Human-level Performance

Human-level performance refers to the ability of an AI system to perform a task at the same level as a human.

Human-level performance is commonly used as a benchmark in AI research to evaluate the effectiveness of AI systems in various tasks, such as image recognition, natural language processing, and game playing.

Measuring human-level performance involves comparing the performance of an AI system to that of humans on the same task. This can be done through experiments, user studies, benchmark tests or competitions where both AI and human participants are evaluated using the same criteria.

It is important to distinguish between the performance of an AI system, the performance of a human without any AI support, and a human using an AI system for inspiration and assistance. In most cases, there are synergistic effect to be expecten when humans use AI as assistance that surpasses the performance of either alone.

Another relevant distinction is between average human performance and extraordinary human performance. For example, an AI system might be able to compose music as well as or either better than an average human, but not as well as a top composer like Mozart or Beethoven. This highlights the need for cautious interpretation when claiming human-level performance, especially in expert tasks.

In some expert tasks, studies have shown that AI can reach human-level performance not because the AI is exceptionally good, but because human performance is inconsistent or suboptimal. In such cases, it can be challenging to determine the correct outcome if human experts do not have unanimous opinions. [cf. this and this paper].

Human-level performance is an essential concept in AI research to understand and evaluate the capabilities of AI systems in comparison to human abilities.