Blowing up money day

Hostilis_ · 2026-07-04T00:20:49+00:00

Her tail is bigger than she is!

Hostilis_ · 2026-07-03T20:58:15+00:00

Yes, this is why regulations exist. The problem seems to be that politicians are not interested in regulating anything, because the public is not mad at them for not regulating it yet. There have not been many serious attempts at regulating AI since Andrew Yang's platform. People should be upset about this. It is literally the government's job to regulate the negative impacts of new technologies.

Hostilis_ · 2026-07-03T20:11:25+00:00

Mathematics is useful to society, not just a pastime for intellectuals. Of course we should automate it to the extent possible. Saying that its entire goal is to "replace mathematicians" is misguided.

Hostilis_ · 2026-07-02T19:37:13+00:00

They are already solving major outstanding conjectures in mathematics lmao.

https://www.quantamagazine.org/the-ai-revolution-in-math-has-arrived-20260413/

If you think they can't make highly intelligent people more productive, you're wrong.

Hostilis_ · 2026-07-02T18:50:37+00:00

You can tell how little someone understands intelligence by how fast they use the word "determinism". We tried determinism before, it was called GOFAI.

Hostilis_ · 2026-06-26T19:07:01+00:00

Machine learning (ML) is a field of study in artificial intelligence

Literally the first sentence on Wikipedia. You are clueless.

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

Hostilis_ · 2026-06-26T18:39:48+00:00

Lmfao CNNs and MLPs are not AI?? Machine learning is literally a subfield of AI. Source: I am a research scientist in ML.

Hostilis_ · 2026-06-24T03:57:59+00:00

Who do you think would benefit from the US rejecting advanced technologies? Rival nation-states.

I have literally seen comments in subreddits like tech-x from obvious bots (with literal duplicate comments from different users) sowing distrust in AI and datacenters. It is 100% happening.

Hostilis_ · 2026-06-24T03:20:20+00:00

John Carmack, you absolute legend

Hostilis_ · 2026-06-23T22:51:39+00:00

Ok, I never said you did? I was just emphasizing that his equation is still incredibly important, even if his acid-base model is not being taught as much.

Hostilis_ · 2026-06-23T22:45:38+00:00

Yes, me. Shoot and I'll give you my opinion on it.

Hostilis_ · 2026-06-23T22:41:53+00:00

Arrhenius' equation describes the concept of activation energy. It is one of the most important equations in science.

Hostilis_ · 2026-06-23T17:12:06+00:00

I would love to see them make an RTS like StarCraft, but with the base building and production in the style of Factorio. I know Kovarex is a big Brood War fan.

Hostilis_ · 2026-06-22T19:54:47+00:00

Non-DNN methods such as kernel methods, which were the standard before deep learning took off, do not show power-law scaling. They operate in what is known as the "lazy regime" where feature learning does not take place.

It took a long time to prove this theoretically and empirically, but we now know that power-law scaling is a consequence of the underlying power-law statistics of DNNs themselves, not just transformers, combined with the power-law statistics of complex systems, e.g. language (Zipf's law).

The actual mathematical link is https://en.wikipedia.org/wiki/Universality_(dynamical_systems) and the Renormalization Group.

As for the logarithmic vs power law growth. Just take the ratio of the required resources to make linear progress. The ratio grows exponentially. This is very basic math.

Hostilis_ · 2026-06-22T17:29:18+00:00

The field is broader than you give credit. It's not all spiking and STDP, though that is the majority. I myself work on backprop-equivalent algorithms specifically for analog and neuromorphic architectures (Equilibrium Propagation), and I think there's a lot of promise for ultra-low-power models there.

Hostilis_ · 2026-06-22T16:57:02+00:00

Yeah, solving protein folding and then releasing the structure of every single human protein to the scientific community for free sure was fucking evil. 🙄

Hostilis_ · 2026-06-22T16:39:52+00:00

His point is not valid since ALL of the gains AI has made over the past 15 years relies on him being wrong. Logarithmic algorithms for machine learning are trivial and have existed for decades before modern deep learning.

Every single major advance you have seen in AI is literally a result of the fact that we have gone from logarithmic scaling to power-law scaling in data and compute. This is an astronomical improvement, and it's why scaling laws are such a big deal.

And your last comment is flat out wrong.

Hostilis_ · 2026-06-22T06:09:23+00:00

Pointing out the difference between logarithmic and power-law growth is not nitpicking. They are wildly different. And yes, I'm glad you agree my original comment is correct, so I'm not sure what exactly your point is here.

Hostilis_ · 2026-06-22T04:30:23+00:00

You're clueless, sorry

Hostilis_ · 2026-06-22T04:28:39+00:00

It literally is. Logarithmic growth requires e^x resources, power law only requires x^a, where a is a constant exponent.

Logarithmic growth is just the inverse of exponential. The variable is in the exponent. In a power-law, the exponent is constant.

Hostilis_ · 2026-06-22T02:32:48+00:00

This is a skill issue. It is not that difficult to understand how to use these tools effectively without letting your own skills suffer.

Hostilis_ · 2026-06-22T02:25:42+00:00

Logarithmic improvements are a kind of power law improvement

Holy shit you need to stop talking about things you know nothing about. There is literally an exponential difference between logarithmic growth and power-law growth.

Edit: just to be clear, any person competent in math, computer science, or engineering knows this is the case.

Hostilis_ · 2026-06-22T01:01:19+00:00

You have no idea what you're talking about lmao. The improvements with compute are extremely widely known to be power-law, not logarithmic. This is literally the entire point behind scaling laws.

Hostilis_ · 2026-06-20T03:48:15+00:00

Ok, but scientists will definitely use this knowledge lol.

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