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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Release date

Lydbog: 20. oktober 2023