Source URL: https://simonwillison.net/2024/Sep/15/terrence-tao/#atom-everything
Source: Simon Willison’s Weblog
Title: Quoting Terrence Tao
Feedly Summary: [… OpenAI’s o1] could work its way to a correct (and well-written) solution if provided a lot of hints and prodding, but did not generate the key conceptual ideas on its own, and did make some non-trivial mistakes. The experience seemed roughly on par with trying to advise a mediocre, but not completely incompetent, graduate student. However, this was an improvement over previous models, whose capability was closer to an actually incompetent graduate student.— Terrence Tao
Tags: o1, generative-ai, openai, mathematics, ai, llms
AI Summary and Description: Yes
Summary: The text discusses the performance of OpenAI’s o1 model, highlighting its ability to work towards correct solutions with guidance while lacking the generation of key conceptual ideas independently. The evaluation compares its capabilities to that of a mediocre graduate student, noting improvements over previous models.
Detailed Description:
The comments provided by Terrence Tao reflect a critical analysis of OpenAI’s o1 model, particularly in the context of its performance in mathematical problem-solving. This insight is relevant to professionals in AI and LLM (Large Language Model) security, as it underscores the potential capabilities and limitations of AI models in generating knowledge and solving complex problems.
Key points to consider:
– **Performance Evaluation**: The o1 model is noted for its capacity to reach correct solutions through hints and guidance, showcasing an essential strength for iterative and collaborative problem-solving.
– **Limitations**: Despite the improvements noted by Tao, the model struggles with independent conceptual generation, which raises concerns regarding its utility in autonomous applications without human intervention.
– **Comparative Analysis**: The comparison with a “mediocre” graduate student suggests that while progress has been made, significant gaps remain in the model’s ability to perform at a high intellectual level, indicating a need for ongoing development.
– **Historical Context**: The text acknowledges that earlier models performed even worse, pointing to a trajectory of improvement that might delight developers and stakeholders in AI training and application.
Considerations for professionals:
– The evaluation by an expert like Tao provides valuable insight into the evolution of LLM capabilities, important for risk assessment in deploying such systems in real-world applications.
– Understanding the limits of AI models helps in developing appropriate governance, controls, and regulations to ensure their effective and safe use, particularly in sensitive domains such as education and research.
– The importance of human-in-the-loop systems is highlighted, indicating a potential area for establishing policies and best practices around model deployment and oversight.
This analysis encourages AI practitioners to consider both advancements and shortcomings in AI capabilities, pushing for a balance between innovation and security/compliance in AI deployments.