_kulang

It’s cybernetics in the original sense of the word

Not my claim, what I am saying is that as long as there is a physical connection between these domains we can’t be surprised about universal representations. In a spatial Fourier transform, spiky things have high frequency information. In a time Fourier transform, spiky sounds have high frequency information. In space, spiky things have sharp edges (attack). In time, sharp sounds have faster attack. The presence of a physical analogue here means that all these experiments are testing is whether brains identify this existing connection.

It would be more surprising if the connection was indicative of genuine universality of brains and their representations, rather than ability to identify a cross-domain mathematical/physics-cal connection.

I think programming was a proxy for understanding, that is, only by understanding a system well could you produce a good program. The actual act of programming is arguably like speaking. I wouldn’t call speaking a skill, but oration is, as is speech writing.

Assessing programming in a uni environment was effective because it was a proxy for understanding and implicitly tested things like design and debugging capability. Perhaps teaching programming required so much repetition because we are not so equipped to teach the actual skills. AI accelerates the pedagogical concern because it can generate programs without the user understanding, so assessing programming no longer by proxy assesses actual skills like design and debugging.

Sure it is. Fourier transform - Bouba and Kiki are far from the same there. And as someone else mentioned, time domain content - attack and decay. There are physical differences in the sounds which are pretty fundamental and can be definitionally linked to sharpness and softness across domains, especially as sharp objects are defined in nearly exactly the same way (except spatially).

It would be more interesting if we had two sounds which didn’t have such apparent objective differences giving rise to specific and universal subjective differences, or if something that was not so cross domain elicited a different response, e.g. linking volume with sharpness as opposed to something like size.

I guess in embedding space it would be white noise? Perfect compression would result in something indistinguishable from white noise. Or at least this is my basic understanding, that degardless of data perfect compression looks like white noise

As someone who is writing a university course that is allowing the use of AI, I think that the value of an in-person education is underestimated. I reckon that a solid chunk of what you are taught in a good university environment is soft in some sense, and that technical knowledge is less valuable than it was as compared to technical ability. The knowledge problem is what MOOCs (and any open resources) solve. I am not sure that technical ability is fostered by MOOCs as much as it is merely enabled by them.

They likely also want to have the users generate more data. They are paying people to use the subscriptions essentially, as compared to API billing.

Thank you, it reads like a psycho hazard

What’s the difference between sloppiness and fraud when you stand behind the paper all the same? I would argue that sloppiness in academia is a form of fraud. It’s one thing to be bad at writing, and another to have poor methodology

At some point during this progression, the poster lost most of the “AI-ness” and I didn’t get such a visceral reaction to it. Cool!

What’s wrong with milliwatt hours?

You’d have to live there, in the presence of a data centre for AI which actually wastes water. The numbers people throw around on social media are absurd. I’m pretty sure they do not take into account the circular reuse of water for cooling, and just look at flow rates passing over chips.

Why argue with it? Why not just edit the memory yourself?

I have quite a bit of success with Claude on Jira tickets as long as the tasks are scoped well. Put effort into the ticket, and fire Claude at the ticket with MCP. I essentially review the PR.

But this is only possible because I have created a completely new python development workflow with fixed formatting, style, and unification of tooling around uv. We’ve also split our projects into smaller repos, some which function as libraries and some which are applications depending on those libraries.

I think the key to success here is to limit what context is required to do development. I’d say it’s slightly more on the extreme side of “modular” than a codebase I would write otherwise

It’s the theory of colour charge essentially, counterpart to electrodynamics. Quarks have colour, rather than electrical charge. Mediating particle is the gluon, analogous to (but entirely different from) the photon

Sure – but I’m entitled to make a judgement on what I consider to be “good” and “bad” output from an LLM, where “good” just means “helpful to my process”.

Claude is certainly able to make a superhuman mess. All of its efficacy still hinges upon good architecture and programming principles, which do not seem to be instilled in the model by anything other than luck

I moved to Sol for my writing and it is so so much better. But it makes more mistakes. I think they have different ideas of product but it seems OpenAI is going to follow Anthropic’s lead over the next year. I think I am going to put more effort into my writing skills to remove myself from this awful situation

Adjusting for sample/population age is the simplest interpretation of that

I saw a great video on YouTube by languagejones, and he asks: do we have to tell the difference between an LLM and an idiot?

The writing is just bad. I think this is clear to everyone, so I guess we could stop at that

This looks cool, and I might use it to make use of my Clara BW (which I bought to read on and realised I liked actual books a lot more)

About the readme though: the AI-generated text is perfectly serviceable, but definitely generated with more recent LLMs (I think this one would be Claude Opus 5 from feel). There’s so much unnecessary detail about what things are “real”, and what they do “so that”. I feel like with recent Claude versions, it’s like the thinking/internal representation is leaking out into the output. It feels unpolished and unintelligent

For whatever reason, the photo is point. People are only doing it for the photo and only doing the photo for the competition. I agree that we lost something, I just also think a majority of people don’t know what’s missing: they just put effort to make the photo, rather than to make the desk

This reads like the author attempted to prompt an AI not to write like an AI. Normally, I do not care if a blog post is AI written, but in this case I could not follow the result

These are nice if you are scientifically literate but I suspect not so great for the lay

Sounds like you either had a fake or more likely, couldn’t fit them properly

Not everyone is duplicitous, and it’s not surprising that the experts on a technology work at a company that creates the technology. I assume you’d think the study was valid if the Moderna staff were testing a Pfizer vaccine?

I think CS degrees are a bit light on classical theory in the modern day. In Australia CS degrees are what they say on the tin, but in America it seems almost as if CS degrees are anywhere from cybernetics to pure software development

It’s very intuitively appealing. We like it at my university for teaching first years how to build a line following robot. It’s one of the first times you can get students to really get that “ah” moment when they realise what they can do with code—it can affect the real world!

The fascination of programmer types with classical control and estimation topics is endlessly interesting as someone who studied control and estimation and hangs out here for interest in the programming. For me it was surprising to see that JEPA is a model predictive control algorithm it an almost literal sense; I guess I’m happy to have studied what I chose when I was 18.

Thermodynamically, heat is waste energy. EVs are so efficient that scavenging isn’t practical anymore; I’m not sure the temperature gets high enough to usefully extract the heat energy for heating. ICE cars obviously produce mostly heat so getting a radiator hot enough to heat the cabin is very easy