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Trying out ChatGPT. Tried out a few topics from my field (electrical engineering) and it failed to make basic circuits. A couple queries I tried were making a CMOS inverter or a common-source amplifier, which are very simple circuits that most who have done a class could easily draw. Asked it to give the answer in SPICE syntax, because it can't draw things and SPICE is basically a code representation of a circuit. The results were poor; a MOSFET SPICE line is of the format Mxxx nd ng ns nb , chatGPT got the order of the drain/gate/source/bulk terminals wrong several times. It had some justification for how it connected the nodes of each individual device, but almost always failed to connect the outputs (drains) together for eg. an inverter. Also seemed to connect other terminals sort of at random.
FWIW these two circuits consist of 2 lines of code at minimum, 4 lines if you want something self-contained, maybe 8 if you want it to a fully functioning & simulatable netlist. So not asking for much here.
It gives lengthy canned responses explaining the circuit reminiscent of how a textbook would describe a circuit, and they sound good, but it's just wrong. Kind of reminds me of when students would throw out buzzwords in an attempt to explain something they don't know.
With some handholding (or, rather, explicit statements of how to fix the circuits) it can get closer to something functional, but usually in the process screws up something unrelated such that it's never quite right. Trying with anything even slightly more complex it falls apart pretty quickly and it's impossible to reconcile with anything approaching a functional circuit. It does much worse with analog circuits than digital circuits.
Seeing it underperform so much in my field is giving me a sort of Gellmann Amnesia effect for people touting how it can write code on its own. It certainly wrote out the circuit, and that circuit could be simulated, but it wouldn't achieve the desired behaviour of someone using it, so I'm skeptical that it can code well in other domains. That said, the field is kind of niche, and manually writing SPICE circuits slightly more so, so maybe it is just weakly trained for this subject. SPICE is also different from code in that it doesn't run sequentially, it's kind of like a hardware description language in that it's just instantiating elements that interact with eachother through simulation, so the interfaces between them aren't as simple as passing a variable to a function which does some abstracted function step-by-step. Also with how much content is out there for coding python/javascript/c# etc. it probably has a much greater wealth of resources to pull from.
I think at the moment it is essentially just stringing together user tutorials from the internet in a somewhat intelligent manner, I think anything novel or requiring critical thought will difficult for it to achieve. Maybe with some improved pattern recognition from the scraped data it will do better, I don't know.
Has anyone else tested it with things you're knowledgeable about and have any judgements of its usefulness?
Edit: it seems reasonably okay at turning explicitly stated english-language commands into bash commands. Probably well trained from stackoverflow, seems like a viable alternative to pulling information from different stackoverflow responses to do the thing you want to do. Also seems kind of helpful for asking how to do random MS Office stuff like highlighting every other cell in a column. Could be useful for simple stuff like this that is rote, common, and has good documentation but you don't usually remember off-hand, although you probably have to be extra careful when running bash scripts.
People as usual wildly overestimate this AI abilities.
Just ask chatgpt "I believe 12 cannot be divided by 4" and realize how inept it is.
Nothing has fundamentally changed, chatgpt is at the end of the day, just a dumb transformer that bruteforce contingent correlates to predict the most likely next token in a sentence.
It is an innovative but lossy way to extract info from existing datasets and as such can be seen as a competitor to scrappers.
However it has no causal understanding per se or if it has, it is messy and by accident.
Neural networks are approximate, inefficient and most importantly cannot do continual learning and are therefore the peak irony of our century, they are a local minima in the research on how to beat local minimas.
Could you expand what you mean by this? I'd think neural networks would be a local maximum.
I remember in the 90s futurists thought machine translation would replace human translators fairly soon, because the simple algorithm of looking up target language words in a hashmap was producing results so fast. BabelFish could translate "El camarero anda por la calle" in 1995. This is probably 70% as good as machine translation needed to be for many usecases. Machine translation software just needed to "tidy up" edge cases like idioms, homophones, different grammar, etc etc.
This didn't happen. Until Google started using deep learning in the 2010s, progress stalled, because the last 30% couldn't be done with hashmap lookup. Now we are in another period of rapid advancement. But this approach will probably also top out eventually.
Minimum, maximum, it doesn't matter to understand the metaphor.
A neural network through gradient descent generally want to find the global minimum of an error function and therefore maximize predictions accuracy.
It could instead search for a global maximum to the inverse of an error function or to another type of function, but the distinction is irrelevant here.
