TL;DR
This newsletter is about o1, OpenAI's new large language model. It looks at what makes the model different from its predecessors and its potential implications for AI development and regulation.
Here are the key takeaways:
On 12 September, OpenAI released a new large language model (LLM) called o1 (in a preview and a mini version). The major improvement of this model is its ability to 'reason'.
There are three key elements to the development of o1:
Large-scale reinforcement learning
Chain-of-thought
New training data
These three key elements (reinforcement learning, chain-of-thought and new data) of o1's development is what has enabled OpenAI to build a model that reasons. OpenAI claims that this is akin to how humans may take time to think about a response to a difficult question.
o1 potentially has three big implications:
The improved performance and capabilities of the o1 models (according to evaluations carried out by OpenAI) mainly come from increasing inference compute. This potentially introduces a new scaling law for inference whereby higher inference compute leads to higher performace.
Given that o1 expends more compute at inference time, users of the model will be hit with higher costs to query the model. No doubt OpenAI will seek to reduce these costs for users over time.
o1 challenges the presumption of AI legislation that uses training compute as a proxy for determining the risk of a model. If the inference scaling law introduced by o1 holds true, then the presumption that higher training compute equates to higher risk is significantly weakened
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