AI researchers doubt that current models will result in AGI

Tech companies have argued that simply expanding their current AI models will lead to artificial general information (AGI). However, the performance of modern models is high, so AI researchers doubt that today's technology will lead to tighter systems.

In a survey of 475 AI researchers, approximately 76% of respondents said they were “impossible” or “very unlikely” to succeed in achieving AGI by expanding their current approach. The survey results are part of a Report by the Society for Progress in Artificial Intelligence, an International Association for Science based in Washington, DC.

This is a noticeable shift in the “need to scale” attitude that has spurred high-tech companies since the launch of the generative AI boom in 2022. Since then, most of the cutting-edge achievements have been trained by increasing the amount of data, which has resulted in improved performance. However, they appear to be stagnant with their latest releases, showing only progressive changes in quality.

“The enormous investment in scaling seemed to be constantly left behind, accompanied by comparable efforts to understand what was going on.” Stuart Russell He was a member of the panel that compiled the report at the University of California, Berkeley. “I think it began to be clear to everyone that about a year ago the benefits of scaling in the traditional sense took away the layers.”

Nevertheless, tech companies plan to spend collectively Estimated $1 trillion Support AI ambitions with data centers and chips for the next few years.

Hype about AI technology may explain why 80% of survey respondents said their current perceptions of AI capabilities were not consistent with reality. “Systems that are declared to match human performance, such as coding problems and mathematical problems, are making painstaking mistakes.” Thomas Neetteric He contributed to the report at Oregon State University. “These systems are extremely useful tools to support research and coding, but they do not intend to replace human workers.”

AI companies have recently focused on what is called inference time scaling, which takes longer for AI models to use more computing power and process queries before responding. Arvind Narayanan At Princeton University. However, he says that this approach is “a unlikely to become a silver bullet” to reach the AGI.

High-tech companies often describe AGI as their ultimate goal, but the very definition of AGI is unstable. There is Google DeepMind explained It is a system that can outperform all humans in a series of cognitive tests, and Huawei has Proposed To reach this milestone, we need a body that allows AI to interact with its environment. Internal reports for Microsoft and Openai It is listed Considering that AGI can only be achieved if Openai develops a model that can generate $100 billion in profits.