Gradient descent often fail to find the global minimum and instead because it descent/jump through derivates it can be stuck in a local minima, which simply means that it has reached a minima on a function curve and at this point, it needs to go upwards to go beyond the minima, therefore it temporarily afford to perform worse, to increase the error rate, in hope to find a new descent on the curve that will be lower than the previous minima
Not being stuck in local minima is the #1 metric to improve deep learning algorithms and while there are many optimizations towards this goal it is not computationally doable with current algorithms to have optimal learning aka reach the global minima.
So now we understand
now let's understand
They are a local minima because Neural networks are fundamentally unfit towards AGI needs.
They are just a vomit of bruteforced contingent correlates and it works surprisingly well but it is inefficient, makes poor contingent amalgamations inherently,
have no causal reasoning abilities, are stateless and cannot do continual learning AKA they can't learn new info in real time without the so called catastrophic forgetting.
For those reasons, they are by design suboptimals and therefore are a local minima in which the world is stuck, in the goal of beating local minimas.
No offence, but it's really striking to see that the rationalist diaspora people live in an alternate reality based on groundless hype and a fundamental lack of methodology, or should I dare say, lack of rationality.
We are in a winter since 2019 or since the 90s depending on what we look at.
What does the average lesswronger or redditor look at?
He looks at cool demos. Or even more than demos, cool domain specific disrutpive applications.
That is what stablediffusion and chatgpt are.
They are indeed very impressive for what they do but at the end of the day that is irrelevant towards the natural language understanding goal.
someome with methodology should instead look at the precise tasks required towards true NLU or even AGI.
POS tagging:
https://paperswithcode.com/sota/part-of-speech-tagging-on-penn-treebank
dependency parsing:
https://paperswithcode.com/sota/dependency-parsing-on-penn-treebank
coreference resolution
https://paperswithcode.com/sota/coreference-resolution-on-ontonotes
word sense disambiguation
https://paperswithcode.com/sota/word-sense-disambiguation-on-supervised
named entity recognition
https://paperswithcode.com/sota/named-entity-recognition-ner-on-conll-2003
semantic parsing
https://paperswithcode.com/sota/semantic-parsing-on-amr-english-mrp-2020
Only to name a few, all of them are needed concomitantly, and that is by far non-exhaustive.
Once you undestand that the error rate is often per word/token instead of per sentence, and that error between those tasks have dependencies and are therefore often multiplicative and you'll undestand that a 95% accuracy while it sounds impressive is in fact dogshit.
What can you see from those SOTA results?
That we have reached a plateau of extreme and increasingly diminishing returns.
Most of the gains are from 2019, the year transformers were popularized. The rest has been a bag of tricks, and unoriginal minor optimizations.
The biggest innovation while still mostly unknown/underappreciated by the researchers group think, is XLnet, from 2019 too.
There is nothing else we can do, we have maxxed out the bruteforcing of statistics amalgamations, contrary to the belief, there is almost zero progress in SOTA results and most importantly there is a fundamental shortage of innovative ideas, wether we speak of an alternative to transformers or about innovating transformers themselves, nothing potent.
While it is obvious transformers are a misdirection, despite this I can improve the state of the art in any NLP task because there are additional ineptia in the research crowd.
Firstly almost nobody is working on improving the SOTA in most tasks, e.g. coreference resolution. Just look at the number of submisions over time to realize this.
Secondly as in every research field, the researchers are highly dysfunctional, AKA they will invent many minor but interesting, universal and complementary/synergetic optimizations ideas and yet nobody will ever attempt to combine them concomitantly, despite it being trivial. That is because researchers are not meta-researchers, and because of potent NIH syndrome and other cognitive biases.
For starters, the worldwide SOTA in dependency parsing is because I asked the researcher to switch BERT for XLnet, and it worked.
I plan to outperform the SOTA in coreference resolution in 2023, that will empirically strengthen my thesis on the dysfunctionality of mankind and on artificial scarcity.
I invite you to read this complementary essay on the topic: https://www.metaculus.com/notebooks/10677/substance-is-all-you-need/
VoiceOfLogic
Is this Julius Branson?
I don't think there's any human being like me on this timeline but I would love to find a clone.
I've never read about Julius Branson https://juliusbranson.wordpress.com/blog/
What makes this person similar to me?
What makes you think I am him?
Are you the founder of the Obsidian.md startup BTW?
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