Archive

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

2025

322. Samuel Colvin (Pydantic), Aparna Dhinakaran (Arize AI), Adam Jones (Anthropic), and Jerry Liu (LlamaIndex) β†’ The Truth About Agents in Production

321. Ben Lorica β†’ The best books we read this year πŸ“š

320. Steve Wilson β†’ The Developer’s Guide to LLM Security

319. Ben Lorica and Evangelos Simoudis β†’ Is AI a Utility? Defining Usability and Public Trust

318. Stefania Druga β†’ How to Build AI Copilots That Teach Rather Than Automate

317. Jure Leskovec β†’ The AI Revolution Finally Comes to Structured Data

316. Philip Rathle β†’ Building the Knowledge Layer Your Agents Need

315. Emmanuel Ameisen β†’ How Language Models Actually Think

314. Ben Lorica and Evangelos Simoudis β†’ How AI Is Reshaping Jobs, Budgets, and Data Centers

313. Ciro Greco β†’ Making Data Engineering Safe for Automation and Agents

312. Mike Freedman and Ajay Kulkarni β†’ Is Your Database Ready for an Army of AI Agents?

311. Nick Schrock β†’ Beyond the Dashboard: Collaborative Analytics in Slack

310. Ben Lorica and Evangelos Simoudis β†’ Stop Piloting, Start Shipping: A Playbook for Measurable AI

309. Luke Wroblewski β†’ Databases for Machines, Not People

308. Heiko Hotz and Sokratis Kartakis β†’ When AI Agents Need to Talk: Inside the A2A Protocol

307. Zhen Lu β†’ The Infrastructure for Production AI

306. Yoni Leitersdorf β†’ How to Make Your Data Truly AI-Ready

305. Ben Lorica and Evangelos Simoudis β†’ Beyond the Agent Hype

304. Jakub Zavrel β†’ How to Build and Optimize AI Research Agents

303. Andrew Rabinovich β†’ Why Digital Work is the Perfect Training Ground for AI Agents

302. Jay Alammar β†’ Beyond the Chatbot: What Actually Works in Enterprise AI

301. Ben Lorica and Evangelos Simoudis β†’ Why China’s Engineering Culture Gives Them an AI Advantage

300. Anant Bhardwaj β†’ Predictability Beats Accuracy in Enterprise AI

299. Ben Lorica and David Talby β†’ 2025 AI Governance Survey

298. Kostas Paralis β†’ The Fenic Approach to Production-Ready Data Processing

297. Ben Lorica and Evangelos Simoudis β†’ When AI Eats the Bottom Rung of the Career Ladder

296. Raiza Martin β†’ From NotebookLM to Audio Companions: Why Google’s AI Team Went Startup

295. Akshay Agrawal β†’ The AI-Native Notebook That Thinks Like a Spreadsheet

294. Josh Pantony β†’ How Agentic AI is Transforming Wall Street

293. Jennifer Prendki β†’ The Quantum Advantage Is Realβ€”But Where’s the Infrastructure?

292. Sagar Batchu β†’ From Human-Readable to Machine-Usable: The New API Stack

291. Yishay Carmiel and Roy Zanbel β†’ Why Voice Security Is Your Next Big Problem

290. Shreya Shankar β†’ Unlocking Unstructured Data with LLMs

289. Douwe Kiela β†’ Building Production-Grade RAG at Scale

288. Zach Lloyd β†’ Unlocking AI Superpowers in Your Terminal

287. Jackie Brosamer and Brad Axen β†’ From Vibe Coding to Autonomous Agents

