This is so beautiful. And at the same time: this is just the beginning <3 An entire generation of autodidacts will emerge with the help of AI.

these results were eye-opening for me... chatgpt agent performed better than i expected on some pretty realistic investment banking tasks

I had early access & ChatGPT agent is, I think, a big step forward for getting AIs to do real work Even at this stage, it does a good job autonomously doing research & assembling Excel files (with formulas!), PowerPoint, etc. It gives a sense of how agents are coming together

Update: I'm alive and well The results are official now and my lead over AI increased from 5.5% to 9.5%😎 Honestly, the hype feels kind of bizarre. Never expected so many people would be interested in programming contests. Guess this means I should drop in here more often👀

ChatGPT Agent: our first AI with access to a text browser, a visual browser, and a terminal. Rolling out in ChatGPT Pro, Plus, and Team today.

Humanity has prevailed (for now!) I'm completely exhausted. I figured, I had 10h of sleep in the last 3 days and I'm barely alive. I'll post more about the contest when I get some rest. (To be clear, those are provisional results, but my lead should be big enough)

I am extremely excited about the potential of chain-of-thought faithfulness & interpretability. It has significantly influenced the design of our reasoning models, starting with o1-preview. As AI systems spend more compute working e.g. on long term research problems, it is critical that we have some way of monitoring their internal process. The wonderful property of hidden CoTs is that while they start off grounded in language we can interpret, the scalable optimization procedure is not adversarial to the observer's ability to verify the model's intent - unlike e.g. direct supervision with a reward model. The tension here is that if the CoTs were not hidden by default, and we view the process as part of the AI's output, there is a lot of incentive (and in some cases, necessity) to put supervision on it. I believe we can work towards the best of both worlds here - train our models to be great at explaining their internal reasoning, but at the same time still retain the ability to occasionally verify it. CoT faithfulness is part of a broader research direction, which is training for interpretability: setting objectives in a way that trains at least part of the system to remain honest & monitorable with scale. We are continuing to increase our investment in this research at OpenAI.

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