Yea, this bubble is mostly LLM, but also deepfakes and other generative image algorithms. They are all ML. LLM has some fame because people can’t seem to realise that it’s crap. They definitely passed the Turing test, while still being pretty much useless.
There are many other useless ML algorithms. Just because you don’t like something doesn’t mean it doesn’t belong. ML has some good stuff and some bad stuff. The statement “ML works” doesn’t mean anything. It’s like saying “math works”.
There have been many AI bubbles in the past as well, as well as slumps. Look up the term AI winter. Most AI algorithms turn out not really working except for a few niche applications. You are probably referring to these few as “ML works”. Most AI projects fail, but some prevail. This goes for all tech though. So… tech works.
What Microsoft is doing is they are trying to cast a wide net to see if they hit one of the few actual good applications for LLMs. Most of them will fail but there might be one or two really successful products. Good for them to have that kind of capital to just haphazardly try new features everywhere.
No, they’re not “all ML”. ML is the whole package, not one part of the algorithm.
Obviously if you apply any tech badly it isn’t magic. ML does what it’s intended to, which is find the best model to approximate a specific phenomena. But when it’s applied correctly to an appropriately scoped problem, it does a good job.
LLMs do not do a good job at anything but telling you what language looks like, and all the investment is people trying to apply them to things they fundamentally cannot do. They are not capable of anything that resembles reasoning in any way, and that’s how the scam companies are pretending to use them.
No, they absolutely are not. You should go get your money back, because you very clearly don’t know what you’re talking about.
Machine learning is, by definition, targeting a single problem space. Using similar techniques to just shove any and all data at an algorithm and taking whatever dogshit gets spit out is categorically not the same thing.
Yea, this bubble is mostly LLM, but also deepfakes and other generative image algorithms. They are all ML. LLM has some fame because people can’t seem to realise that it’s crap. They definitely passed the Turing test, while still being pretty much useless.
There are many other useless ML algorithms. Just because you don’t like something doesn’t mean it doesn’t belong. ML has some good stuff and some bad stuff. The statement “ML works” doesn’t mean anything. It’s like saying “math works”.
There have been many AI bubbles in the past as well, as well as slumps. Look up the term AI winter. Most AI algorithms turn out not really working except for a few niche applications. You are probably referring to these few as “ML works”. Most AI projects fail, but some prevail. This goes for all tech though. So… tech works.
What Microsoft is doing is they are trying to cast a wide net to see if they hit one of the few actual good applications for LLMs. Most of them will fail but there might be one or two really successful products. Good for them to have that kind of capital to just haphazardly try new features everywhere.
No, they’re not “all ML”. ML is the whole package, not one part of the algorithm.
Obviously if you apply any tech badly it isn’t magic. ML does what it’s intended to, which is find the best model to approximate a specific phenomena. But when it’s applied correctly to an appropriately scoped problem, it does a good job.
LLMs do not do a good job at anything but telling you what language looks like, and all the investment is people trying to apply them to things they fundamentally cannot do. They are not capable of anything that resembles reasoning in any way, and that’s how the scam companies are pretending to use them.
They are all ML. I don’t know how to convince you of this so I give up. Bye. I have a Master’s degree in Machine Learning, btw.
No, they absolutely are not. You should go get your money back, because you very clearly don’t know what you’re talking about.
Machine learning is, by definition, targeting a single problem space. Using similar techniques to just shove any and all data at an algorithm and taking whatever dogshit gets spit out is categorically not the same thing.