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

"The Cognitive Revolution" | AI Builders, Researchers, and Live Player Analysis

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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