Practical AI: Machine Learning, Data Science
Technology:Software How-To
You can’t build robust systems with inconsistent, unstructured text output from LLMs. Moreover, LLM integrations scare corporate lawyers, finance departments, and security professionals due to hallucinations, cost, lack of compliance (e.g., HIPAA), leaked IP/PII, and “injection” vulnerabilities.
In this episode, Chris interviews Daniel about his new company called Prediction Guard, which addresses these issues. They discuss some practical methodologies for getting consistent, structured output from compliant AI systems. These systems, driven by open access models and various kinds of LLM wrappers, can help you delight customers AND navigate the increasing restrictions on “GPT” models.
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Timestamps:
(00:00) - Welcome to Practical AI
(01:02) - Growing AI interests
(02:36) - Disclaimer?
(04:17) - Co-host & guest?
(05:34) - Pressures of AI
(11:47) - Unlocking more value
(14:50) - Sponsor: Changelog News
(16:43) - Open access models
(20:25) - Where do we place our bets?
(25:35) - Structured 7 typed output
(30:33) - Problems of the space
(37:55) - Determining your needs
(41:22) - Ease of use
(43:03) - No code world
(45:27) - The future question
(48:06) - Goodbyes
(48:52) - 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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