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

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

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Oct 18, 2023

Dive into the exciting world of AI as Trey Kollmer reveals how H100 chips are set to revolutionize model training and boost capabilities to GPT-4 levels. Discover OpenAI’s staggering revenue growth, forecasted to leap from $20 million to $1.3 billion in a year. Learn about the token economics shaping competition in AI, and how new models like LAMA 2 are interpreting space and time, proving AI can map geography with surprising accuracy. The podcast also navigates the intricacies of AI safety and model confidence in unfamiliar scenarios.

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INSIGHT

H100 Chips Accelerate AI Development

H100 chips achieve 100 trillion operations per second, enabling faster training of large language models (LLMs).
Training GPT-4-level models will become much faster, accelerating AI research and experimentation.

INSIGHT

OpenAI's Explosive Growth

OpenAI's revenue has increased 50x year over year, showing strong demand for AI services.
OpenAI likely aims to lower prices, making large model training less necessary for most users.

ADVICE

Using FreshLLMs Research

Use Google's FreshLLMs research as a starting point for connecting LLMs to search tools for current knowledge.
Temper expectations of surpassing Perplexity's performance immediately, given their benchmark focus and latency optimization.

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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 erik@turpentine.co.