No Stupid Questions

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.

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.

AI is a super broad topic, I've heard people refer to Principal Component Analysis, which is from the 1930s, as "Machine Learning" or "AI". In reality its just that we have infrastructure and data at scale to start applying techniques in larger contexts.

I know pharmaceutical companies have been using AI in drug discovery for probably a decade now, but those models are very different from what a large language model looks like, and you still have a human sifting through the results and performing validation on a physical system to make sure the compounds do what is predicted safely, most of which do not.

When you ask something like ChatGPT a question like that, its doing something akin to looking up the most recent papers on the subject that it was trained on, and outputting something that looks like a chemical compound that the paper would be generated. It doesn't have an understanding of what that formula means, only that when you arrange letters in that way, it looks superficially similar to what would have been in the paper. Its like in movies, when they need to express someone doing math, they just fill a chalk board with random equations that look like advanced math at first glance, but might be introductory level material, or even just gibberish.

AI has helped radiologists detect 20% more cases of breast cancer during screenings, new study finds

A real AI would have explained you that with great probability a room temperature (and atmospheric pressure) superconductor is not possible.

Few experiments were successful with small grains tested under enormous pressures. But apart from that a room temperature superconductor is unlikely due to the high entropy.

The AI most regular people are using, you can think of as better at sifting the sea of data to find what you are looking for, and discussing it with you in a more understandable way.

The breakthroughs with AI are often computer programs that are told to look for an answer and change themselves somewhat randomly, but with many, many copies trying the task. The ones that fail are deleted, and the most successful change up again. This is repeated until you reliably get your result.

You wouldn't use an LLM (chat bot to appear human) for finding new materials, you'd specialize it with physical and chemical data and give it technically requirements. Something similar is done via AI for genome sequencing. AI is also used to design complex structures that are hard for humans to get their heads behind, like a new structure of a CPU. AI can also create new art, but depending on the model, similar to how humans do it, it will take more inspiration or less from already existing works. AI is not completely new, as we've used machine learning to filter stuff via pattern or finding anomalies for decades. It just never was this easy to train your own model and generate usable results.

The "breakthrough" likely happened a long time ago, but as with all tech, it was only recently accessible to the general public. LLM alone isn't even that sophisticated to begin with.

AI assistant will soon be an essential part of our lives. It will handle grocery shopping based on your dietary requirements, conduct basic diagnosis of your health, create personalized software, books, music, and movies on the fly for you, do your taxes and offer financial advices. All these are already happening.