Date: 23-Sept-2024

Hey {{first name | AI enthusiast}},

Welcome to the latest edition of the One More Thing in AI newsletter! We are diving deep into Open AI’s Chat GPT o1 release in this edition.

There are some cool examples of folks using it!

Best regards,

Renjit Philip

OpenAI has developed a new AI model called o1. This model is exceptionally good at solving tough problems. For example:

- Programming Questions: On Codeforces, a site for competitive programming, o1 scores better than 89% of participants.

- Math Competitions: In the AIME, a math contest that helps select students for the USA Math Olympiad, o1 ranks among the top 500 students in the entire country.

- Science Tests: On a challenging test called GPQA, which covers physics, biology, and chemistry, o1 does better than human PhD students.

Even though o1 isn't as easy to use as some other models yet, OpenAI is releasing an early version called OpenAI o1-preview. You can try it out now in ChatGPT, and some trusted users can access it through an API.

OpenAI trained o1 using a method called reinforcement learning. This helps the model learn to think carefully through problems step by step, which we call a "chain of thought." What's cool is that this training method doesn't need a lot of data. They've noticed that o1 gets better when they:

- Spend more time training it (called train-time compute).
- Allow it more time to think when solving problems (called test-time compute).

The way they are improving and scaling this model is different from how they usually train large language models. They're still exploring the best ways to make it even better.

Why does it matter?

This is reported to be an AI model that is different in design from Chat Gpt4 and one that Open AI has been working on for a long time. Could this be the improvement on the transformer model that we have been waiting for?

This model demonstrates reasoning abilities at a PhD level- what is next? Boggles the mind!

Also, spare a thought for all those startups that were working on developing agents using the older LLMs. This model makes them obsolete in one fell swoop.

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