Practical AI: Machine Learning, Data Science
Technology
Model sizes are crazy these days with billions and billions of parameters. As Mark Kurtz explains in this episode, this makes inference slow and expensive despite the fact that up to 90%+ of the parameters don’t influence the outputs at all.
Mark helps us understand all of the practicalities and progress that is being made in model optimization and CPU inference, including the increasing opportunities to run LLMs and other Generative AI models on commodity hardware.
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Show Notes:
Something missing or broken? PRs welcome!
Timestamps:
(00:44) - Neural Magic Mark Kurtz
(03:24) - Why does LLM size matter?
(06:15) - GPUs vs. CPUs
(08:45) - Overcoming perception
(10:54) - Most parameters dont affect results
(16:01) - Balancing space & sparsity
(17:47) - Tackling performance hits
(20:38) - Aware optimization vs not?
(23:52) - Community tools
(26:11) - Neural Magic tools
(29:56) - Supporting new architecture
(31:40) - Exciting research trends
(34:52) - Looking forward in this space
(37:05) - 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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