I’ve gone down a rabbit hole here.
I’ve been looking at lk99, the potential room temp superconductor, lately. Then I came across an AI chat and decided to test it. I then asked it to propose a room temp superconductor and it suggested (NdBaCaCuO)_7(SrCuO_2)_2 and a means of production which got me thinking. It’s just a system for looking at patterns and answering the question. I’m not saying this has made anything new, but it seems to me eventually a chat AI would be able to suggest a new material fairly easily.
Has AI actually discovered or invented anything outside of it’s own computer industry and how close are we to it doing stuff humans haven’t done before?
It’s important to be clear what kind of actual system you’re using when you say “AI”.
If you’re talking about something like ChatGPT, you’re using an LLM, or “Large Language Model”. Its goal is to produce something that reasonably looks like a human wrote it. It has reviewed a ridiculous amount of human text, and has a metric assload of weights associating the relationships between these words.
If the LLM sees your question and associates a particular compound with superconductors, it’s because it’s seen these things related in other writings (directly or indirectly) or at least sees the relationship as plausible.
It’s important not to ascribe more intent behind what your seeing than exists. It can’t understand what a superconductor is or how materials can achieve the state, it’s just really good at relaying related words in a convincing manner
That’s not to say it isn’t cool or useful, or that ML(Machine Learning) can’t be used to help find answers to these kinds of questions.
Exactly. It’s just text prediction software that is really good at making itself sound plausible. It could tell you something completely false and have no idea it’s stating a lie. There’s no intelligence here. It’s a very precise word guesser. Which is great for specific settings. But there’s a huge amount of hype associated with this tool and it’s very much by design (by tech companies).
If the LLM sees your question and associates a particular compound with superconductors, it’s because it’s seen these things related in other writings (directly or indirectly).
I’m not convinced of this. LLMs haven’t been just spitting out prior art, despite what some people seem to suggest. It’s not just auto-complete, that’s just a useful analogy.
For instance, I’m fascinated by the study that got GPT4 to draw a unicorn using LaTeX. It wasn’t great, but it was recognizable to us as a unicorn. And apparently that’s gotten better with iterations. GPT (presumably) has no idea what a unicorn looks like, except through text descriptions. Who knows how it goes from written descriptions of a mythical being to a 2d drawing with a markup language without being trained on images, imagery, or any concept of what things look like.
It’s important not to ascribe more intent behind what your seeing than exists.
But also, this is true as well. I’m trying hard not to anthropomorphize this LLM but it sure seems like there’s some emergent effect that kind of looks like an intelligence to a layman like myself.
To be clear, I’m not trying to make the argument that it can only produce exactly what it’s seen, I recognize that this argument is frankly overstated in media. (The interviews with Adam Conover are great examples; he’s not wrong per se, but he does oversimplify things to the point that I think a lot of people misunderstand what’s being discussed)
The ability to recombine what it’s seen in different ways as an emergent property is interesting and provocative, but isn’t really what OP is asking about.
A better example of how LLMs can be useful in research like what OP described would be asking it to coalesce information from multiple existing studies about what properties correlate with superconducting in order to help accelerate research in collaboration with actual material scientists. This is all research that could be done without LLMs, or even without ML, but having a general way to parse and filter these kinds of documents is still incredibly powerful, and will be a sort of force multiplication for these researchers going forward.
My favorite example of the limitation on LLM’s is to ask it to coin a new word, then google that word. It physically is unable to produce a combination of letters that it doesn’t have indexed, and it doesn’t have an index for words it hasn’t seen. It might be able to create a new meaning for a word that it’s seen, but that isn’t necessarily the same.
i believe google created something to do with protein folding or something using deepmind
idk tho
Define AI.
Machine learning, image processing and similar “AI” techniques have been used for decades in sciences. When a telescope does large field survey to detect an transient phenomena like a supernova, you don’t have an astrophysicist looking at the photo, a smart astrophysicist (well several ones) used image processing and machine learning to teach the computer that there is something interesting on the image and send an automated message to every telescope about : Something is happening at this position so they can watch it immediately. Is it AI ?
What about the LHC who takes so much data that they need very advanced algorithm to store them and process them, is that AI ? What about protein folding which is a very complex problem relying on machine learning to find the proper solution
The big and recent breakthrough is that it’s now accessible on an ordinary computer and that the training of large model is cheap enough to use it for less complicated topics
Yes, AI has and is being used extensively for research already, particularly in problem spaces where pattern matching can yield particularly powerful results for solution searches, which is actually a lot of problem spaces. Protein structures are probably the best example.
The AIs we have at our disposal can’t invent a thing - yet - because they aren’t true AIs - again: yet.
They are merely, and should be perceived as tools, nothing more. It’s the people who use them that may apply them to tasks that will result in invention, but on their own, they are closer to the Chinese Room principle, than to thinking and inventive constructions.
I agree with the basic idea, but there’s not some fundamental distinction between what we have now and true AI. Maybe we’ll find breakthroughs that help, but the systems we’re using now would work given enough computing power and training. There’s nothing the human brain can do that they can’t, so with enough resources they can imitate the human brain.
Making one smarter than a human wouldn’t be completely trivial, but I doubt it would be all that difficult given that the AI is powerful enough to imitate something smarter than a human.
I agree with the basic idea, but there’s not some fundamental distinction between what we have now and true AI.
Are AIs we have at our disposal able and allowed to self-improve on their own? As in: can they modify their own internal procedures and possibly reshape their own code to better themselves, thus becoming more than their creators predicted them to be?
There’s nothing the human brain can do that they can’t, so with enough resources they can imitate the human brain.
Human brain can:
- interfere with any of its “hardware” and break it
- go insane
- preocupy itself with absolutely pointless stuff
- create for the sake of creation itself
- develop and upkeep illusions it will begin to trust to be real
- choose ad act against undeniable proof given to it
These are of course tongue-in-cheek examples of what a human brain can, but - from the persepctive of neuroscience, psychology and a few adjacent fields of study - it is absolutely incorrect to say that AIs can do what a human brain can, because we’re still not sure how our brains work, and what they are capable of.
Based on some dramatic articles we see in news that promise us “trauma erasing pills”, or “new breakthrough in healing Alzheimer” we may tend to believe that we know what this funny blob in our heads is capable of, and that we have but a few small secrets to uncover, but the fact is, that we can’t even be sure just how much is there to discover.
Are AIs we have at our disposal able and allowed to self-improve on their own?
Yes. That’s what training is. There’s systems for having them write their own training data. And ultimately, an AI that’s good enough at copying a human can write any text that human can. Humans can improve AI by writing code. So can an AI. Humans can improve AI by designing new microchips. So can an AI.
These are of course tongue-in-cheek examples of what a human brain can, but - from the persepctive of neuroscience, psychology and a few adjacent fields of study - it is absolutely incorrect to say that AIs can do what a human brain can, because we’re still not sure how our brains work, and what they are capable of.
We know they follow the laws of physics, which are turing complete. And we have pretty good reason to believe that their calculations aren’t reliant on quantum physics.
Individual neurons are complicated, but there’s no reason to believe they exact way they’re complicated matters. They’re complicated because they have to be self-replicating and self-repairing.
