Viva, Atticus!

> Fui um dos membros "fundadores" dessa organização e gostaria de poder 
> responder quaisquer perguntas que vocês tiverem a respeito. Eu me oponho 
> fortemente ao uso atual da IA na pesquisa matemática, e acho que, se não 
> houver reformas fortes com respeito a publicação, revistas, padrões éticos na 
> área, etc, o jeito no qual muitos matemáticos (incluindo alguns dos 
> matemáticos mais prominentes) estão usando a IA vai destruir a nossa 
> comunidade.
>
> Se vocês tiverem perguntas, por favor, me perguntem; eu tô totalmente 
> disposto a explicar o propósito da organização mais.

Tenho uma pergunta: seria este o tipo de "parceria" contra a qual a
AHM se posiciona?

%%%

https://arxiv.org/html/2609.05669v1#intelligence:~:text=1%2E2%2E%20Use%20of%20artificial%20intelligence

1.2. Use of artificial intelligence

To complete this work, we built our own experimental proof-development
environment primarily around OpenAI’s Codex, with occasional use of
Anthropic’s Claude Code.

The evolving body of mathematical information which is required to
discover and construct a long mathematical proof—not merely the
streamlined argument presented a posteriori in the finished
paper—spans many model context windows. Our environment therefore
served as an external memory and coordination layer, as well as the
interface through which we interacted with the models and directed the
development of the proof. It decomposed our evolving proof into over a
hundred blocks, distributed across auxiliary files, and a “master”
file assembling them. Models could operate on individual blocks
largely independently, using only the locally relevant context and
dependencies.

An HTML-based visualizer displayed the proof’s structure and current
review status of each block, while Python tools tracked their
dependencies and propagated status changes downstream. This setup
allowed us to retain full mathematical control while using frontier
models to draft and check technical passages. It also allowed us to
quickly propagate changes in the proof architecture through the entire
argument. When, for example, we changed an early definition or lemma,
the models carried out routine downstream revisions and identified any
genuinely new mathematical obstacles that arose. This allowed us to
fully focus on the genuine blockers at each moment.

[...]

This is a factual account of the division of labor in this project,
not a general claim about the limits of the models. We record it not
only for transparency, but also because it illustrates a mode of
AI-assisted mathematics that is obscured by a focus on one-shot
theorem proving. In this project, the strategy and key computations
remained human-generated, while AI substantially accelerated their
local completion, systematic criticism, revision, and propagation.

As the models and the surrounding workflows improve, the division of
labor described here may also change; this statement records the
balance in the present project. We realize, however, that the
mathematics to come may emerge from a different balance between human
and machine.

%%%

Tudo de bom,
João Marcos

-- 
https://sites.google.com/site/sequiturquodlibet/

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