Navigating the Rapidly Evolving Tech Landscape

Trey Kollmer returns to discuss the latest AI research revelations with Nathan Labenz. They explore how new techniques will shave 10% off global compute needs, how analogical prompting beats few-shot prompting, and how compressive historical records can increase LLM memory and retention abilities. If you need an ERP platform, check out our sponsor NetSuite: http://netsuite.com/cognitive.

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Every week investor and writer of the popular newsletter The Diff, Byrne Hobart, and co-host Erik Torenberg discuss today’s major inflection points in technology, business, and markets – and help listeners build a diversified portfolio of trends and ideas for the future. Subscribe to “The Riff” with Byrne Hobart and Erik Torenberg: https://link.chtbl.com/theriff

TIMESTAMPS:

(00:00:00) - Episode Preview

(00:01:11) - Paper: Think Before You Speak

(00:03:13) - Multimodal models for combining vision and language

(00:04:19) - Backspace Paper

(00:06:25) - Chain of thought prompting for step-by-step reasoning

(00:09:14) - Backspacing in language models to correct mistakes

(00:12:05) - Attention sinks for expanding context length

(0012:41) - Paper: Large Language Models as Analogical Reasoners

(00:15:24) - Pause tokens for language models to "think"

(00:18:23) - Analogical prompting to recall relevant examples

(00:20:52) - Long context windows for language models

(00:23:20) - Markdown works best for OpenAI

(00:24:23) - Ring attention to break memory constraints

(00:26:15) - Paper: StreamingLLMs

(00:27:46) - Potential for superhuman performance with longer contexts

(00:31:01) - Dynamic context window adjustment at runtime

(00:33:53) - Retention and memory capabilities for transformers

(00:37:12) - Planning algorithms combined with memory and scale

(00:39:49) - Paper: Ring Attention

(00:42:35) - Executive assistant prompting and critique

(00:45:23) - Self-RAG for language models to find own examples

(00:48:02) - Timelines and predictions for future capabilities

(00:50:37) - Applications like analyzing long texts and scripts

(00:53:15) - Local versus global attention in transformers

(00:55:59) - Architectural changes versus just training adjustments

(00:58:41) - Pre-training strategies like random start points

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.