The Future of the Transformer Part 1 with Trey Kollmer | H100 Chips will Supercharge AI Hardware

Trey Kollmer joins Nathan Labenz for a roundup of the latest AI research! They discuss Microsoft’s Self-Taught Optimizer (STOP) research and Google’s FreshLLMs, how H100 chips will supercharge development of programs with GPT-4 level compute, LLM representation of space and time, and more! If you're looking for an ERP platform, check out our sponsor, NetSuite: http://netsuite.com/cognitive

We're hiring across the board at Turpentine and for Erik's personal team on other projects he's incubating. He's hiring a Chief of Staff, EA, Head of Special Projects, Investment Associate, and more. For a list of JDs, check out: eriktorenberg.com.

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TIMESTAMPS: (00:00:00) – Introduction (00:00:56) – Update to WGA Strike (00:03:00) – Trey Kollmer's background (00:06:00) – Scaling compute for AI training experiments with GPT-4 as reference point (00:09:00) – Inflection's plan to acquire 22,000 H100s to reach GPT-4 scale compute in 5 days (00:12:00) – Addressing knowledge cutoff in LLMs using search engines (00:15:00) – Inserting structured search results into prompts with metadata (00:16:07) – Sponsors: Netsuite | Omneky (00:18:00) – Comparing approach to Perplexity system (00:18:08) — Fresh LLMs (00:21:00) – Microsoft’s Self-taught Optimizer (STOP): Recursive self-improvement framework (00:24:00) – STOP framework works with GPT-4 but not GPT-3.5 (00:27:00) – STOP removed sandbox flag in some cases (00:30:00) – LLMs represent space and time with probe models (00:33:00) – Visualizations show emergence of spatial maps (00:33:14) — OpenAI rumours (00:36:00) – Techniques like linear probes and holdout studies (00:39:00) – DNNs extrapolate predictably by falling back to ignorance (00:42:00) – Testing different architectures, loss functions, distribution shifts (00:45:00) – Design systems to be conservative out of distribution (00:48:00) – Potential for recursive architecture search (00:50:21) — LLMs represent Space and Time (00:51:00) – Vision API enabling more capable web agents (00:54:00) – Discussion of research insights (00:57:00) – Thoughts on stochastic parrots debate (01:11:25) — Deep Neural Networks Tend to Extrapolate Predictably

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This show is produced by Turpentine: a network of podcasts, newsletters, and more, covering technology, business, and culture — all from the perspective of industry insiders and experts. We’re launching new shows every week, and we’re looking for industry-leading sponsors — if you think that might be you and your company, email us at [email protected].

Release date

Lydbog: 18. oktober 2023