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The AI Fundamentalists
Dr. Andrew Clark & Sid Mangalik
43 episodes
2 weeks ago
Here it is! We review the year where scaling large AI models hit its ceiling, Google reclaimed momentum with efficient vertical integration, and the market shifted from hype to viability. Join us as we talk about why human-in-the-loop is failing, why generative AI agents validating other agents compounds errors, and how small expert data quietly beat the big models. • Google’s resurgence with Gemini 3.0 and TPU-driven efficiency • Monetization pressures and ads in co-pilot assistants •...
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All content for The AI Fundamentalists is the property of Dr. Andrew Clark & Sid Mangalik and is served directly from their servers with no modification, redirects, or rehosting. The podcast is not affiliated with or endorsed by Podjoint in any way.
Here it is! We review the year where scaling large AI models hit its ceiling, Google reclaimed momentum with efficient vertical integration, and the market shifted from hype to viability. Join us as we talk about why human-in-the-loop is failing, why generative AI agents validating other agents compounds errors, and how small expert data quietly beat the big models. • Google’s resurgence with Gemini 3.0 and TPU-driven efficiency • Monetization pressures and ads in co-pilot assistants •...
Show more...
Technology
Business,
News,
Tech News
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Linear programming: Building smarter AI agents from the fundamentals, part 3
The AI Fundamentalists
29 minutes
6 months ago
Linear programming: Building smarter AI agents from the fundamentals, part 3
We continue with our series about building agentic AI systems from the ground up and for desired accuracy. In this episode, we explore linear programming and optimization methods that enable reliable decision-making within constraints. Show notes: Linear programming allows us to solve problems with multiple constraints, like finding optimal flights that meet budget requirementsThe Lagrange multiplier method helps find optimal solutions within constraints by reformulating utility f...
The AI Fundamentalists
Here it is! We review the year where scaling large AI models hit its ceiling, Google reclaimed momentum with efficient vertical integration, and the market shifted from hype to viability. Join us as we talk about why human-in-the-loop is failing, why generative AI agents validating other agents compounds errors, and how small expert data quietly beat the big models. • Google’s resurgence with Gemini 3.0 and TPU-driven efficiency • Monetization pressures and ads in co-pilot assistants •...