Comment by BirbSingularity
6 days ago
I can't help but think of orthogonal frequency-division multiplexing and it's use in encoding data on multiple carrier frequencies, and it makes me wonder what other parallels we will discover between digital transmission technology for cross-domain stuff like this.
Not even cross-domain. (Nor cross-co-domain.)
Trigonometric polynomials are also polynomials. And linear spaces are all "the same". That is what the definition is for. Even the transpose-mapping is linear.
I have this strange sensation that I can't put into words that somehow we are on the brink of unveiling an entirely new paradigm of AIs or perhaps even of combining AI with classical algorithms in a way to rapidly iterate between each other (and sensor data) that will instantly 10x or 100x current capabilities.
Anyone else feel this?
I think part of it is the feeling of false understanding that comes from using llms regularly. They let you operate at a higher conceptual level, and they paper over enough of the actual details that your conceptual model might not actually be correct.
I'm a mechanical engineer by training, and have similar vibes with the similarities I see between llm training and metallurgy. I could probably put together a formal concept for these vibes at this point, but is there actually a "there" there? I have no idea. And it would take me years to actually dive in and learn everything to gain the deep understanding that would be required to know if I'm just experiencing my own brand of AI psychosis or not.
It's a brave new world, that's for sure.
Andrej Karpathy said something along the lines of “while you can use llms to outsource some of your thinking, you can’t use them to outsource your understanding “.
> that will instantly 10x or 100x current capabilities.
In the 1920s we had legions of very smart, highly trained (arguably better trained in mathematics) basically chucking relays and vacuum tubes together with reckless abandon to build the most valuable and complicated systems mankind had ever come up with (telephony, radio, radar, etc). They had no idea how they worked and only ad-hoc rules of thumb to construct them.
It took the insight of a handful of these people both in and outside of industry to formalize the theory of operation of most of what people were already building and then use that theory to establish formal design practices.
The people before these theories were realized were exceptionally smart and good at what they did, it's just they didn't have better design tools to reason about the things they were building.
And once they had those tools they didn't 10x or 100x overnight.
no. we're approach a sigmoid. AI is bloated carcass and we're tweaking out the size of the models and speed they'll run on smaller hardware.
I think to feel what you're feeling, you've bought into "all we need is more context". I think evolution demonstrates that's not really true.
They said "there are algorithmic changes that remains to be discovered" and you said they bought into the idea that "all we need is more context". Seems like opposites to me.
would you really bet that this is it? there is nothing beyond this?
reminds me of the famous anecdote of a 19th century physics professor who said "there is nothing left to be discovered in physics, only minor corrections"
then came Einstein...
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I feel like this is an inverted interpretation? Transmission tech uses those methods because the math shows the desired properties.
Linear algebra is used everywhere, orthogonalization, SVD, eigenvalues etc are valuable because the resulting properties are very useful in many places.
Yea, I could have used a better word choice. I was thinking about the domains here in the generalized sense such as signal processing and wireless communication being applicable to the domain of artificial intelligence. In reality, you are correct that it's all tied together under of domain of applied maths or computer science.