Gradient Dissent: Conversations on AI
En podkast av Lukas Biewald - Torsdager
Kategorier:
107 Episoder
-
Deploying Autonomous Mobile Robots with Jean Marc Alkazzi at idealworks
Publisert: 18.5.2023 -
How EleutherAI Trains and Releases LLMs: Interview with Stella Biderman
Publisert: 4.5.2023 -
Scaling LLMs and Accelerating Adoption with Aidan Gomez at Cohere
Publisert: 20.4.2023 -
Neural Network Pruning and Training with Jonathan Frankle at MosaicML
Publisert: 4.4.2023 -
Shreya Shankar — Operationalizing Machine Learning
Publisert: 3.3.2023 -
Jasper AI's Dave Rogenmoser & Saad Ansari on Growing & Maintaining an LLM-Based Company
Publisert: 17.2.2023 -
Sarah Catanzaro — Remembering the Lessons of the Last AI Renaissance
Publisert: 2.2.2023 -
Cristóbal Valenzuela — The Next Generation of Content Creation and AI
Publisert: 19.1.2023 -
Jeremy Howard — The Simple but Profound Insight Behind Diffusion
Publisert: 5.1.2023 -
Jerome Pesenti — Large Language Models, PyTorch, and Meta
Publisert: 22.12.2022 -
D. Sculley — Technical Debt, Trade-offs, and Kaggle
Publisert: 1.12.2022 -
Emad Mostaque — Stable Diffusion, Stability AI, and What’s Next
Publisert: 15.11.2022 -
Jehan Wickramasuriya — AI in High-Stress Scenarios
Publisert: 6.10.2022 -
Will Falcon — Making Lightning the Apple of ML
Publisert: 15.9.2022 -
Aaron Colak — ML and NLP in Experience Management
Publisert: 26.8.2022 -
Jordan Fisher — Skipping the Line with Autonomous Checkout
Publisert: 4.8.2022 -
Drago Anguelov — Robustness, Safety, and Scalability at Waymo
Publisert: 14.7.2022 -
James Cham — Investing in the Intersection of Business and Technology
Publisert: 7.7.2022 -
Boris Dayma — The Story Behind DALL·E mini, the Viral Phenomenon
Publisert: 17.6.2022 -
Tristan Handy — The Work Behind the Data Work
Publisert: 9.6.2022
Join Lukas Biewald on Gradient Dissent, an AI-focused podcast brought to you by Weights & Biases. Dive into fascinating conversations with industry giants from NVIDIA, Meta, Google, Lyft, OpenAI, and more. Explore the cutting-edge of AI and learn the intricacies of bringing models into production.