286. Manos Koukoumidis β†’ How a Public-Benefit Startup Plans to Make Open Source the Default for Serious AI

285. Dan Schwarz β†’ The Highly Uncertain Future of OpenAI’s Dominance

284. Jason Martin β†’ Beyond Guardrails: Defending LLMs Against Sophisticated Attacks

283. Evangelos Simoudis β†’ Navigating the Generative AI Maze in Business

282. Lin Qiao β†’ The Practical Realities of AI Development

281. Hamel Husain β†’ Beyond the Demo: Building AI Systems That Actually Work

280. Steve Yegge β†’ Vibe Coding and the Rise of AI Agents: The Future of Software Development is Here

279. Nestor Maslej β†’ 2025 Artificial Intelligence Index

278. Kian Katanforoosh β†’ How AI is Transforming Talent Development

277. David Hughes β†’ Prompts as Functions: The BAML Revolution in AI Engineering

276. Chi Wang β†’ Building the Operating System for AI Agents

275. Ilan Kadar β†’ Bridging the AI Agent Prototype-to-Production Chasm

274. Travis Addair β†’ The Evolution of Reinforcement Fine-Tuning in AI

273. Hagay Lupesko β†’ Beyond GPUs: Cerebras’ Wafer-Scale Engine for Lightning-Fast AI Inference

272. Ben Lorica and Paco Nathan β†’ Monthly Roundup: Regulation, Foundation Models & User Experience

271. The AI Agent Rundown: 10 Things to Know Now

270. Tom Smoker β†’ Why β€˜Structure’ Is All You Need: A Deep Dive into Next-Gen AI Retrieval

269. Andrew Burt β†’ Why Legal Hurdles Are the Biggest Barrier to AI Adoption

268. Hjalmar Gislason β†’ Unlocking Spreadsheet Intelligence with AI

267. Ben Lorica and Paco Nathan β†’ 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 β†’ The Data-Centric Shift in AI: Challenges, Opportunities, and Tools

2024

263. Qian Li and Peter Kraft β†’ Breaking the Cloud Barrier: How DBOS Transforms Application Development

262. Shreya Rajpal β†’ The Essential Guide to AI Guardrails

261. Deepti Srivastava β†’ Beyond ETL: How Snow Leopard Connects AI, Agents, and Live Data

260. Ben Lorica and David Talby β†’ 2024 Generative AI in Healthcare Survey Results

259. Ben Lorica and Paco Nathan β†’ Monthly Roundup: BAML, Tencent’s Hunyuan Model, AI & Kubernetes, and the Future of Voice AI

258. Vasant Dhar β†’ Building the Future of Finance: Inside AI Valuation Bots

257. Vaibhav Gupta β†’ Unleashing the Power of BAML in LLM Applications

256. Tim Persons β†’ Cracking the Code: How Enterprises Are Adopting Generative AI

255. Ben Lorica and Paco Nathan β†’ Monthly Roundup: Ray Compiled Graphs, Llama 3.2 and Multimodal AI, and Structured Data for RAG

254. Matt Welsh β†’ Reimagining Code: The AI-Driven Transformation of Programming and Data Analytics

253. Mars Lan β†’ The Security Debate: How Safe is Open-Source Software?

252. Yishay Carmiel β†’ Generative AI in Voice Technology

251. Aurimas GriciΕ«nas β†’ Building An Experiment Tracker for Foundation Model Training

250. Ben Lorica and Paco Nathan β†’ Monthly Roundup: AI Regulations, GenAI for Analysts, Inference Services, and Military Applications

