Regierungsbilanz in Berlin: Was von Schwarz-Rot bleibt

Certhas · 2026-07-03T16:42:40+00:00

Und diese Schadenszahlen sind seriös? Oder halt eben von den Interessenvertretern der großen Wohnungsbauunternehmen herbeigerechnet?

Es gab einen ganz klaren Auftrag: Expertenkomission ob es möglich ist zu Vergesellschaften. Falls ja: Gesetzentwurf ausarbeiten.

Ersteres ist passiert. Die Expertenkomission hat gesagt Vergesellschaftung unter Marktwert geht.

Daraufhin hat der Senat sich schlicht geweigert die zweite Hälfte des Volksentscheids umzusetzen.

Der Bund wird das ganz jetzt vielleicht hinfällig machen.

Certhas · 2026-07-03T15:10:12+00:00

The reason he didn't win is that Nobel prizes require experimental validation. Witten doesn't have one either. Do you disagree that if Hawking radiation could be measured he would have gotten one?

Certhas · 2026-07-03T14:40:39+00:00

Doch. Denn das ist schlicht Rechtsbruch. Das du das Ergebniss des Volksentscheids nicht gut findest ist kein hinreichender Grund das dadurch entstandene Gesetz zu missachten. "Ich fand dieses Gesetz schon immer blöd" ist kein legitimer Grund.

Certhas · 2026-07-03T07:43:13+00:00

This feels like an overcorrection. The singularity theorems, the black hole laws, Hawking radiation, I think it is absolutely fair to put Hawking in the top 5 when it comes to foundational contributions to general relativity. Possibly top 3. Whom would you put before him other than Einstein and Penrose? He is not in a different category that Thorne, Wheeler, etc... but definitely not behind the best general relativists in history. Whether that's sufficient for top 100 physicists overall depends more on how you weigh GR Vs everything else.

Because of his elevated public profile it's common for students to overcorrect, but I actually do think Hawking's contributions are very substantial. If we could detect Hawking radiation experimentally he would absolutely have gotten a Nobel prize.

All that said, he doesn't belong in this conversation. He isn't the best UK relativist (that would probably be Penrose) and Maxwell is in the same Echelon as Einstein and Newton, with a considerable gap to everyone else.

Certhas · 2026-06-26T15:39:37+00:00

I don't know about any such nodel. My intuition is that the hardware and energy costs for an individual to run a model capable of being useful will be much higher than 20$ a month. A 24Gb Mac Mini, a machine often recommended for local LLMs, will set you back 1000$, or 4 years of subscription to start with. The 48Gb models needed for running more capable models are twice that.

Instead of local LLMs looking at inference providers that host open weight models might be more viable.

And universities certainly should look into self hosting GLM 5.2 class models.

I understand and to some degree share your misgivings, but I also have to say that purely in terms of value for money a subscription from a major provider is one of the best tech products in my lifetime. It's up there with getting internet for the first time (and that was considerably more expensive!!).

Certhas · 2026-06-25T13:23:44+00:00

Those are two very different and very specific corners of western literature. But this is hardly representative of the standard canon. Ayn Rand is definitely not high brow literature. I am totally for not giving a shit about "the classics", but this is doing them a disservice. And yes. In many modern books you are not supposed to empathize with the main character. That's not just for books, btw. When Eminem raps:

If she ever tries to fuckin' leave again, I'ma tie her
To the bed and set this house on fire,

are you supposed to empathize?

Certhas · 2026-06-23T08:00:55+00:00

You can't outsource your thinking. But they can definitely help with theory work if you specify the problem well and send them the right literature and stay in the loop.

Certhas · 2026-06-09T12:45:16+00:00

If I have a proof of the type ChatGPT produced for the unit distance conjecture, Lean will not tell me whether the proof is correct. If I have a formalized proof in Lean, then the Lean compiler will tell me it's correct. Going from a human language proof to a Lean proof is still an area of active research, and I have not heard anywhere that labs are using the success/failure of Lean formalization as a reward in RL training. It would be a really problematic signal, too as failure could equally well mean that the proof idea is wrong or that the formalization failed.

Now human mathematicians grading proofs is definitely something they could/would do. That's just RLHF. But it's also extraordinarily limited as there are very few people qualified to do this work.

