YHK - yet another hacker news clone

It would be cool if some prestigious such program revamped itself to be AI-focused, maybe in collaboration with a CS department.

2) TFA argues golden age, provided LLM infrastructure is independent.

3) Tao just opened a new SAIR AI platform.

4) Tao just opened a journal for papers with Lean proofs.

5) Academics are known to play both sides of the fence.

It is absolutely bewildering how some people miss the finer points.

> Universities, professional societies, funding agencies, and scientific foundations should coordinate the development and governance of competitive LLMs. Scientists will need access to frontier capabilities, transparency about their operation and use of research data, strong confidentiality protections, and the ability to test and reproduce research claims independently.

> The massive computing resources, specialized personnel, data infrastructure, and ongoing investments needed to support this kind of initiative are not within the reach of a single institution, and a huge leadership effort will be necessary to conjure the combination of independent model development, guaranteed access to commercial systems, public computing infrastructure, and open research resources that could do the job.

the post is calling for....an enormous investment in LLM infrastructure.

By all means, please read the article if it's interesting to you and ignore it if it isn't. But I believe that it's mostly directed to those already within the research math community, with a far more important purpose than to be interesting.

As the post itself explains, we (research mathematicians, as a profession) need to coordinate on deciding what a bright future will look like in a world with powerful AI, and in plotting a course to get there. And we need to do it fast, or circumstances might decide for us.

Historically, we've rather sucked at coordination. We need leadership -- we need someone to step forward, and begin by floating ideas and getting the conversation rolling. Personally I am very grateful for what Tao is doing here.

One big problem here is that OpenAI would be hoarding this ability by 1) not releasing their top models for public use,as we have already seen them do, and 2) limiting the capability of the public API, which we have also seem them do.

This not only kills incentive for math, it means that OpenAI becomes the arbiter of math and research for the world. The research that will be done is the research OpenAI decides to do.

This seems very dangerous to me.

Another path forward is distributed local models. Professors can do research on their HPC machines, individuals can have full access to their models. It's a much more democratic approach.

When said button is in the basement, at the bottom of a broken set of stairs, locked behind a door that says "beware of the leopard", inside a filing cabinet, and implemented as

    dontTrain() { //NYI
    }

What will the ethics of math research be like in the AI era? Will it be like the apparent ethics of the tech oligarchs?

As in: does competition or cooperation produce greater economic gains in terms of cost of living, quality of life, etc?

Subjectively, I've been forced to live and work under a competitive capitalist system that goes against most of my instincts for my entire adult life, which has resulted in a profoundly negative economic outcome for myself.

Others I know have done better, either finding ways to tread water as independent freelancers/artisans or managing to save modest nest eggs (although only by finding themselves on the winning side of the stock market, rarely/never through work at a job).

Vanishingly few have reached economic independence by winning the internet lottery for example, and I don't know them personally.

So based on the evidence before me, competition is a failed model. Yes yes, I'm writing this on an electronic device that cost billions of dollars of R&D to produce. I have food in my stomach and a roof over my head. But have I worked on important problems that would advance the state of the art in my chosen field? Of course not. And neither have most of you reading this.

So I would rephrase the question: will AI allow us to explore cooperation as an alternative economic model to the one we have?

I believe that it will, but not from the top down. If we wait for the wealthy and powerful to share, we'll be waiting forever. No, progress now must come from the most downtrodden among us creating alternative ways to provide time/money/resources to the underemployed and indigent so that they can finally self-actualize outside of the status quo.

Specifically, that means automating necessity. How do I acquire enough money to pay for my food and shelter today, even if I don't have a job? How can I acquire enough money to provide for myself, someone else, and some children (my personal definition of success) without being beholden to an employer? How can I remain insulated from inflation and the tendency for the owners of private property (not personal property) to devalue currency to make the means of production they own more profitable? How can I pay this forward to the next generation so that they can become educated and someday enjoy the commons which I myself have enjoyed? And so on and so forth.

If AI can't do those things, then frankly change may remain forever out of reach. Which makes the question of whether specific fields like math and science will benefit rather moot.

Maybe it doesn’t matter and maybe that’s just the new flavour of golden.

Frontier models can easily one shot what I ask for, and make adjustments as ask to quickly make an app that meets what I wanted.

But often when I programmed outside of work (and at work too) I learnt something.

Quite often that my favourite pattern is an antipattern in some use cases.

That might never happen again at the rate of development.

> The result may be impressive, but if the only beings that can move math forward in future are machines grinding away on billions of scenarios that no human would have the patience to undertake

Seems like the same thing as neural networks. You have something like a few thousand parameters for computer that works well, but no underlying algorithms and formulas, like the shaders.

A lot of progress comes from taking assumptions and breaking them. There was a time when everyone assumed that locked checkouts were absolutely necessary for version control, for instance. You could argue that AI could learn how to do this well but humans have context that machines won't. This is just an extension of the fact that things with independent existence will have different impressions of the world. If they didn't they would functionally be one thing.

No one from OpenAI is willing to give a talk on any of their 300+ results.

There is a fixed landscape of logically provable mathematical statements. Normally, the process of exploring that landscape simultaneously improves human understanding. Now, formal proofs and actual knowledge have become decoupled.

I like the UK's archaic system of measures for the similar reasons. It has the stamp of humanity. For those who think about such things it reminds of who we were. Without historical context we think less of ourselves.

How is that Bloom filter written in Rust using Agile coming along?