aleph_minus_one

> Anyone credible [who believes P=NP with some reasonable probability?

Do you accept

- Moshe Y. Vardi [1]

- Anil Nerode [2]

- Donald Knuth [3]

to be credible? Source for the statements that they consider P=NP to be possible: [4]

And you may include me in the list of credible P=NP believers because treat myself as a credible authority, i.e. when I ask myself whether something is right, I consider myself to be much more credible than some famous scientist. ;-)

(to be very clear: the previous paragraph is rather a clear statement against this kind of "hero worship" with respect to mathematical or scientific problems. One should consider the evidence by oneself instead of believing in P!=NP because lots of computer scientists claim all the time that this is likely true, or consider P=NP now to be possible just because some famous computer scientists consider it to be possible)

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[1] https://en.wikipedia.org/w/index.php?title=Moshe_Vardi&oldid...

[2] https://en.wikipedia.org/w/index.php?title=Anil_Nerode&oldid...

[3] https://en.wikipedia.org/w/index.php?title=Donald_Knuth&oldi...

[4] https://en.wikipedia.org/w/index.php?title=P_versus_NP_probl...

> stop shilling

What makes you believe stillpointlab is a shill instead of a person who has a specific opinion on this topic?

> Imagine a programmer telling their boss it’s not about working software it’s about the journey of writing the code. Good luck getting hired.

Your formulation is a little bit exaggerated, but I (and many, though far from all, programmers) would agree with the following more "tame" formulation:

Getting software to solve a business problem in an elegant and maintainable way means that we got the abstractions right. Similarly, if a software can easily and elegantly be modified to cope with model business problems, it is also a strong sign that we got the abstractions right. In this sense, the business problems that the software solves are just waypoints on the journey of finding the proper elegant abstractions for putting the business problems into code.

Turning these novel insights about "good" abstractions into code and programming languages enables the team to iterate faster and generate business value faster. So, appreciating that it indeed all about the journey means a commitment towards an approach to accelerate the rate with which the team can turn business insights into code and code into business value.

I know that many managers do hate such a formulation, but I stand behind it.

This depends a lot on how deeply integrated you and your workflows are into the idiosyncrasies of some specific LLM or class of LLMs.

> It's going to boil down to cost of the hardware vs. pay per use cloud models. I'm on the 100$/month chatgpt plan (~88euro). With all the resets, credits, and all the rest I typically don't run out of tokens. This is fine for me. I might go to the 200$/month plan at some point.

It's also about "insurance protection" against the AI company raising the prices or changing their terms (e.g. becoming more frugal with respect to token drops).

> He has a youtube channel with lectures in number theory, category theory and other things.

The channel: https://www.youtube.com/@richarde.borcherds7998

> What else are you paying for?

- Lab access

- Access to PhD advisor

- Research seminars

- ...

> Yea, that is why we are not getting any where...

Which alternative way of doing measurements do you propose? As far as I know, measuring intrinsic properties, i.e. "using a ruler that inhabits the same space" is with our current technology the only possible way of doing measurements.

> Oh, you mean like Google? Yea, how's that working out.

Because of such reasons many companies are indeed quite suspicious of using GCP, and for the same reasons prefer to use AWS or Microsoft Azure.

> How are general-relativistic effects relevant for the outcome of the Michelson-Morley experiment?

General-relativistic effects are relevant for the outcome of the Michelson-Morley experiment in the sense that the general theory of relativity is a generalization of the special theory of relativity. :-)

Otherwise: the additional predictions that the general theory of relativity makes about gravity, curvature of space etc. are not relevant for the outcome of the Michelson-Morley experiment.

> But for the world itself and what harbor's it, asking for falsifiability is like measuring the expansion of space using a ruler that inhabits the same space.

Which exactly is what general relativity is centrally about and the reason why general relativity is defined in terms of intrinsic properties of the Einstein manifold describing the space of the universe. Intrinsic properties are, colloquially, indeed properties that can be measured "using a ruler that inhabits the same space".

Gauß' Theorema Egregium [1] is an early predecessor of intrinsic properties of general manifolds, and says that the curvature of a surface can indeed be "be determined entirely by measuring angles, distances and their rates of change on a surface, without reference to the particular manner in which the surface is embedded in the ambient 3-dimensional Euclidean space" ([1]).

[1] https://en.wikipedia.org/w/index.php?title=Theorema_Egregium...

> ...for a niche of customers, from whom the revenue is probably entirely negligible to OpenAI.

> Not saying it's right, ethical or anything like that, but it's not "stupid"

Quite many well-paying customers become very suspicious of whether integrating OpenAI into their workflows if they read about cases where OpenAI banned accounts willfully.

If OpenAI wants to become "infrastructure provider other companies should depend on (and thus pay)", OpenAI must behave like an infrastructure provider as for electricity or internet, i.e. must only ban accounts in exceptionally rare cases that 99.9 % of society will consider to be acceptable.

> consulting firms [...] rarely hire true subject matter experts (they don't pay well enough).

This may hold in the USA, but clearly not in Germany. If, as a subject matter expert, you do have the choice and can tolerate the high stress and high workload, big consulting firms typically pay much better.

The reasons why true subject matter experts nevertheless often work somewhere else are different:

- As I already mentioned: the consulting environment is very stressful and the workload is huge. This environment is rather not suitable if you have or plan to have a family.

- Consulting companies typically want to hire employees who are smooth salesmen first and subject matter experts second. Many real experts on some topic often rather tend to be more opinionated and "rough around the edges". So they very often don't get hired.