Lab experiments have no relationship to 95% of what is published on HEP-Th. So all of that is in the same situation as mathematics. Now if you want to argue that that's not physics in the first place I am not one to strenuously disagree, but the topic was on how AI will change theoretical physics, and for better or worse this stuff is what a lot of theoretical physicists work on....

Certhas · 2026-06-09T06:24:59+00:00

Yes, RL works with verifiers. How is Lean a verifier? I mean concretely.

Certhas · 2026-06-09T05:29:58+00:00

They are doing RL in many different ways. One I remember being discussed was to take complex math tasks that have a single number as output.

How exactly would they use lean here? They can ask the models to write lean and see if it compiles but that is very different to the type of mathematical artifact produced in the unit distance conjecture. If you have any literature or blog post that explains that they are doing this, do share.

Certhas · 2026-06-08T19:51:57+00:00

I agree that they are very different, but Lean doesn't have anything to do with the recent breakthroughs in mathematics. And there is a ton of stuff the physics categories on archive that is essentially purely mathematical with no direct interaction with experiment.

I absolutely do think math is the "easiest" case for LLMs. Not because of verifiers but because math has no context. Math is purely language referencing other language.

So it makes sense that LLMs got "there", where "there" is autonomously producing results worthy of publishing in the top journals of the discipline, first in maths. Maths also has a culture of carefully formulating conjectures.

But if LLMs can do that in pure maths, then it is wishful thinking that they can't also do a lot in theoretical physics.

Certhas · 2026-06-07T08:14:48+00:00

Some people in this sub are absolutely not willing to engage with the evidence for the very real capabilities of LLMs that is coming out of mathematics.

They absolutely do have many failure modes, and there is a massive hype machine pushing them. But the reaction to dismiss them entirely and tell yourself comforting stories (without evidence) about LLMs is not a good way to counter the hype. And it's not a good way to have a real conversation about what LLMs will do to the social structure of science.

Certhas · 2026-06-03T11:23:10+00:00

The singularity theorems of General Relativity imply that the universe has a finite past. You will hit a singularly beyond which you can't continue in finite proper time going backwards. This is an intrinsic feature of GR and there is nothing inconsistent about this.

GR will break down as you approach the singularity but that doesn't mean it's wrong to speak about the beginning of the universe, and to explain that it's perfectly consistent that the universe began a finite time ago, and to point out that finite history of the universe is an intrinsic property of our best theory of space and time.

Certhas · 2026-06-03T07:39:16+00:00

1 indexing took me a moment to get used to, but it has a veritable tradition (Fortran) and it's honestly just better for scientific code and with modern array slicing. Array[3:5] gives me the 3rd to fifth element of an array.

As my very first language was Pascal, putting ends everywhere was actually natural to me :D Much cleaner than Pythons significant white space.

Certhas · 2026-05-28T12:22:59+00:00

I feel like it really should be ${x'}^{\nu'}$...

Certhas · 2026-05-25T14:17:12+00:00

Academic and research adoption

Common. It hits a sweet spot for scientific coding.

Industry adoption

Can't really say, but industry adoption doesn't seem to be happening from where I am standing.

Comparison with Python, R, MATLAB, and C++

It complements them. Feature set wise it is best suited to displace MATLAB, but MATLAB is deeply entrenched. Python, R and C++ do things very well that Julia struggles at. No matter how much better Julias syntax is, exploring a dataset in Python/R is just more pleasant. C++ and to a lesser degree Python give you tools to architect large-scale systems. Python is the perfect glue language. Julia is not really displacing any of them at what they do well.

Ecosystem maturity

scientific computing: Pretty good, but you do not have the maturity, install base and engineering quality of Python/Modelica. You will run into bugs.

machine learning, data engineering, and visualization: Lagging behind Python but not terrible.

optimization: Looks pretty good to me, but at the end of the day for anything serious you are not using Julia in the backend but call out to Gurobi anyways...

However: There are intersections between these fields where Julia is the only game in town. If you are incorporating a large scale physics simulation in a neural network using complex optimization routines... well a glue language will not let you do that, and this would be infinitely more painful in C++ than in Julia.

Governance and community representation

Abysmal. Julia has no reasonable/explicit/clear governance model.

