This project is part of OpenAI's broader strategy to measure its AI models against human professionals in diverse fields. In September, OpenAI initiated a new evaluation process aimed at gauging AI performance relative to human expertise. The company views this comparison as a crucial metric in its pursuit of artificial general intelligence (AGI), defined as an AI system capable of surpassing human capabilities in most economically valuable tasks.
One confidential OpenAI document stated, "We’ve hired folks across occupations to help collect real-world tasks modeled off those you’ve done in your full-time jobs, so we can measure how well AI models perform on those tasks. Take existing pieces of long-term or complex work (hours or days) that you’ve done in your occupation and turn each into a task."
The data collection effort raises questions about intellectual property and data privacy. While OpenAI has not publicly commented on specific measures taken to address these concerns, the company's internal documents suggest an awareness of the need to handle sensitive information responsibly. The initiative also highlights the growing demand for high-quality training data in the AI industry, where the performance of AI models is heavily reliant on the data they are trained on.
The move reflects a broader trend in AI development, where companies are increasingly focused on creating AI systems that can perform complex, real-world tasks. By comparing AI performance against human benchmarks, OpenAI aims to identify areas where its models excel and areas where further improvement is needed. This approach is intended to accelerate the development of more capable and reliable AI systems.
The evaluation process could have significant implications for the future of work. As AI models become more proficient at performing tasks currently done by humans, it could lead to automation in various industries. However, OpenAI emphasizes that its goal is not to replace human workers but to create AI systems that can augment human capabilities and improve productivity. The company has not yet released specific findings from its evaluation process, but it is expected to share updates on its progress in the coming months.
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