Machine Learning is such a better name. It describes what is happening - a machine is learning to do some specific thing. In this case to take text and output pictures… It’s limited by what it learned from. It learned from arrays of numbers representing colours of pixels, and from strings of text. It doesn’t know what that text means, it just knows how to translate it into arrays of numbers… There is no intelligence, only limited learning.
Isn’t meaning just comparing and contracting similarly learned patterns against each other and saying “this is not all of those other things”.?
The closer you scrutinize meaning the fuzzier it gets. Linguistically at least, though now that I think about it I suppose the same holds true in science as well.
Yes, we absolutely are different. Okay, maybe if you really boil down every little process our brains do there are similarities, we do also do pattern recognition, yes. But that isn’t all we do, or all ML systems do, either. I think you’re selling yourself short if you think you’re just recognising patterns!
The simplest difference between us and ML systems was pointed out by another commenter - they are trained on a dataset and then they remain static. We constantly re-evaluate old information, take in new information, and formulate new thoughts and change our minds.
We are able to perceive in ways that computers just can’t - they can’t understand what a smell is because they cannot smell, they can’t understand what it is to see in the way that we do because when they process images it is exactly the same to a computer as processing any other series of numbers. They do not have abstract concepts to relate recognised patterns to. Generative AI is unable to be truly creative in the way that we can, because it doesn’t have an imagination, it is replicating based on its inputs. Although, again, people on the internet love to say “that’s what artists do”, I think it’s pretty obvious that we wouldn’t have art in the way we do today if that was true… We would still be painting on the walls of caves.
Machine Learning isn’t a good name for these services because they aren’t learning. You don’t teach them by interacting with them. The developers did the teaching and the machine did the learning before you ever opened the browser window. You’re interacting with the result of learning, not with the learning.
Why does the timing of the learning matter though? It’s just a matter of saying “this machine learned to do X” rather than “this machine is currently learning from you.” Plus I’m sure there are machine learning products/projects out there that do learn on the fly to attempt to continually improve results, even if the learning might be cloud based and the local on-device neural nets are just for demonstrating the results.
I’d say “artificial student” is a subset of AI/ML.
Machine Learning is such a better name. It describes what is happening - a machine is learning to do some specific thing. In this case to take text and output pictures… It’s limited by what it learned from. It learned from arrays of numbers representing colours of pixels, and from strings of text. It doesn’t know what that text means, it just knows how to translate it into arrays of numbers… There is no intelligence, only limited learning.
Am I machine learning?
Only sometimes
sometimes worse
I thought so…
Are we so different?
Isn’t meaning just comparing and contracting similarly learned patterns against each other and saying “this is not all of those other things”.?
The closer you scrutinize meaning the fuzzier it gets. Linguistically at least, though now that I think about it I suppose the same holds true in science as well.
Yes, we absolutely are different. Okay, maybe if you really boil down every little process our brains do there are similarities, we do also do pattern recognition, yes. But that isn’t all we do, or all ML systems do, either. I think you’re selling yourself short if you think you’re just recognising patterns!
The simplest difference between us and ML systems was pointed out by another commenter - they are trained on a dataset and then they remain static. We constantly re-evaluate old information, take in new information, and formulate new thoughts and change our minds.
We are able to perceive in ways that computers just can’t - they can’t understand what a smell is because they cannot smell, they can’t understand what it is to see in the way that we do because when they process images it is exactly the same to a computer as processing any other series of numbers. They do not have abstract concepts to relate recognised patterns to. Generative AI is unable to be truly creative in the way that we can, because it doesn’t have an imagination, it is replicating based on its inputs. Although, again, people on the internet love to say “that’s what artists do”, I think it’s pretty obvious that we wouldn’t have art in the way we do today if that was true… We would still be painting on the walls of caves.
Machine Learning isn’t a good name for these services because they aren’t learning. You don’t teach them by interacting with them. The developers did the teaching and the machine did the learning before you ever opened the browser window. You’re interacting with the result of learning, not with the learning.
Learnéd Machines
Why does the timing of the learning matter though? It’s just a matter of saying “this machine learned to do X” rather than “this machine is currently learning from you.” Plus I’m sure there are machine learning products/projects out there that do learn on the fly to attempt to continually improve results, even if the learning might be cloud based and the local on-device neural nets are just for demonstrating the results.
I’d say “artificial student” is a subset of AI/ML.