PM_ME_VINTAGE_30S [he/him]

Disagree. The technology will never yield AGI as all it does is remix a huge field of data without even knowing what that data functionally says.

We definitely don’t need AGI for AI technologies to be useful. AI, particularly reinforcement learning, is great for teaching robots to do complex tasks for example. LLMs have shocking ability relative to other approaches (if limited compared to humans) to generalize to “nearby but different, enough” tasks. And once they’re trained (and possibly quantized), they (LLMs and reinforcement learning policies) don’t require that much more power to implement compared to traditional algorithms. So IMO, the question should be “is it worthwhile to spend the energy to train X thing?” Unfortunately, the capitalists have been the ones answering that question because they can do so at our expense.

For a person without access to big computing resources (me lol), there’s also the fact that transfer learning is possible for both LLMs and reinforcement learning. Easiest way to explain transfer learning is this: imagine that I want to learn Engineering, Physics, Chemistry, and Computer Science. What should I learn first so that each subject is easy for me to pick up? My answer would be Math. So in AI speak, if we spend a ton of energy to train an AI to do math and then fine-tune agents to do Physics, Engineering, etc., we can avoid training all the agents from scratch. Fine-tuning can typically be done on “normal” computers with FOSS tools.

all it does is remix a huge field of data without even knowing what that data functionally says.

IMO that can be an incredibly useful approach for solving problems whose dynamics are too complex to reasonably model, with the understanding that the obtained solution is a crude approximation to the underlying dynamics.

IMO I’m waiting for the bubble to burst so that AI can be just another tool in my engineering toolkit instead of the capitalists’ newest plaything.

Sorry about the essay, but I really think that AI tools have a huge potential to make life better for us all, but obviously a much greater potential for capitalists to destroy us all so long as we don’t understand these tools and use them against the powerful.

The exam is tomorrow (today is another)

Ouch. Been there. Good luck on your exams!

Sounds like fun! I’m going to bed soonish but I’m willing to answer questions about multivariable calculus probably when I wake up.

When I took multivariable calculus, the two books that really helped me “get the picture” were Multivariable Calculus with Linear Algebra and Series by Trench and Kolman, and Calculus of Vector Functions by Williamson, Crowell, and Trotter. Both are on LibGen and both are cheap because they’re old books. But their real strength lies in the fact that both books start with basic matrix algebra, and the interplay between calculus and linear algebra is stressed throughout, unlike a lot of the books I looked at (and frankly the class I took) which tried to hide the underlying linear algebra.

I’m autistic too and I had to relearn math as an adult. Now I know calculus and advanced mathematics.

I can go find some book recommendations, but when I was first learning I really got a lot out of watching The Organic Chemistry Tutor.

Linear algebra (ex: multiply the matrices A and B), multivariable calculus (example: find ∇F with F=[xy,yz,xz]^T ), or actual “multidimensional analysis” (example: define the norm of [1m,1m/s,1m/s^2 ] in a way that makes sense)? I can help with all three.

It can use ChatGPT I believe, or you could use a local GPT or several other LLM architectures.

GPTs are trained by “trying to fill in the next word”, or more simply could be described as a “spicy autocomplete”, whereas BERTs try to “fill in the blanks”. So it might be worth looking into other LLM architectures if you’re not in the market for an autocomplete.

Personally, I’m going to look into this. Also it would furnish a good excuse to learn about Docker and how SearXNG works.

LLMs are not necessarily evil. This project seems to be free and open source, and it allows you to run everything locally. Obviously this doesn’t solve everything (e.g., the environmental impact of training, systemic bias learned from datasets, usually the weights themselves are derived from questionably collected datasets), but it seems like it’s worth keeping an eye on.

Google using ai, everyone hates it

Because Google has a long history of doing the worst shit imaginable with technology immediately. Google (and other corporations) must be viewed with extra suspicion compared to any other group or individual because they are known to be the worst and most likely people to abuse technology.

Literally if Google does literally anything, it sucks by default and it’s going to take a lot more proof to convince me otherwise for a given Google product. Same goes for Meta, Apple, and any other corporations.

If your signal looks like f(t) = K•u(t)e^at with u(t) = {1 if t≥0, 0 else}:

• If Real(a) > 0, then your signal will eventually blow up.
• If Real(a) < 0, then you signal will not blow up. In fact, your signal will have a maximum absolute value of |K|, and it will approach zero as time goes on.
• If Real(a) = 0, it is either a complex sinusoid or a constant. In either case, it is bounded with maximum absolute value of |K|. It very much does not blow up.

So e pops up all the time in stable systems and bounded signals because the function e^at solves the common differential equation dx/dt = ax(t) with x(0)=1 regardless of the value of a, particularly regardless of whether or not the real part of a causes the solution to blow up.

Reddit --> Lemmy

Facebook --> fucking nothing lmao

YouTube --> FreeTube + Invidious [1]

Windows --> Debian [2] with KDE Plasma

Word --> LyX

Microsoft Office --> LibreOffice

Built-in phone music player --> Odyssey [3]

Firefox --> LibreWolf [4]

Adobe Reader --> Okular + Librera on Android

Default phone launcher --> KISS Launcher

[1] I prefer FreeTube on computers where I have it installed, but one of my family’s jank 10-year-old work PCs can’t handle it, so I’ll typically watch videos in Invidious in LibreWolf on that computer.

[2] I can’t recommend Debian for absolutely everyone since it prioritizes stability and predictability over new features and ease of use, but it’s great for most of my use cases. I typically recommend Linux Mint for complete beginners.

[3] It handles extremely large music libraries (>100 GB of .mp3 files) without constantly taking forever to reload when I add a single new album.

[4] Firefox is pretty good and FOSS, but LibreWolf comes with better defaults and I’m a lazy fucker.