Researchers used AI to design a new material that they used to build a working battery – it requires up to 70 percent less lithium than some competing designs.
That’s not true at all. AI can in fact generate novel techniques and solutions and has already done so in biotech and electrical engineering. I don’t think you understand how AI works or what it is
I think maybe people are running into a misunderstanding between LLMs and neural nets or machine kearning in general? AI has become too big of an umbrella term. We’ve been using NNs for a while now to produce entirely new ways to go about things. They can find bugs in games that humans can’t, been used to design new wind turbine blades (even made several asymmetrical ones which humans just don’t really do), or plot out entirely new ways of locomotion when given physical bodies. Machine learning is fascinating and can produce very unique results partly because it can be set up to not have existing design biases like humans do
And the nature of computers is that they are magnitudes better than humans at brute forcing. Machine learning can brute force (depending on the technique, it can be smarter than brute forcing, being more efficient) test many many many more designs and techniques than we could manually do. Sure it’ll fail many times, but it’s just a numbers game, and it can pump those numbers. It’ll try a lot of weird and unique stuff we wouldn’t even think to try, with varying degrees of success.
Name one that wasn’t just doing the thing it was told and the users being surprised. You know, the same way that people are surprised when research has results they did not expect using other approaches.
It’s a weird way of asking this. Of course it’s going to do what’s told, the alternative is that it, out of the blue, spits a battery design for no reason. If it were to somehow find a way to make batteries with less lithium in a way that never did before, isn’t that an unexpected result using other approaches?
This is not general artificial intelligence, everything we have is narrow AI, focused on solving one specific problem, for identifying birds to understand instructions between drugs.
That’s not true at all. AI can in fact generate novel techniques and solutions and has already done so in biotech and electrical engineering. I don’t think you understand how AI works or what it is
I think maybe people are running into a misunderstanding between LLMs and neural nets or machine kearning in general? AI has become too big of an umbrella term. We’ve been using NNs for a while now to produce entirely new ways to go about things. They can find bugs in games that humans can’t, been used to design new wind turbine blades (even made several asymmetrical ones which humans just don’t really do), or plot out entirely new ways of locomotion when given physical bodies. Machine learning is fascinating and can produce very unique results partly because it can be set up to not have existing design biases like humans do
And the nature of computers is that they are magnitudes better than humans at brute forcing. Machine learning can brute force (depending on the technique, it can be smarter than brute forcing, being more efficient) test many many many more designs and techniques than we could manually do. Sure it’ll fail many times, but it’s just a numbers game, and it can pump those numbers. It’ll try a lot of weird and unique stuff we wouldn’t even think to try, with varying degrees of success.
Name one that wasn’t just doing the thing it was told and the users being surprised. You know, the same way that people are surprised when research has results they did not expect using other approaches.
It’s a weird way of asking this. Of course it’s going to do what’s told, the alternative is that it, out of the blue, spits a battery design for no reason. If it were to somehow find a way to make batteries with less lithium in a way that never did before, isn’t that an unexpected result using other approaches?
This is not general artificial intelligence, everything we have is narrow AI, focused on solving one specific problem, for identifying birds to understand instructions between drugs.
Yeah, that would be coming up with a battery design.