249. Petros Zerfos and Hima Patel β†’ Unlocking the Power of LLMs with Data Prep Kit

248. Andrew Ng β†’ Advancing AI: Scaling, Data, Agents, Testing, and Ethical Considerations

247. Jay Dawani β†’ Bridging the Hardware-Software Divide in AI

246. Ben Lorica and Paco Nathan β†’ Monthly Roundup: The Economic Realities of Large Language Models

245. Evangelos Simoudis β†’ From Hype to Reality: The Current State of Enterprise Generative AI Adoption

244. Shuveb Hussain β†’ Automating Unstructured Data Extraction with LLMs

243. Alfred Spector β†’ Generative AI in Context: Hybrid Intelligence and Responsible Development

242. Ben Lorica and Paco Nathan β†’ Monthly Roundup: Navigating the Peaks and Valleys of Generative AI Technology

241. Andrew Burt β†’ From Preparation to Recovery: Mastering AI Incident Response

240. Chang She β†’ Unlocking the Power of Unstructured Data

239. Ajay Kulkarni and Mike Freedman β†’ Postgres: The Swiss Army Knife of Databases

238. Philip Rathle β†’ Supercharging AI with Graphs

237. Ben Lorica and Paco Nathan β†’ Monthly Roundup: SB 1047, GraphRAG, and AI Avatars in the Workplace

236. Jiwoo Hong and Noah Lee β†’ Integrating Fine-tuning and Preference Alignment in a Single Streamlined Process

235. Pete Warden β†’ TinyML, Sensor-Driven AI, and Advances in Large Language Models

234. Ken Liu β†’ Machine Unlearning: Techniques, Challenges, and Future Directions

233. Joao Moura β†’ Unleashing the Power of AI Agents

232. Ben Lorica and Paco Nathan β†’ Monthly Roundup: Llama 3, Agents, Evaluation Metrics, Cyc, TikTok, and more

231. Gunther Hagleither β†’ LLMs for Data Access – Unlocking Insights with Text-to-SQL

230. Nestor Maslej β†’ 2024 Artificial Intelligence Index

229. Hagay Lupesko β†’ DBRX and the Future of Open LLMs

228. Ben Lorica and Paco Nathan β†’ Monthly Roundup – New LLMs, GTC 2024, Constraint-Driven Innovation, Model Safety, and GraphRAG

