What is AI Engineering?
105. Anshul Pandey → NLP and AI in Financial Services
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