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Singapore – OpenAI Unveils o1 AI Model with Human-Like Reasoning

OpenAI Unveils o1 AI Model with Human-Like Reasoning

OpenAI has introduced a new AI model, internally named “Strawberry”, which showcases enhanced reasoning abilities, setting it apart from the competition in the AI landscape.

 

This latest model, known as o1, emphasizes deeper computation to provide more thoughtful responses, particularly when faced with multi-step queries, such as complex math and coding problems.

 

Though still in its early stages, it lacks certain features found in previous ChatGPT versions, such as web browsing and file uploads. However, OpenAI views this model as a breakthrough in handling intricate reasoning tasks. The company has chosen to reset its model numbering with this release, marking a fresh start with the o1 series.

 

This release comes at a time when OpenAI, headquartered in San Francisco, is seeking additional funding and faces increasing competition in the AI sector. Notably, rival companies like Anthropic and Google have been highlighting similar reasoning capabilities in their own advanced AI models.

 

In its blog post, OpenAI showcased examples of the AI model’s performance across various subjects, including coding and math, and demonstrated its ability to solve basic crossword puzzles. According to Noam Brown, an OpenAI research scientist, the preview is aimed at understanding how users interact with the model and identifying areas for improvement.

 

Users may notice a slight change in their experience with this model. Before providing a response, the new model takes a moment to consider multiple related prompts in the background, a process known as “chain of thought” prompting. This deliberate pause enables the AI to generate more comprehensive and accurate answers.

 

OpenAI has been refining multi-step problem-solving capabilities in its models for some time. In May 2023, the company published a paper detailing how it trained AI systems to handle complex math problems by rewarding each correct step in the solution process, rather than just focusing on the final correct answer.