Share
Redefining AI is the 2024 New York Digital Award winning tech podcast! Discover a whole new take on Artificial Intelligence in joining host Lauren Hawker Zafer, a top voice in Artificial Intelligence on LinkedIn, for insightful chats that unravel the fascinating world of tech innovation, use case exploration and AI knowledge. Dive into candid discussions with accomplished industry experts and established academics. With each episode, you'll expand your grasp of cutting-edge technologies and ...
…
continue reading
Manage episode 472480859 series 2814833
Content provided by Databricks. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Databricks or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://staging.podcastplayer.com/legal.
In this episode, Brandon Cui, Research Scientist at MosaicML and Databricks, dives into cutting-edge advancements in AI model optimization, focusing on Reward Models and Reinforcement Learning from Human Feedback (RLHF).
Highlights include:
- How synthetic data and RLHF enable fine-tuning models to generate preferred outcomes.
- Techniques like Policy Proximal Optimization (PPO) and Direct Preference
Optimization (DPO) for enhancing response quality.
- The role of reward models in improving coding, math, reasoning, and other NLP tasks.
Connect with Brandon Cui:
https://www.linkedin.com/in/bcui19/
43 episodes
))