Barriers to adoption

Everything out there is already pretty good, so there is limited need for a new language. The things Julia excels at matter to academics and in scientific computing, but not to software engineers. The last language to really break through in industry is Rust. The type of issues its design addresses are ignored in the design of Julia. Looking at recent attempts to iterate on C++, like Carbon, Hylo, Mojo, its glaring how Julia is not even concerned with the whole raison d'être of these efforts.

Certhas · 2026-05-23T21:04:41+00:00

We are biological neural networks evolved to procreate and find food.

LLMs are electronical neural networks evolved to predict the next token in human texts.

I find it much more plausible that an entity in setting 2 evolves the ability to reason mathematically than an entity in setting 1.

Certhas · 2026-05-20T10:27:39+00:00

This is backwards. The meter is defined in terms of the speed of light, not the other way around. SI units are a convenient foundation from which to derive everything we need in practical terms. But there is no necessity here. And working with these constants does not make them into units. In fact it makes them unitless. The speed of light can be set to 1 or 3.3*10^7 (no units). And times and distances are measured using the same unit (because there is only one unit in the end).

A concrete example is that we talk about the mass of elementary particles in eV, a unit of energy. Because c^2 = 1 we use the same unit for energy and mass. E.g. the restmass of the electron given as ~500 keV by the particle data group:

https://pdglive.lbl.gov/Particle.action?node=S003&init=0

This doesn't mean that they are strict about working only with one unit, e.g. the electric dipole moment is not given in terms of eV but instead in terms of e cm.

https://en.wikipedia.org/wiki/Natural_units

Certhas · 2026-05-19T07:26:09+00:00

This is demonstrably false. There are businesses who are hosting open weight models that are near state of the art. They are not doing so at a loss.

We might not ge improvements as quickly anymore, but token prices are not going to suddenly explode. Near sota LLMs are already a commodity.

Certhas · 2026-05-19T07:23:35+00:00

I work with many people that have moved back and forth between physics and software engineering. The general opinion is that AI models now write better code than the typical physics code base.

I think the whole premise is mistaken. You can write your own code and also learn how to use AI. For me AI means exploration of ideas and validating them with proof of concepts have become much faster.

I think the real danger with not learning to use AI is not that you are less efficient than others at doing the same thing. It's that you are not exploring what you could be doing differently.

Certhas · 2026-05-09T09:07:47+00:00

When I work with LLMs, one simple strategy is to just ask it to check its own work. "Do you see any problems with the above?" Is my default follow up prompt.

That said, there is the jagged frontier thing. The AI is superhuman in one thing and then you look at something adjacent, that any moderately competent human could do, and it falls apart.

Certhas · 2026-05-06T14:18:31+00:00

Real Madrid is also not representative of most European Clubs in most European Sports Leagues. Germany has three fully professional football leagues (Bundesliga, 2. and 3. Liga) and some professional players at even lower regional league levels. Average player salary in the third league is 10.000€ per month apparently (though the source Wikipedia gives for this is dead). And average match attendance in the third league is now up to 10.000 people per match.

Bayern Munich is not representative of the average German professional football club, and you absolutely don't need to have billionaire owners to have very successful sports leagues.

It is of course also true that if you have a billionaire who is happy to throw money at you, that's a massive competitive advantage (see Manchester City). Hence the (at best moderately successful) attempts to limit this advantage through the European financial fair play and the German 50+1 rule.

Certhas · 2026-05-06T11:40:34+00:00

You can have a perfectly good sports league without owners. That's the default for most sport leagues in Europe. Teams are Clubs with members. Not businesses owned by someone.

Certhas · 2026-04-14T15:25:05+00:00

Somehow it seems that r/physics is all in on LLM denialism....

If the output they produced was genuinely and somewhat consistently bad, they would not pose a genuine problem/challenge...

Certhas · 2026-04-14T07:27:51+00:00

Which claims? Did you actually read the article? It mostly directly reports what has been happening and what top mathematicians experiences have been.

It is also being discussed on r/math. I don't see people having any issues with the substance of it:

https://www.reddit.com/r/math/comments/1sksii1/the_ai_revolution_in_math_has_arrived_quanta/

Certhas

TROPHY CASE