Quantified Uncertainty Research Institute
QURI is a nonprofit research organization researching forecasting and epistemics to improve the long-term future of humanity.
Squiggle Workshop in Washington DC this Thursday
If you happen to be around Washington DC, I'll be doing a free ~1.5hr Squiggle workshop this Thursday evening. It will cover the basics, and I'll of course be around afterwards for extended discussion. Make sure to bring a laptop. See more information and RSVP
Squiggle AI: Better Annotation, Customization
We've been hard at work improving Squiggle AI since our last announcement. Our latest update introduces a style guide, flexible documentation steps, and improved workflows that make it easier to create clear, well-documented models. New Style Guide & Step One major challenge with the original version of Squiggle
Enhancing Mathematical Modeling with LLMs: Goals, Challenges, and Evaluations
Introduction Summary Mathematical models are important tools for reasoning and decision-making across diverse fields. With the advent of large language models (LLMs), there's an opportunity to integrate these AI systems with formal mathematical modeling, potentially enhancing the capabilities and applications of both. However, evaluating the quality and effectiveness
Squiggle AI: An Early Project at Automated Modeling
We're releasing Squiggle AI on SquiggleHub. Squiggle AI runs long sequences of LLM calls in order to write, fix, and improve Squiggle code. Use it here Squiggle AI is a significant improvement over our previous experiments with Squiggle GPT and simple Claude prompts, offering more reliability and customization
Current Claims and Cruxes on LLM Forecasting & Epistemics
I think that recent improvements in LLMs have brought us to the point where LLM epistemic systems are starting to be useful. After spending some time thinking about it, I've realized that such systems, broadly, seem very promising to me as an effective altruist intervention area. However, I
LLM-Secured Systems: A General-Purpose Tool For Structured Transparency
Researcher Notes: This is a semi-polished ideation & formalization post. I spent about a week on it. It's a bit outside our main work at QURI, but I thought it was promising enough to be worth some investigation. I've done some reading into this area, and
Manifest 2024 & Squiggle
ManifestManifest is a forecasting and prediction markets festival hosted by Manifold Markets. June 7-9, 2024. Berkeley, California. Manifest 2024 was this last week, along with Manifest Summer Camp. You can see the schedules for these here. I gave two Squiggle workshops, one Squiggle presentation, and three focused events. Each session
"Full Automation" is a Slippery Metric
Research Status: Written & researched quickly. I think the key point is fairly simple and obvious. I relied on Claude to help with rewriting. There's been a growing interest in predicting when various products or jobs will be "fully automated." Will we soon have popular movies,
A Case for Superhuman Governance, using AI
I believe that: 1. AI-enhanced organization governance could be a potentially huge win in the next few decades. 2. AI-enhanced governance could allow organizations to reach superhuman standards, like having an expected "99.99" reliability rate of not being corrupt or not telling lies. 3. While there are
Ideas for Next-Generation Writing Platforms, using LLMs
I've been doing more writing recently and have been heavily leaning on LLMs to help. This has been useful, but it seems clear that the fundamental LLM abilities are far outpacing the frontends and tools that use them. Here's a quick list of ideas that I&
Higher-Order Forecasts
Higher-order forecasting could be a useful concept for prediction markets and forecasting systems more broadly. The core idea is straightforward: Nth-order forecasts are forecasts about (N-1)th order forecasts. Examples Here are some examples: 0-Order Forecasting (i.e., the ground truth) * Biden won the 2020 U.S. presidential election * The
Scorable Functions: A Format for Algorithmic Forecasting
Introduction Imagine if a forecasting platform had estimates for things like: 1. "For every year until 2100, what will be the probability of a global catastrophic biological event, given different levels of biosecurity investment and technological advancement?" 2. "What will be the impact of various AI governance
