Practical AI: Machine Learning, Data Science
Technology:Software How-To
At the recent ODSC East conference, Daniel got a chance to sit down with Erin Mikail Staples to discuss the process of gathering human feedback and creating an instruction tuned Large Language Models (LLM). They also chatted about the importance of open data and practical tooling for data annotation and fine-tuning. Do you want to create your own custom generative AI models? This is the episode for you!
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Show Notes:
Something missing or broken? PRs welcome!
Timestamps:
(00:00) - Welcome to Practical AI
(00:43) - Erin Mikail Staples
(02:09) - Open source attendees
(03:54) - The key to RLHF
(05:35) - Tooling for RLHF
(07:33) - Humanities in data science
(11:22) - Label Studio's workflow
(15:41) - The open data ecosystem
(21:04) - Do data labeling
(22:33) - Exciting changes coming
(24:15) - DevRel(ish) and other resources
(25:13) - Goodbyes
(25:45) - Outro
Rise of the AI PC & local LLMs
AI in the U.S. Congress
First impressions of GPT-4o
Full-stack approach for effective AI agents
Autonomous fighter jets?!
Private, open source chat UIs
Mamba & Jamba
Udio & the age of multi-modal AI
RAG continues to rise
Should kids still learn to code?
AI vs software devs
Prompting the future
Generating the future of art & entertainment
YOLOv9: Computer vision is alive and well
Representation Engineering (Activation Hacking)
Leading the charge on AI in National Security
Gemini vs OpenAI
Data synthesis for SOTA LLMs
Large Action Models (LAMs) & Rabbits 🐇
Collaboration & evaluation for LLM apps
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