227. Steve Pike β†’ Automating Software Upgrades – How to Combine AI and Expert Developers

226. Chetan Gupta β†’ Generative AI in the Industrial Sphere

225. Semih Salihoglu β†’ The Intersection of LLMs, Knowledge Graphs, and Query Generation

224. Sadegh Riazi β†’ Unlocking the Potential of Private Data Collaboration

223. Ben Lorica and Paco Nathan β†’ Frontiers of AI – From Text-to-Video Models to Knowledge Graphs

222. Jerry Kaplan β†’ Where AI Systems Are Heading Next

221. 2024 Themes and Trends in AI

220. Bryan Cantrill β†’ The AI Infrastructure Revolution: From Cloud Computing to Data Center Design

219. Evangelos Simoudis β†’ AI in Depth: Transforming Transportation, Enterprise, and Policy

218. Sharon Zhou and Greg Diamos β†’ Software Meets Hardware – Enabling AMD for Large Language Models

217. Uri Gneezy β†’ Incentives are Superpowers – Mastering Motivation in the AI Era

216. Dmitriy Ryaboy β†’ The Convergence of Biology and AI

215. Jian Zhang β†’ AI Co-Pilots in Action – Transforming Function Calling in Cybersecurity

214. Sarmad Qadri β†’ Tools and Techniques to Make AI Development More Accessible

213. Nir Shavit β†’ LLMs on CPUs, Period

2023

212. Chirag Yagnik β†’ Democratizing Wealth Management With AI

211. Juan Sequeda and Dean Allemang β†’ Knowledge Graphs: Contextualizing Enterprise Data for More Accurate LLMs

210. Max Mergenthaler and Azul Garza Ramirez β†’ TimeGPT: Machine Learning for Time Series, Made Accessible

209. Waleed Kadous β†’ Best Practices for Building LLM-Backed Applications

208. Kieren James-Lubin β†’ The Evolution of Crypto, Blockchain, and Web3

207. Ben Lorica on the Open||Source||Data podcast β†’ Open Source Data and AI: Past, Present, Future

206. Malte Pietsch β†’ Orchestration for LLM and RAG applications

205. Paco Nathan and Ben Lorica β†’ Reflections from the First AI Conference in San Francisco

204. Semih Salihoglu β†’ KΓΉzu – A simple, extremely fast, and embeddable graph database

203. Philipp Moritz and Goku Mohandas β†’ Navigating the Nuances of Retrieval Augmented Generation

202. Bill Marcellino and Nathan Beauchamp-Mustafaga β†’ The Rise of Generative AI-Powered Social Media Manipulation

201. Yucheng Low β†’ Versioning and MLOps for Generative AI

200. Christopher Nguyen β†’ Navigating the Generative AI Landscape

199. Sudhir Hasbe β†’ Trends in Data Management: From Source to BI and Generative AI

198. Yishay Carmiel β†’ AI and the Future of Speech Technologies

197. Casey Ellis β†’ The Future of Cybersecurity – Generative AI and its Implications

196. Daniel Lenton β†’ Ivy – The One-Stop Interface for AI Model Deployment and Development

195. Andrew Burt β†’ Navigating the Risk Landscape – A Deep Dive into Generative AI

194. Michele Catasta β†’ Software Development with AI and LLMs

193. Alex Chao β†’ A Lightweight SDK for Integrating AI Models and Plugins

192. Steve Hsu β†’ Using LLMs to Build AI Co-pilots for Knowledge Workers

191. Brian Raymond β†’ ETL for LLMs

190. Emil Eifrem β†’ The Future of Graph Databases

189. David Talby β†’ Delivering Safe and Effective LLM and NLP Applications with LangTest

188. Jeff Jonas β†’ Using Data and AI to Democratize Entity Resolution and Master Data Management

187. Jerry Liu β†’ An Open Source Data Framework for LLMs

186. Tim Davis β†’ Redefining AI Infrastructure

185. Andrew Feldman β†’ The Rise of Custom Foundation Models

184. Louis Brandy β†’ The Future of Vector Databases and the Rise of Instant Updates

183. Amin Ahmad β†’ LLMs Are the Key to Unlocking the Next Generation of Search

182. Jonas Andrulis β†’ Building and Deploying Foundation Models for Enterprises

181. Alex Remedios β†’ Building Robust AI Infrastructure for Critical Solutions

180. Patrick Hall and Agus Sudjianto β†’ Machine Learning for High-Risk Applications

179. Omar Maher β†’ Boosting Perception With Synthetic Data

178. Simon Chan β†’ Revolutionizing B2B: Unleashing the Power of AI and Data

177. Gev Sogomonian β†’ AI Metadata

176. Raymond Perrault β†’ 2023 AI Index

175. Hagay Lupesko β†’ Custom Foundation Models

174. Jakub Zavrel β†’ Uncovering and Highlighting AI Trends

173. Chris Wiggins β†’ How Data and AI Happened

172. Paras Jain and Sarah Wooders β†’ Blazing fast bulk data transfers between any cloud

171. Pablo Villalobos β†’ Exhaustion of High-Quality Data Could Slow Down AI Progress in Coming Decades

170. Jinsung Yoon and Sercan Arik β†’ Generating high-fidelity and privacy-preserving synthetic data

169. Brandon Jenkins β†’ How technology is disrupting the venture capital industry

168. Zongheng Yang β†’ 2023 Running Machine Learning Workloads On Any Cloud

167. Jesse Anderson, Evan Chan, and Ben Lorica β†’ 2023 Trends in Data Engineering and Infrastructure

166. Gabriela Zanfir-Fortuna and Andrew Burt β†’ Preparing for the Implementation of the EU AI Act and Other AI Regulations

