Wrote a spiking neural net (something I’d never heard of previously) in Rust (a language I had 0 experience with) in three days… while being unable to get the motivation to look for a new apartment until literally the day before school started and my lease ended.
Fun times.
May I draw your attention to the humble single word “set” which has 48 definitions?
Or the documentation is basically empty and tells you nothing at all (looking at you WinRT…)
Futurama had a couple bangers both Hermes Requisitioned His Groove Back and, of course, The Devils Hands Are Idle Playthings
Destiny has cheated me by forcing me to decide upon
The woman that I idolize, or the hands of an automaton
Without these hands I can’t complete the opera that was captivating her
But if I keep them, and she marries him, he probably won’t want me dating her
Your boss:
I talk with my friends because I’m part of the bourgeoisie
You talk to your friends but you’re members of the proletariat
We are not the same. You’re fired.
“Paul Atreides who catches hunter seekers with his bare hands, like a hero of old!”
Need to find apartment. Can’t do anything else till I find new apartment. Also can’t bring self to find new apartment. Suffering.
¿Qué dice Juan?
Idk about it helping me fall asleep faster, but the precision amount of time I stay asleep when on my meds seems to fit with “resetting my clock”
Melatonin might help
If I take melatonin when I’m not on my meds, I fall asleep fast enough but don’t sleep very well and have odd dreams.
If I take my meds but not melatonin, I tend to stay awake a long time.
If I take melatonin while my meds are in my system, I sleep for almost exactly 7.5hrs and wake up automatically, well rested.
No idea why that is. Anyone else relate?
Ah rip.
Fun fact I don’t think I meant to respond to your comment since you seem to have systems and are able to make tasks and reminders. Unfortunately I just read through all the comments and none of them sound familiar so I don’t know which comment I meant to respond to. Perhaps my reading comprehension is just terrible right now lol
Anyway, you seem to be on top of things, more so than myself so sorry for the misplaced suggestion.
Oh but you might like Obsidian if you want digital notes, and if you want to like remember trains of thoughts or ideas that you don’t have time for in the moment but think you’ll forget about. You can link notes so you can find the ideas later by remembering what the thought was related to instead of having to like organize the notes into folders (though you can do that too).
Get medicated! (This is not meant to be a demand just excitement because it was the solution for me) My siblings and mother can make massive amounts of lists and routines, and I never understood how they could plan things and remember to look at or follow the plans.
Then I got prescribed adderall. Guess who can make lists now…
Tbf it still takes effort but the executive dysfunction isn’t nearly as impassable as it was. Plus getting anxious makes me want to make lists now instead of making me too anxious to make lists lol
Technically evolution is an emergent phenomenon from the reproduction of organisms. If you were to clone yourself with slight mutations it would still allow evolution to happen with zero inherent need for sex or sexuality whatsoever
Regardless of that, sexualizing women typically leads one to be an incel not a successfully mating male which means it would be the opposite of evolution by your reasoning.
It’s also interesting that your premise is generic but your conclusion is specific. “Sex is to be encouraged for evolution; that’s why we should sexualize women specifically” If you aren’t a misogynist, you might want to switch your TL;DR to “sexualize people” not specifically “women”
That’s awfully presumptuous of you, perhaps the comment was made by a woman who is sexualizing short men
I ask myself that everytime I watch One Punch Man and reach episode 2
The Japanese are not afraid to give a mosquito monster tits and ass and sexy camera pans lol
Thanks, I almost didn’t post because it was an essay of a comment lol, glad you found it insightful
As for Wolfram Alpha, I’m definitely not an expert but I’d guess the reason it was good at math was that it would simply translate your problem from natural language into commands that could be sent to a math engine that would do the actual calculation.
So basically act like a language translator but for typed out math to a programming language for some advanced calculation program (like wolfram Mathematica)
Again, this is just speculation because I’m a bit too tired to look into it rn, but it seems plausible since we had basic language translators online back then (I think…) and I’d imagine parsing written math is probably easier than natural language translation
Engineer here with a CS minor in case you care about ethos: We are not remotely close to AGI.
I loathe python irrationally (and I guess I’m masochist who likes to reinvent the wheel programming wise lol) so I’ve written my own neural nets from scratch a few times.
Most common models are trained by gradient descent, but this only works when you have a specific response in mind for certain inputs. You use the difference between the desired outcome and actual outcome to calculate a change in weights that would minimize that error.
This has two major preventative issues for AGI: input size limits, and determinism.
