Mathematicians at Leiden Univ. who helped write the Leiden Declaration
Within two weeks of the Open A.I. solution of the unit distance problem, a group of 16 mathematicians, in consultation with colleagues and math organizations worldwide, published the Leiden Declaration on Artificial Intelligence and Mathematics. It stated aim to “frame the conversation about future AI directions,” said Dame Ursula Martin, one of the authors, and a mathematician at Oxford.
This effort came as A.I. models have been making headlines with successful results in research-level mathematics. In late May, OpenAI, the maker of ChatGPT, announced that one of its models had disproved a notable 80-year-old mathematics conjecture in the field of combinatorial geometry. (See previous post).
The conjecture is one of some 1,200 problems posed by the Hungarian mathematician Paul Erdos. While some of these “Erdos problems” have been considered throwaway questions of narrow interest, others have proved influential and field- shaping. Along with a research paper describing the proof, OpenAI released a companion paper by several independent mathematicians. Jacob Tsimerman of the University of Toronto, an expert in the adjacent subfield of number theory, commented:
“This is a really impressive piece of work, and I would accept it for any journal without hesitation.”
In other words, the A.I. model had attained the status of a human professional journal contributor. Needless to say, panic swept the mathematics community. How many more times would A.I. models succeed in doing what their human counterparts could not. What did this incursion mean for mathematics itself?
So no surprise other figures in the field were less sanguine, including Prof. Melanie Matchett Wood, a Harvard mathematician. She remarked that the OpenAI paper did not appropriately reference “a history of closely related ideas in the literature.”
Such historical referencing with the necessary citations, is standard practice in all STEM journals. For example, in the Introduction to my 1983 Solar Physics paper on SID Flares and Sunspot Morphology,
http://adsabs.harvard.edu/full/1983SoPh...88..137A
I present a concise survey of relevant past contributions, i.e. also seeking flare-sunspot associations or correlations. Wood's take then was that the Open A.I. paper did not reach the level of an acceptable journal paper. She went on to comment:
“It is a powerful tool, and I think it will be a great tool to accelerate mathematics research. But she the community needs to figure out how to use A.I. in a way that will maintain human understanding of the mathematics.”
Here she was clearly referring to the Open AI model solution being highly counterintuitive. In this sense, most who had taken on this same Erdos problem, of the unit distance problem, had sought to prove it - rather than disprove it as the Open AI model had done. Thus, as one Open AI employee put it: "This would have been totally bananas even a month ago."
Well, because the model actually used advanced algebraic number theory (not geometry) to achieve the solution - which was in fact a disproof of the Erdos conjecture, yielding many more pairs than anyone (even Erdos) imagined. As the WSJ piece on the achievement noted:
"Only by defying conventional wisdom and experimenting with seeming improbable strategies did the model find an unexpected path forward."
But this strategy conflicts with the natural human understanding of the mathematics, which of course explains why no human solutions were forthcoming.
Included in the potential threats that the Leiden Declaration authors articulated:
- Accuracy and reliability: Journal editors are already complaining about a flood of plausible seeming A.I.- generated papers and proofs that have turned out to be incorrect, and in ways that are difficult for mathematicians to discern.
- Questions remain whether the many A.I. companies (i.e. Anthropic, Google, Open AI etc) tackling mathematics are keeping the field’s best interests in mind.
They pointedly write in the Declaration:
“Technology companies’ involvement in researchnraises the risk that research questions are prioritized and incentivized because of their amenability to A.I. methods and models, rather than their deeper significance to understanding.”
They point out this disadvantages researchers who choose not to use the technology, and those who do not have access to it.
A valid point.
According to Michael Harris of Columbia University:
“The purpose of the Declaration, from my perspective, is to recover control of the narrative about the values and the goals of mathematics from the A.I. industry. Mathematicians are concerned that the values of the profession are being misrepresented, not intentionally but due to the media campaign on the part of the industry, which seems to want to promote the belief that they are in a position to transform mathematics — 'the A.I. revolution in math,' as one headline put it not long ago."
Adding:
"If the people who make the decisions about funding base their decisions only on what’s being reported in most of the articles in the press, they could easily get the impression that A.I. is where the future of mathematics is.
We want to affirm certain values that have characterized the profession: openness, honesty, giving credit where credit is due, sharing, transparency about methodologies, and access for independent verification of results."
All of which are totally justified points. Granted then A.I. models will always have a superiority in their synthesis ability over humans, but we can't have that define the norms for the entire field of mathematics. Which also cannot afford to rend asunder the basis of human understanding of the results which will always entail independent verification.
A.I. contributions then, are exciting, but cannot become the whole, leaving out the roles and dimensions of human research in mathematics.
In addition there are ethical aspects that need to be considered, according to Rodrigo Ochigame, another participant. He noted that several A.I. companies are investing in dedicated teams focusing on mathematics, using problems as benchmarks and publications as training data.
In effect, these companies are training their models to prove theorems not because they want to advance mathematical knowledge, but because they hope that such training will improve the models’ reasoning abilities more generally.
So it's perhaps not a coincidence that OpenAI’s announcement about the unit distance conjecture solution (previous post) came out the same day the news broke that the company is preparing to file for an I.P.O.
As Ochigame points out, this has put mathematicians in a troubling ethical position. Without their consent, their published work is being used as strategic training data for the development of general-purpose A.I. The resulting models are being commercialized for many purposes, including military applications, that raise grave ethical concerns.
Most mathematicians never imagined, much less consented, that their work would be used for such purposes.
Humans need to be much more alert and on guard to what is happening amidst all the hype and hubbub, and especially how these events can adversely affect human welfare and advancement.
See Also:
Open AI Model Solves 80-Year Old Erdos Problem - And Mathematicians Freak Out
And:
Brane Space: A.I.-Bubble Angst Spills Into Bond Market - What It Means