165. Dylan Patel β†’ The Open Source Stack Unleashing a Game-Changing AI Hardware Shift

164. Peter Norvig and Alfred Spector β†’ Data Science and AI in Context

163. Percy Liang β†’ Evaluating Language Models

162. Ben Lorica, Mikio Braun, and Jenn Webb β†’ 2023 Opportunities and Trends – Data, Machine Learning, and AI

161. Mark Chen β†’ Exploring DALLΒ·E 2

2022

160. Wendy Foster and Olivia Liao β†’ Data Science at Shopify and Stitch Fix

159. Shayan Mohanty β†’ Building a data management system for unstructured data

158. Frank Liu β†’ A Cloud Native Vector Database Management System

157. Ira Cohen β†’ What’s Next for Machine Learning in Time Series

156. Roy Schwartz β†’ Efficient Methods for Natural Language Processing

155. Andrew Burt and Bob Friday β†’ Responsible and Trustworthy AI (Thanksgiving holiday episode)

154. Hung Bui β†’ Building a premier industrial AI research and product group

153. Bob van Luijt β†’ An open source, production grade vector search engine

152. Federico Garza and Max Mergenthaler Canseco β†’ A comprehensive suite of open source tools for time series modeling

151. Christopher Nguyen β†’ Building Safe and Reliable AI applications

150. Ram Sriharsha β†’ A new storage engine for vectors

149. Karthik Ramasamy β†’ Project Lightspeed: Next-generation Spark Streaming

148. Piotr Ε»elasko β†’ The Unreasonable Effectiveness of Speech Data

147. Yaron Singer β†’ Machine Learning Integrity

146. Yashar Behzadi β†’ Synthetic data technologies can enable more capable and ethical AI

145. Sadegh Riazi β†’ Confidential Computing for Machine Learning

144. John Bohannon β†’ Applied NLP Research at Primer

143. Jon Udell β†’ Using SQL to Retrieve Data from APIs and Web Services

142. Aadyot Bhatnagar β†’ Machine Learning for Time Series Intelligence

141. Maarten Grootendorst β†’ Unleashing the power of large language models

140. Hamza Tahir and Adam Probst β†’ Building production-ready machine learning pipelines

139. Omri Allouche β†’ Machine Learning at Gong

138. Danny Bickson and Amir Alush β†’ Data Infrastructure for Computer Vision

137. Mark Chen β†’ How DALLΒ·E works

136. Jules Damji and Richard Liaw β†’ Scalable, end-to-end machine learning, for everyone

135. Rick Lamers β†’ Orchestration and Pipelines for Data Scientists

134. Devin Petersohn β†’ Dataframes at scale

133. Nick Schrock β†’ Software-defined Assets

132. Edmon Begoli β†’ Adversarial Machine Learning

131. Haytham Abuelfutuh β†’ Orchestrating Machine Learning Applications

130. Hilary Mason β†’ Narrative AI

129. Oren Razon β†’ Machine Learning Model Observability

128. Jeremiah Lowin β†’ Dataflow Automation

127. Sebastian Raschka β†’ Practical Machine Learning and Deep learning

126. Ade Fajemisin and Donato Maragno β†’ Machine Learning for Optimization

125. Barret Zoph and Liam Fedus β†’ Efficient Scaling of Language Models

124. Olivia Liao β†’ Data Science at Stitch Fix

123. Jack Clark β†’ The 2022 AI Index

122. Ajay Kulkarni and Mike Freedman β†’ Why You Need A Time-Series Database

121. Wendy Foster β†’ Data Science at Shopify

120. Elham Tabassi and Andrew Burt β†’ An AI Risk Management Framework

119. Amit Sharma and Emre Kiciman β†’ An open source and end-to-end library for causal inference

118. Leo Meyerovich β†’ The Graph Intelligence Stack

117. Dia Trambitas-Miron and David Talby β†’ NLP and Language Models in Healthcare and the Life Sciences

116. Simon Crosby β†’ Delivering Continuous Intelligence at Scale

115. Nicholas Boucher β†’ Imperceptible NLP Attacks

114. Anjali Samani β†’ Evolving Data Science Training Programs

113. Savin Goyal β†’ Building Machine Learning Infrastructure at Netflix and beyond

112. Moshe Wasserblat β†’ Democratizing NLP

111. Gaurav Chakravorty β†’ Machine Learning at Discord

110. Mike Tung β†’ Applications of Knowledge Graphs

109. Ben Lorica and Mikio Braun in conversation with Jenn Webb β†’ Key AI and Data Trends for 2022

2021

108. Connor Leahy and Yoav Shoham β†’ Large Language Models

107. Azeem Ahmed β†’ Data and Machine Learning Platforms at Shopify

106. Christopher Nguyen β†’ What is AI Engineering?

105. Anshul Pandey β†’ NLP and AI in Financial Services

104. Che Sharma β†’ Modern Experimentation Platforms

103. Nic Hohn and Max Pumperla β†’ Reinforcement Learning in Real-World Applications

102. Nikhil Muralidhar β†’ MLOps Anti-Patterns

101.Β  Pardhu Gunnam and Mars Lan β†’ Why You Need a Modern Metadata Platform

100. Yoav Shoham β†’ Making Large Language Models Smarter

99. Jeremy Stanley β†’ AI Begins With Data Quality

98. Michel Tricot β†’ Modernizing Data Integration

97.Β  Hamel Husain β†’ Deploying Machine Learning Models Safely and Systematically

96.Β  Bob Friday β†’ Large-scale machine learning and AI on multi-modal data

95.Β  Viviana Acquaviva β†’ Machine Learning in Astronomy and Physics

94.Β  Viral Shah β†’ The Unreasonable Effectiveness of Multiple Dispatch

93.Β  Jike Chong and Yue Cathy Chang in conversation with Jenn Webb and Ben Lorica β†’ How To Lead In Data Science

92.Β  Paco Nathan in conversation with Jenn Webb and Ben Lorica β†’ Why interest in graph databases and graph analytics are growing

91.Β  Tara Kelly in conversation with Jenn Webb and Ben Lorica β†’ The State of Data Journalism

90.Β  Rayid Ghani and Andrew Burt β†’ Auditing machine learning models for discrimination, bias, and other risks

89.Β  Charles Martin β†’ An oscilloscope for deep learning

88.Β  Jesse Anderson in conversation with Jenn Webb and Ben Lorica β†’ What’s new in data engineering

