OpenAI o3 shows that AI models are evolving in new ways – but also at a cost
The article discusses progress in AI scaling laws, pointing to OpenAI’s o3 model as a key breakthrough. The o3 model shows an impressive performance in benchmarks such as ARC-AGI, scoring 88% compared to 32% of its predecessor o1.
This improvement is due to the “test time scale”, which involves more computation during the test. However, this approach leads to higher costs, because o3 requires more resources than the previous model.
Despite its advantages, the high computing requirements of o3 make it practical for daily use, limiting it to high-end scenarios where its cost is justified.
The article states that while o3 shows potential to develop AI capabilities, it is not yet AGI and still struggles with simple tasks due to hallucinations.
Although the high cost of the time scale of the test is suitable for certain fields such as academia and finance, it raises questions about its widespread applicability.
The development of better AI research chips can improve test time. Overall, o3’s performance shows that, despite its current limitations, test-time computing could be a promising area for scaling AI models in the future.
Basic techniques:
OpenAI’s o3 model shows significant performance in benchmarks such as ARC-AGI, but requires significant computational resources and is expensive to run.
Increasing the test time, which involves more computation during the test, is a promising way to improve the performance of AI models, but it also increases the cost.
Despite its high performance, o3 is not yet practical for daily use due to its high computing costs, making it more suitable for special, high-end applications.
Developing more efficient AI research chips can help reduce costs associated with increasing test times and make advanced AI models more affordable in the future.
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