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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.
AI has now understanding. It is like letting a person who grew up in a village in the amazon observe a nucit lear power plant operator push buttons and then giving the villagers the controls. They might be able to mimic the behaviour but there is no understanding. AI doesn't understand and it doesn't reason, Just guessing the next chess move by observing what elite players have played is one thing. Interacting with the real world without any actual understanding will never work. There is a reason why dentists learn chemistry, engineers learn math and why education has hands on labs. Without real world experience and intuition one can never become proficient.
Actual understanding and context are AI problems that haven't really progressed at all and until they do AI is going to be stuck in the realm of tools used in specific circumstances.
Username checks out :)
The death of GOFAI is a tragedy, however humans also mostly learn by mimetism however they build a model of reality based on mimetics insights and that, a neural network cannot reliably.
However, while I still believe chatgpt is a data illusion, for the first time in my life I fail to explain the illusion as chatgpt is able to do things reliably that goes far beyong an ability to flexibly scrap existing datasets.
The other tragedy is that neural networks based on precise emulation of the architecture of an animal brain are completely non-funded and conversely the funding on retro-engineering of simple animal brains is of close to zero. We are very close to a full observability and mapping of the c-elegans brain, however nobodys working on the remaining gaps (e.g. GABA neurons). As I have disocovered in my life, almost all key blockers to scientific disruption share a similar issue: nobody's working on them. Nobody's funding them.
Hence when people forecast AGI progress on metaculus, they systematically fail to understand that the forecast is not a number of pending years but the infinity of time.
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But that's the thing, it isn't capable of accomplishing all sorts of goals as seen in this thread, because it lacks understanding. It will need that understanding to ever get to a point where it becomes an X-risk.
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This is an appeal to ignorance.
Understanding something is having a causal model of it.
It allows to analyze such system and reliably predicts it and its consequences.
A system with a reliable understanding should be able to output argumentative text/syllogisms showing said understanding, free of logical fallacies and with source to the truth values of the premises.
To mysticize what understanding is really shows once again the truism that epistemology should be taught in schools.
At the end of the day, the result is what matter indeed, but without understanding a system is non-reliable and cannot be trusted for many serious needs.
That is a geat comment, I will answer it properly when I get the time.
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Observe the nuclear power plant operator long enough and you can plausibly gain enough understanding to run the power plant while never figuring out what fission is.
No, I don't think so, there are likely way too many edge cases that all require genuine understanding to solve.
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Until you run in a situation you never encountered before as the world is highly variadic and then the system pathetically fail.
Yes, which is a problem solved by more training data.
That is not a solid solution to any dataset changing in real time. More data can only do so much, as a metaphor, see the limits of AOT versus JIT for compilers.
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