87.Β  Sean Taylor in conversation with Jenn Webb and Ben Lorica β†’ Changes to the data science role and to data science tools

86.Β  Steven Feng and Eduard Hovy β†’ Data Augmentation in Natural Language Processing

85.Β  Brad King β†’ Storage Technologies for a Multi-cloud World

84.Β  Chris White in conversation with Jenn Webb and Ben Lorica β†’ Towards a next-generation dataflow orchestration and automation system

83.Β  Reza Hosseini and Albert Chen β†’ Building a flexible, intuitive, and fast forecasting library

82.Β  Sercan Arik β†’ Neural Models for Tabular Data

81.Β  Connor Leahy β†’ Training and Sharing Large Language Models

80.Β  Paolo Cremonesi and Maurizio Ferrari Dacrema β†’ Questioning the Efficacy of Neural Recommendation Systems

79.Β  Hyun Kim β†’ Automation in Data Management and Data Labeling

78.Β  Nicolas Hohn β†’ Reinforcement Learning For the Win

77.Β  Andrew Burt β†’ How Companies Are Investing in AI Risk and Liability Minimization

76.Β  Travis Addair β†’ The Future of Machine Learning Lies in Better Abstractions

75.Β  Yonatan Geifman and Ran El-Yaniv β†’ Why You Should Optimize Your Deep Learning Inference Platform

74.Β  Jerry Overton in conversation with Jenn Webb and Ben Lorica β†’ AI Beyond Automation

73.Β  Steve Touw β†’ Injecting Software Engineering Practices and Rigor into Data Governance

72.Β  Davit Buniatyan β†’ Building a data store for unstructured data and deep learning applications

71.Β  Zhe Zhang β†’ How Technology Companies Are Using Ray

70.Β  Abe Gong β†’ Data quality is key to great AI products and services

69.Β  Parisa Rashidi β†’ Machine Learning in Healthcare

68.Β  Simon Rodriguez in conversation with Jenn Webb and Ben Lorica β†’ Measuring the Impact of AI and Machine Learning Research

67.Β Β  Ryan Wisnesky β†’ The Mathematics of Data Integration and Data Quality

66.Β  Jian Pei β†’ Pricing Data Products

65.Β  Sharon Zhou in conversation with Jenn Webb and Ben Lorica β†’ Challenges, Opportunities, and Trends in EdTech

64.Β  Alex Wong and Sheldon Fernandez β†’ Towards Simple, Interpretable, and Trustworthy AI

63.Β  Assaf Araki and Ben Lorica in conversation with Jenn Webb β†’ The Rise of Metadata Management Systems

62.Β  Michael Mahoney β†’ Tools for building robust, state-of-the-art machine learning models

61.Β  Sonal Goyal and Ben Lorica in conversation with Jenn Webb β†’ Creating Master Data at Scale with AI

60.Β  Bruno Fernandez-Ruiz β†’ Bringing AI and computing closer to data sources

59.Β  Bharath Ramsundar β†’ Deep Learning in the Sciences

58.Β  Ira Cohen β†’ Taking business intelligence and analyst tools to the next level

57.Β Β  Omer Dror β†’ Data exchanges and their applications in healthcare and the life sciences

2020

56. Β  Ben Lorica and Mikio Braun in conversation with Jenn Webb β†’ Key AI and Data Trends for 2021

55.Β Β  Jesse Anderson and Ben Lorica in conversation with Jenn Webb β†’ A Unified Management Model for Successful Data-Focused Teams

54.Β Β  Dan Geer and Andrew Burt β†’ Security and privacy for the disoriented

53.Β Β  Rumman Chowdury β†’ The State of Responsible AI

52.Β Β  Jack Morris β†’ Improving the robustness of natural language applications

51.Β Β  Yishay Carmiel β†’ End-to-end deep learning models for speech applications

50.Β Β  Ram Shankar β†’ Securing machine learning applications

49.Β Β  Marco Ribeiro β†’ Testing Natural Language Models

48.Β Β  Xiyin Zhou β†’ Detecting Fake News

47.Β Β  Neil Thompson β†’ The Computational Limits of Deep Learning

46.Β Β  Piero Molino β†’ Making deep learning accessible

45.Β Β  Mayank Kejriwal β†’ Building and deploying knowledge graphs

44.Β Β  Murat Γ–zbayoğlu β†’ Financial Time Series Forecasting with Deep Learning

43.Β Β  Viral Shah β†’ A programming language for scientific machine learning and differentiable programming

42.Β Β  Kira Radinsky β†’ Using machine learning to modernize medical triage and monitoring systems

41.Β Β  Max Pumperla β†’ Connecting Reinforcement Learning to Simulation Software

40.Β Β  Weifeng Zhong β†’ Using machine learning to detect shifts in government policy

39.Β Β  Ofer Razon β†’ What is AI Assurance?