- Very often the daily job working at a consulting company is very different from the topics one is an expert in. Many subject matter experts prefer an environment where they can apply their skills better.

- For many subject matter experts, the topic where they really excel in is very narrow and specialized and very different from the topics where a consulting companies can easily sell "experts" to the customer. So, many subject matter experts don't get hired because their topic of expertise is not easy to sell to customers and/or is too niche.

> Of course Bell later proved by experiment that quantum state cannot be explained as simply as a local hidden variable, no matter how it looks like.

Bell's theorem does not prove that. An alternative explanation is superdeterminism:

> https://en.wikipedia.org/wiki/Superdeterminism

> That's like saying "you love cooking, so for fast progress you insert coins into doordash to make fast progress on a meal". If you do that, you don't love cooking.

Rather, it's like saying "I love eating good food."

One approach is to learn to cook excellently, buy high-quality ingredients etc. Then invest time to cook a delicious meal. This is like learning math, becoming exceptional in one area, and then work on the problem.

Another approach is to go to a well-rated restaurant, order some food there, and pay a decent amount of money for the food. This is like buying AI tokens, and setting up some agents to work on math problems.

Both approaches are suitable. And I guess/hope many people who really would love to contribute to progress in mathematics would really prefer to go the first way. But because

- they are not highly gifted

- have a time-consuming daily job and/or family

- simply cannot afford the high opportunity cost to get to a suitable level in math

- ...

the only way such people can contribute to progress in math is the second approach. That's why these people behave like they do.

I am quite confident that most of such contributors don't like that their money lands in the hands of OpenAI/Anthropic.

So: Which approach would you recommend these people who are really eager to contribute to progress on some mathematical problems?

> For this work, you're either inserting coins into Codex, effectively donating money

Rather: as a necessary evil you give money to OpenAI/Anthropic because you really want to see (ideally fast) progress on this mathematical/algorithmic problem.

> Why would someone contribute to an AI project without getting paid?

Because there exist people who are in deep love with mathematics, but weren't able to get a research position (or cannot afford to take a temporary position at some university for which they would even have to relocate).

So, your question is more like: why would someone play a video game without getting paid?

> You don't read AI written code, you ask an AI to explain it to you.

Rather: you reject AI-written code if it is not of really good quality.

See https://bigospeedrun.com/exact-dft for the comparison to the previous record of κ=1.266⋅10^−3

See also https://bigospeedrun.com/integer-multiplication

> I need to see SQLite implemented in PowerShell

I would be much more interested in seeing an implementation in Nock ([1], see also [2]).

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[1] https://docs.urbit.org/nock/what-is-nock

[2] https://en.wikipedia.org/wiki/Urbit

Does "Visual Basic" in OpenAI's list also include VBA?

> Though, as a public figure if your private life becomes embarrassing, or salacious you’re guaranteed to have news hounds on your trail.

This is like: if you engage in anti-social behaviour or plan some crime, you deserve to have hounds (law enforcement agencies, intelligence agencies) on your trail because of surveillance data that was gathered about you.

> Can’t take the heat, stay out of the kitchen.

This is like the political argument: "If you have nothing to hide, why are you opposed to the new surveillance law?"

> I do think politicians should waive some privacy rights when elected. Same thing for their bank accounts and other assets.

This would make surveillance laws socially much more acceptable. The politicians will claim:

- "We are surveilled anyway, so we can pass surveillance laws against citizens, too.", or

- "The citizens really ask for surveillance laws against specific groups (in this case: politicians). So, we will, of course, give the citizens what they want and pass surveillance laws against potential terrorists/child abusers/[whatever is currently fashionable]".

If you consider privacy or the right not to be surveilled to be important, this better should hold for the enemy (politicians).

> If OpenAI's super-intelligence can solve Navier-Stokes why can't they fix this stupid bug that ate up 330GB of my Mac's disk space?

Because the former can be marketed very prestigiously, the latter hardly can.

According to https://www.briefia.fr/en/article/openai-revendique-la-resol... it is estimated that solving Navier-Stokes did cost OpenAI 40 million USD. I would bet that if OpenAI spent the same amount of money using its super-intelligence to fix this bug, this would work.

But seems not to be very economical. :-)

> Whereas pretty much everything hacking-related is already a felony thanks to DMCA and other similar laws, so they don't have much to lose.

Hackers were much more rebellious in former days before such laws were past. My opinion the reason why hackers got more "civilized" is that you could earn quite a lot of money by getting (and staying in) a job in big tech.

> finding yourself in an echo chamber (by choice or not) online means that you may find yourself less comfortable talking with people you don't agree with, things like that. I grew up in a small town pre-social media, and that meant I didn't have much of a choice about who I was surrounded by. If I was growing up today, what would stop me from retreating to a comfy social media-mediated environment without any challenge or friction? [...] That world sounds terrifying to me.

One advantage of the new situation is that you collect less hate for people whose opinion you disagree with because you can mostly ignore them instead of having to get along with them - and with each interaction collecting another dose of hate for them and their opinion.

> My friend, who is a math professor, told me that they weep because they are about to become just like us developers: prompt writers and AI output reviewers.

If it was about understanding interesting proofs that some AI came up with, and extracting their essence, I don't think the situation would be so bad.

I see the much larger problem in the now existing career prospects of non-tenured researchers in mathematics. Everybody know how precarious the career prospects in academia and how precarious often the non-tenured positions in academia already are. With the new AI breakthroughs in math, these have in my opinion even worsened a lot.

UPDATE: For a related point, see also pixl97's post: https://news.ycombinator.com/item?id=50104145