As generative AI (GenAI) becomes popular across the spectrum, Sam Altman-run OpenAI has introduced a new ‘reasoning’ model which has been trained to answer more complex questions, faster than a human can.
According to the AI company, it trained the ‘OpenAI o1 model’ to spend more time thinking through problems before they respond, much like a person would. Through training, they learn to refine their thinking process, try different strategies, and recognize their mistakes.
The new AI model can be used by healthcare researchers to annotate cell sequencing data, by physicists to generate complicated mathematical formulas needed for quantum optics, and by developers in all fields to build and execute multi-step workflows.
“We’ve developed a new series of AI models designed to spend more time thinking before they respond. They can reason through complex tasks and solve harder problems than previous models in science, coding, and math,” the company added.
In tests, the model performs similarly to PhD students on challenging benchmark tasks in physics, chemistry, and biology.
“We also found that it excels in math and coding. In a qualifying exam for the International Mathematics Olympiad (IMO), GPT-4o correctly solved only 13 percent of problems, while the reasoning model scored 83 percent,” said OpenAI.
The coding abilities were evaluated in contests and reached the 89th percentile in Codeforces competitions.
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As an early model, it doesn’t yet have many of the features that make ChatGPT useful, like browsing the web for information and uploading files and images.
However, for complex reasoning tasks, this is a significant advancement and represents a new level of AI capability.
“Given this, we are resetting the counter back to 1 and naming this series OpenAI o1,” said the company.
It has also developed a cheaper model in the ‘reasoning’ series, called OpenAI o1-mini, which is a faster reasoning model that is particularly effective at coding.
As a smaller model, o1-mini is 80 percent cheaper than o1-preview, making it a powerful, cost-effective model for applications that require reasoning but not broad world knowledge.
