Quotations from the "REMARKS ON THE DISPROOF OF THE UNIT DISTANCE CONJECTURE" paper that might help non-mathematicians to appreciate its relevance to other scientific fields.

Especially relevant for biomedical problems are the noted ability of AI to find connections among scientific facts that are part of different specialized fields, and the persistence in exploring the implications of these connections.

Noga Alon

" AI was able to do here what lots of excellent human researchers tried and failed to do. Like other mathematicians who had the opportunity to experiment, even if only briefly in my case, with ChatGPT Pro 5.5, my impression has been that AI tools are capable of changing research

in mathematics in a dramatic way. The new spectacular solution of the Erdős unit distance problem convinces me that it is hard to overestimate the full potential impact of this change."

Example of first step of brainstorming on human collective scientific intelligence, with 3 AI models.

- May 2026

PROMPT

The following is the current focus of a discussion among scientists and scholars, shown on the cellcomm.org website. Can you provide a comment about the usefulness of the plan for human health and knowledge, suggestions on how to make it more likely to succeed and an estimate of the likely number of participants after the first Round, after the second Round and after five years?

Summary of older AI and Collective Intelligence discussions

        The recent progress in Artificial Intelligence provides both challenges and opportunities for scientific collective intelligence. Among the most notable examples of AI progress are ChatGPT and other Large Language Models, which have shown unexpected capabilities (Wei 2022, Mitchell 2023), and AlphaFold, which can predict the 3D shape of proteins from their genetic sequence with unprecedented accuracy (Jumper 2021). The development of AlphaFold has been recognized by the award of the 2014 Nobel Prize in Chemistry.

Challenges

          AI poses specific challenges for science. There are many reports of errors in statements from ChatGPT and from other AI systems. The types of errors and blind spots seem different from those more common in humans.

            These AI systems consist of neural networks with billions to trillions of parameters (Mitchell,…

Contribution received from Christopher Wills, Professor Emeritus of Biology, University of California, San Diego

The size increase in the human brain, which is exceptionally large for a primate having such a body mass (Miller, 2019), has led to hypotheses of a co-evolutionary positive feedback driving brain evolution. Proposed mechanisms are positive feedback between brain size and culture or language (Wills, 1993; Deacon, 1998) or between the brain sizes of humans engaged in a socio-cognitive evolutionary arms race (Dunbar, 1998; Miller, 2011). Drawing an analogy with these processes affecting the evolution of individual intelligence, we can expect co-evolution to lead to accelerated progress of AI and human collective intelligence. This is likely to be primarily cultural but given enough time it may lead to biological evolutionary changes such as the recent repeated selection of lactose tolerance in cultures that have domestic animals for milk production.

- Wills C. 1993. The R…