The Many Ways that Digital Minds Can Know – Ryan Moulton's Articles

LLMs do all the things people say they do at once, even if those people vociferously disagree, and they’re all useful.

Detractors of LLMs describe them as blurry jpegs of the web , or stochastic parrots . Promoters of LLMs describe them as having sparks of AGI , or learning the generating function of multivariable calculus . These positions seem opposed to each other and are the subject of acrimonious debate. I do not think they are opposed, and I hope in this post to convince you of that. In particular: LLMs do both of the things that their promoters and detractors say they do. They do both of these at the same time on the same prompt. It is very difficult from the outside to tell which they are doing. Both o

related reading

• As Rocks May Think | Eric Jangevjang.com
• Patterns for Building LLM-based Systems & Productseugeneyan.com
• LLM Daydreaming · Gwern.netgwern.net
• What We’ve Learned From A Year of Building with LLMs – Applied LLMsapplied-llms.org
• GenAI Handbookgenai-handbook.github.io
• Against LLM Reductionism — LessWronglesswrong.com
• Large Language Model: world models or surface statistics?thegradient.pub
• [2605.15156] MeMo: Memory as a Modelarxiv.org
• Is AI Reasoning Right for the Wrong Reasons? | Quanta Magazinequantamagazine.org
• Things we learned about LLMs in 2024simonwillison.net
• Thinking to recall: How reasoning unlocks parametric knowledge in LLMsresearch.google
• Rethinking LLM Memorization – Machine Learning Blog | ML@CMU | Carnegie Mellon Universityblog.ml.cmu.edu