Analysis finds that analyzes in artificial intelligence models may slow down soon.

and analysis By EPOCH AI, the Institute of Non -profit artificial intelligence, that the artificial intelligence industry may not be able to obtain tremendous performance gains from the signs of artificial intelligence for a much longer period. Once progress is slowing from thinking models within a year, according to the results of the report.

Thinking models like Openai’s O3 It has led to great gains in artificial intelligence standards in recent months, especially the criteria that measure mathematics and programming skills. Models can apply more computing to problems, which can improve their performance, with the negative side that it takes longer than traditional models to complete the tasks.

Thinking models were developed through the first training on a traditional model on a huge amount of data, then a technology application called reinforcement learning, which effectively gives the model “reactions” to its solutions to difficult problems.

To date, Frontier AI Laboratory Laborators such as Openai have not applied an enormous amount of computing strength to the reinforcement learning stage for training form, according to EPOCH.

This changes. Openai said that it has applied about 10x computing to train O3 more than its predecessor, O1, and Epoch speculates that most of this computing was allocated to enhance learning. Openai Dan Roberts recently revealed that the company’s future plans are to call Give priority to reinforce reinforcement To use computing power much more, more than the initial model training.

But there is still a higher limit to the amount of computing that can be applied to reinforcement learning, for each period.

Josh You, an EPOCH analyst and author of the analysis, explains that performance gains from standard training of artificial intelligence are currently every year, while performance gains from reinforcement grows ten times every 3-5 months. Thinking training will continue to “may converge with the total borders by 2026.”

EPOCH analysis makes a number of assumptions, and is partially dependent on the general comments of AI’s executives. But it also makes the situation in which the decline in thinking models may be proven that they are difficult for reasons besides computing, including the highest costs of research.

“If there is a continuous general cost required for research, the thinking forms may not expand as expected,” he writes to you. “Quick scaling is a very important component of the thinking model, so it is worth tracking it closely.”

It is possible that any indication is possible that thinking models may somewhat in the near future to worry about the artificial intelligence industry, which has invested huge resources to develop these types of models. Indeed, studies have shown that thinking models, which can be Incredibly expensive for operationYou have dangerous defects, such as tendency to Hell more From some traditional models.