38.Β Β  Alan Nichol β†’ Best practices for building conversational AI applications

37.Β Β  Paco Nathan and Ben Lorica in conversation with Jenn Webb β†’ Tools for scaling machine learning

36.Β Β  Joel Grus β†’ From Python beginner to seasoned software engineer

35.   Bruno Gonçalves → Assessing Models and Simulations of Epidemic Infectious Diseases

34.Β Β  Karthik Ramasamy and Arun Kejariwal β†’ Improving the hiring pipeline for software engineers

33.Β Β  Lauren Kunze β†’ How to build state-of-the-art chatbots

32.Β Β  Ameet Talwalkar β†’ Democratizing Machine Learning

31.Β Β  Denise Gosnell β†’ How graph technologies are being used to solve complex business problems

30.Β Β  Amy Heineike β†’ Machines for unlocking the deluge of COVID-19 papers, articles, and conversations

29.Β Β  Christopher Nguyen β†’ Designing machine learning models for both consumer and industrial applications

28.Β Β  Matthew Honnibal β†’ Building open source developer tools for language applications

27.Β Β  Chris Wiggins β†’ Viewing machine learning and data science applications as sociotechnical systems

26.Β Β  Andrew Burt β†’ Identifying and mitigating liabilities and risks associated with AI

25.Β Β  Arun Verma (in conversation with Jenn Webb) β†’ How machine learning is being used in quantitative finance

24.Β Β  Harish Doddi β†’ Understanding machine learning model governance

23.Β Β  Wes McKinney β†’ Improving performance and scalability of data science libraries

22.Β Β  Pete Warden β†’ Why TinyML will be huge

21.Β Β  Evan Sparks β†’ An open source platform for training deep learning models

20.Β Β  Kenneth Stanley β†’ Algorithms that continually invent both problems and solutions

19.   Bruno Gonçalves → Computational Models and Simulations of Epidemic Infectious Diseases

18.Β Β  Robert Munro β†’ Human-in-the-loop machine learning

17.Β Β  Chris Nicholson β†’ Next-generation simulation software will incorporate deep reinforcement learning

16.Β Β  Solmaz Shahalizadeh β†’ Business at the speed of AI: Lessons from Shopify

15.Β Β  Edo Liberty β†’ How deep learning is being used in search and information retrieval

14.Β Β  Alejandro Saucedo β†’ The responsible development, deployment and operation of machine learning systems

13.Β Β  Edmon Begoli β†’ Hyperscaling natural language processing

12.Β Β  Krishna Gade β†’ What businesses need to know about model explainability

11.Β Β  Dean Wampler β†’ Scalable Machine Learning, Scalable Python, For Everyone

10.Β  Dafna Shahaf β†’ Computational humanness, analogy and innovation, and soft concepts

9.Β Β Β  David Talby β†’ Building domain specific natural language applications

8.Β Β Β  Morten Dahl β†’ The state of privacy-preserving machine learning

7.Β Β Β Β  Sijie Guo β†’ Taking messaging and data ingestion systems to the next level

6.Β Β Β  Bahman Bahmani β†’ Business at the speed of AI: Lessons from Rakuten

5.Β Β Β  Nir Shavit β†’ The combination of the right software and commodity hardware will prove capable of handling most machine learning tasks

2019

4.Β Β Β  Ben and Mikio Braun β†’ Key AI and Data Trends for 2020

3.Β Β Β  Rajat Monga β†’ The evolution of TensorFlow and of machine learning infrastructure

2.Β Β Β  Reza Zadeh β†’ Building large-scale, real-time computer vision applications

1.Β Β Β  Paco Nathan β†’ Taking stock of foundational tools for analytics and machine learning