The Future of the Transformer Part 2 with Trey Kollmer

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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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

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