We’re releasing Squiggle AI, a tool that generates probabilistic models using the Squiggle language. This can provide early cost-effectiveness models and other kinds of probabilistic programs.

No prior Squiggle knowledge is required to use Squiggle AI. Simply ask for whatever you want to estimate, and the results should be fairly understandable. The Squiggle programming language acts as an adjustable backend, but isn’t mandatory to learn.

Beyond being directly useful, we’re interested in Squiggle AI as an experiment in epistemic reasoning with LLMs. We hope it will help highlight potential strengths, weaknesses, and directions for the field.

Screenshots

The “Playground” view after it finishes a successful workflow. Form on the left, code in the middle, code output on the right.The “Steps” page. Shows all of the steps that the workflow went through, next to the form on the left. For each, shows a simplified view of recent messages to and from [...]

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Outline:

(00:44) Screenshots

(01:13) Motivation

(02:51) Description

(04:04) Example Outputs

(07:07) How Good Is It?

(08:52) Alternatives

(09:42) Using Squiggle AI

(11:24) Recommended Tips for Better Results

(13:00) Complementary Tools

(14:11) Privacy

(14:34) Technical Details

(14:37) Performance and Costs

(15:41) State Machine Details

(17:26) Conclusion

(17:54) Appendix: Lessons Learned During Development

(18:00) LLM cost-effective estimates seem tractable

(18:53) LLMs are willing to estimate more than you might expect, with some prompting

(19:41) Specialized Steps Improve Results

(20:25) It's difficult to make complex models understandable

(21:39) Prompt engineering can be a lot of work

The original text contained 1 image which was described by AI.

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First published:
January 3rd, 2025

Source:
https://www.lesswrong.com/posts/7worWgggeHL3Eb7wq/introducing-squiggle-ai

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Narrated by TYPE III AUDIO.

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Images from the article:

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“Introducing Squiggle AI” by ozziegooen

LessWrong (30+ Karma)