Practical AI: Machine Learning, Data Science
Technology
With all the LLM hype, it’s worth remembering that enterprise stakeholders want answers to “why” questions. Enter causal inference. Paul Hünermund has been doing research and writing on this topic for some time and joins us to introduce the topic. He also shares some relevant trends and some tips for getting started with methods including double machine learning, experimentation, difference-in-difference, and more.
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Timestamps:
(00:00) - Welcome to Practical AI
(00:43) - Intro to causality & Paul Hünermund
(05:35) - Why causality?
(08:11) - Determinism vs non-determinism
(11:01) - Gaining confidence
(14:06) - Sponsor: Changelog News
(15:53) - Main ways to use causal inference
(20:09) - Making it practical
(22:50) - First steps to take
(25:10) - Some helpful resources
(27:35) - Daniel's practical example
(33:01) - The effects of causal learning
(37:11) - Closing thoughts
(41:33) - Outro
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
Advent of GenAI Hackathon recap
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