The weight matrices are set for a certain number of inputs. Unfortunately you can’t just add a new unit of input and assume the weights will be nearly the same. Instead you have to retrain the entire network. (This problem is called transfer learning if you want to learn more)
This input constraint is preventative of AGI because it means a network trained like this cannot have an input larger than a certain size. Problematic since the illusion of memory that LLMs like ChatGPT have comes from the fact they run the entire conversation through the net. Also just problematic from a size and training time perspective as increasing the input size exponentially increases basically everything else.
Point is, current models are only able to simulate memory by literally holding onto all the information and processing all of it for each new word which means there is a limit to its memory unless you retrain the entire net to know the answers you want. (And it’s slow af) Doesn’t sound like a mind to me…
Now determinism is the real problem for AGI from a cognitive standpoint. The neural nets you’ve probably used are not thinking… at all. They literally are just a complicated predictive algorithm like linear regression. I’m dead serious. It’s basically regression just in a very high dimensional vector space.
ChatGPT does not think about its answer. It doesn’t have any sort of object identification or thought delineation because it doesn’t have thoughts. You train it on a bunch of text and have it attempt to predict the next word. If it’s off, you do some math to figure out what weight modifications would have lead it to a better answer.
All these models do is what they were trained to do. Now they were trained to be able to predict human responses so yeah it sounds pretty human. They were trained to reproduce answers on stack overflow and Reddit etc. so they can answer those questions relatively well. And hey it is kind of cool that they can even answer some questions they weren’t trained on because it’s similar enough to the questions they weren’t trained on… but it’s not thinking. It isn’t doing anything. The program is just multiplying numbers that were previously set by an input to find the most likely next word.
This is why LLMs can’t do math. Because they don’t actually see the numbers, they don’t know what numbers are. They don’t know anything at all because they’re incapable of thought. Instead there are simply patterns in which certain numbers show up and the model gets trained on some of them but you can get it to make incredibly simple math mistakes by phrasing the math slightly differently or just by surrounding it with different words because the model was never trained for that scenario.
Models can only “know” as much as what was fed into them and hey sometimes those patterns extend, but a lot of the time they don’t. And you can’t just say “you were wrong” because the model isn’t transient (capable of changing from inputs alone). You have to train it with the correct response in mind to get it to “learn” which again takes time and really isn’t learning or intelligence at all.
Now there are some more exotic neural networks architectures that could surpass these limitations.
Currently I’m experimenting with Spiking Neural Nets which are much more capable of transfer learning and more closely model biological neurons along with other cool features like being good with temporal changes in input.
However, there are significant obstacles with these networks and not as much research because they only run well on specialized hardware (because they are meant to mimic biological neurons who run simultaneously) and you kind of have to train them slowly.
You can do some tricks to use gradient descent but doing so brings back the problems of typical ANNs (though this is still possibly useful for speeding up ANNs by converting them to SNNs and then building the neuromorphic hardware for them).
SNNs with time based learning rules (typically some form of STDP which mimics Hebbian learning as per biological neurons) are basically the only kinds of neural nets that are even remotely capable of having thoughts and learning (changing weights) in real time. Capable as in “this could have discrete time dependent waves of continuous self modifying spike patterns which could theoretically be thoughts” not as in “we can make something that thinks.”
Like these neural nets are good with sensory input and that’s about as far as we’ve gotten (hyperbole but not by that much). But these networks are still fascinating, and they do help us test theories about how the human brain works so eventually maybe we’ll make a real intelligent being with them, but that day isn’t even on the horizon currently
In conclusion, we are not remotely close to AGI. Current models that seem to think are verifiably not thinking and are incapable of it from a structural standpoint. You cannot make an actual thinking machine using the current mainstream model architectures.
The closest alternative that might be able to do this (as far as I’m aware) is relatively untested and difficult to prototype (trust me I’m trying). Furthermore the requirements of learning and thinking largely prohibit the use of gradient descent or similar algorithms meaning training must be done on a much more rigorous and time consuming basis that is not economically favorable. Ergo, we’re not even all that motivated to move towards AGI territory.
Lying to say we are close to AGI when we aren’t at all close, however, is economically favorable which is why you get headlines like this.
Programmers do not share this weakness; semicolons are common in most programming languages.
Don’t be this way. One of my most distinct memories of my dad is me telling him he was wrong about something. We looked into it together and he admitted he was wrong.
It’s a core memory because of how rare it was in my early childhood for an adult to listen to me, not get angry or dismissive, and listen to evidence despite it contradicting their intuition.