A Study from Google: AI Models Think Collectively, Not Individually

SadaNews - A recent research study co-authored by researchers from Google Research revealed that some advanced artificial intelligence models do not operate on simple linear logic. Instead, they exhibit patterns closer to collective intelligence, in a behavior resembling an internal debate among a group of human minds.

The study, published on the arXiv platform under the title "Reasoning Models Generate Societies of Thought", focused on advanced reasoning models, including DeepSeek-R1 and Alibaba's QwQ-32B model, concluding that these models not only process data mathematically but also implicitly imitate multi-agent interactions within a single model.

Internal Debate Instead of Unilateral Thinking

According to the researchers, these models demonstrate what is known as "diversity of perspectives", generating opposing ideas at times and then weighing and resolving contradictions internally, in a manner akin to a team discussion striving to reach the best possible decision.

In other words, the final answer emerges only after an internal debate that is not visible to the user, according to a report published by "digitaltrends" that was reviewed by "Al Arabiya Business".

This proposition challenges the prevailing notion for years in Silicon Valley, which assumed that improving artificial intelligence primarily depends on expanding model sizes, increasing data, and injecting more computational power.

The study emphasizes that the way the thinking process itself is organized is just as important as size or computational strength.

The Devil's Advocate Inside the Model

The research findings indicate that the effectiveness of these models stems from their ability to perform what resembles a "perspective shift", where they review their conclusions, pose clarifying questions, and test multiple alternatives before arriving at the final answer.

This is akin to having an internal "devil's advocate" that compels the model to question its own logic rather than settle for the first conclusion.

What Does This Mean for Users?

For users, this shift could represent a qualitative leap in the quality of artificial intelligence.

Instead of confident but sometimes incorrect answers, collective intelligence models can be more accurate, better at handling complex and ambiguous questions, and behave more like human thinking.

Researchers also view this approach as a way to help reduce bias, as considering multiple perspectives internally minimizes falling into a single line of thought or an incomplete vision.

Towards a New Generation of Artificial Intelligence

Ultimately, these findings push towards redefining artificial intelligence, from being an advanced computing machine to an organized thinking system based on internal diversity and implicit collaboration.

If these hypotheses are confirmed, the future of artificial intelligence may not only be in building larger models but also in designing digital work teams within a single model.

This positions the concept of collective intelligence, often associated with biology and human societies, as a candidate to become the basis for the next leap in the technology world.