Intelligent evaluation and emerging phenomena in the AI era In the era of generative AI, intelligent evaluation standards are facing changes. The anthropomorphic behavior of large models triggers the uncanny valley effect, and the Turing test standard is questioned. New viewpoints emerge, and there is a hidden connection with the phenomenon of project manpower demand.

The so-called uncanny valley effect refers to the feeling of discomfort and fear that occurs when a robot or humanoid image is similar to a human to a certain degree. This phenomenon is particularly evident in the application of large models. For example, when we communicate with intelligent customer service, if its answers are too realistic but slightly unnatural, we may feel uneasy.

At the same time, the Turing test proposed by Alan Turing in 1950 as a standard for measuring machine intelligence has been increasingly challenged. Many experts believe that the ability to have a conversation alone cannot fully represent intelligence, and other aspects such as reasoning ability are equally important.

An interesting phenomenon in this series of changes and discussions is that the demand for people to work on projects is increasing. Although on the surface, this seems to have no direct connection with the technological changes in the field of AI, a deeper look will reveal that there are inextricable connections between them.

With the development of AI technology, many new projects are emerging. These projects often require talents with specific skills and knowledge to promote them. For example, in the fields of natural language processing and computer vision, the demand for professional talents is becoming increasingly urgent.

The process of finding people for a project is actually a screening and evaluation of the intelligence level of talents. When looking for suitable candidates, we should not only consider their professional skills, but also their innovation ability, problem-solving ability, teamwork ability and other qualities.

From another perspective, the advancement of AI technology has also provided new means and ways to find talent for projects. Through big data analysis and intelligent algorithms, project requirements and talent characteristics can be matched more accurately, improving the efficiency and accuracy of finding talent.

However, this process is not smooth sailing. There are also some problems and challenges in the process of finding people for the project. For example, due to insufficient understanding of AI technology, the definition of talent needs is not accurate enough; or due to information asymmetry, it is difficult to find excellent talents.

In the face of these problems, we need to continue to explore and innovate. On the one hand, we need to strengthen the learning and application of AI technology and improve the level of project management and talent selection; on the other hand, we need to establish a more complete talent evaluation system to give full play to everyone's potential.

In short, in the AI era, the changes in intelligent evaluation standards, the development of large models, and the phenomenon of project recruitment are intertwined and jointly affect the development of the science and technology field. We need to respond to these changes with an open mind and innovative thinking to keep moving forward in this era full of opportunities and challenges.

Guan Leiming

Ola Lowe