shash42

The premise is simple: AI is already improving lives, and we should have a page that collects these wins.

We only talk about how "intelligent" the models are, what jobs they might automate, the trillions being invested, who will "win" the AI race, and the safety risks. All these things naturally worry people, and make them averse to AI. I think we're missing the point.

The goal of technology should be to improve people's lives. AGI, ASI, RSI are all just in-group goals, that aren't obviously beneficial on their own. Speculative promises shouldn't be all we have to offer. Imagine releasing a new model and saying "it will score 99% on these benchmarks one day" instead of reporting what it already does today.

I've spent the last few weekends reading about AI systems, both generative and predictive, that are already making a large positive impact on the world. It gives me hope, and I made this page so it does the same for others.

Ultimately, the final, and only evaluation for AI that matters is whether it improved lives. The models are already so good, and yet the world is not that different. I think it's important to change that, and this is my attempt to paint the target.

There's no way the list is complete, so if you think an AI system has had a large positive impact, do share it through the submit page!

For multi-agent jailbreak propagation research only

New blogpost: Why I think automated research is the means, not just the end, for training superintelligent AI systems.

In pointing models at scientific discovery, we will have to achieve the capabilities today's LLMs lack: - long-horizon palnning - continual adaptation - reasoning about uncertainty - information-efficient learning - and creative exploration.

Some of these capabilities may emerge from large-scale training. Others will will require changes in how we implement and train AI systems. I don't yet know how exactly such a training loop would look. So consider this post a conjecture.

But science offers a few unique properties at its foundation: - large open data - verifiability - truth-seeking (instead of power-seeking) incentives.

And thus I think scientific discovery is the ideal successor to internet-scale pretraining. It's not just an application, it maybe the means to building what we're missing. Maybe that's why we have @openai @GoogleDeepMind @periodiclabs @futurehouse etc. all focusing on it.

Where do learning signals come from when there is no ground truth in post-training?

New paper shows how to convert inference-time compute into high quality supervision for RL training.

Up to 30% rel. improvement on a realistic non-verifiable tasks (HealthBench), with the models own self-synthesised rubrics!

Does continued scaling of large language models (LLMs) yield diminishing returns? Real-world value often stems from the length of task an agent can complete. We start this work by observing the simple but counterintuitive fact that marginal gains in single-step accuracy can compound into exponential improvements in the length of a task a model can successfully complete. Then, we argue that failures of LLMs when simple tasks are made longer arise from mistakes in execution, rather than an inability to reason. We propose isolating execution capability, by explicitly providing the knowledge and plan needed to solve a long-horizon task. We find that larger models can correctly execute significantly more turns even when small models have 100\% single-turn accuracy. We observe that the per-step accuracy of models degrades as the number of steps increases. This is not just due to long-context limitations -- curiously, we observe a self-conditioning effect -- models become more likely to make mistakes when the context contains their errors from prior turns. Self-conditioning does not reduce by just scaling the model size. In contrast, recent thinking models do not self-condition, and can also execute much longer tasks in a single turn. We conclude by benchmarking frontier thinking models on the length of task they can execute in a single turn. Overall, by focusing on the ability to execute, we hope to reconcile debates on how LLMs can solve complex reasoning problems yet fail at simple tasks when made longer, and highlight the massive benefits of scaling model size and sequential test-time compute for long-horizon tasks.