A papyrus scroll that was burned and carbonized when Mount Vesuvius erupted almost 2,000 years ago has been virtually unrolled and partially deciphered with the help of artificial intelligence.
Well, apparently journalists here are doing a good job and actually understand that generative AI and machine learning models pertain to the same family of techniques.
I wouldn’t call that “doing a good job”. If they were doing a good job, they would disambiguate Machine Learning from Generative AI. I don’t know that they’re doing this intentionally, but i would definitely appreciate that distinction being made.
I work in the field of machine learning as a researcher. There is no distinction. Machine learning is a set of techniques, Artificial Intelligence is a synonym which became popular in recent years.
Generative AI only defines how a model is used, not what the model is. You might as well use a kNN model to do generative AI. Generative AI is machine learning. More specifically it is an application of supervised machine learning.
Some people have started distinguishing machine learning to identify classical models and AI to definite stuff that uses deep learning.
If you want to be specific you may use the name of the specific model, but that’s a bit too much for an article title. Otherwise, the term AI is quite appropriate in this context and far more recognized than ML.
And me myself never say AI, I don’t like too much that it ingrains some feeling of intelligence; but for these articles i feel it is appropriate.
As someone else who works in ML but as a user for research purposes, I agree that there is no technical distinction but we’re talking about public communications here. You and I both know that AI and ML are the same thing. Most people do not. If I talked with my mom about AI she would assume I mean generative AI. She wouldn’t understand the nuance in your comment.
In fact, even as a researcher who uses both simple ML techniques and generative AI, I tend to make the assumption that when someone mentions AI they mean generative AI.
Common language changes based on the common understanding of society. The fact that the technical terms mean something different from the common understanding in society means that we need to be even more careful with the terms that we use, especially with something as polarizing as generative AI.
We have a different opinion here. I don’t think general public has an understanding of the difference between the two things. It may be useful to teach people about it, but I’m not sure how much it would help.
People understand generative AI when they hear AI because it’s been the only thing they’ve been in direct contact with. Despite them using ML every day in different aspects of their life this is not something they get to know about. LLMs are different since they directly use the model.
I feel it is good people are getting to know ML and the fact that it is a useful technology; the fact that currently it is associated with LLMs is a side effect. It probably would be better if it wasn’t that way, but I do not see it as a big problem.
On the fact that it is polarizing, sure that is currently the case; probably it won’t be so in 10 years. That is nothing compared to the past 70 years in which ML has been used and applied in several fields with great success without anyone knowing it even existed.
Yeah but now the meaning of the word AI has changed in most people’s minds to mean generative ai and mostly LLMs.
Well, apparently journalists here are doing a good job and actually understand that generative AI and machine learning models pertain to the same family of techniques.
I wouldn’t call that “doing a good job”. If they were doing a good job, they would disambiguate Machine Learning from Generative AI. I don’t know that they’re doing this intentionally, but i would definitely appreciate that distinction being made.
I work in the field of machine learning as a researcher. There is no distinction. Machine learning is a set of techniques, Artificial Intelligence is a synonym which became popular in recent years.
Generative AI only defines how a model is used, not what the model is. You might as well use a kNN model to do generative AI. Generative AI is machine learning. More specifically it is an application of supervised machine learning.
Some people have started distinguishing machine learning to identify classical models and AI to definite stuff that uses deep learning.
If you want to be specific you may use the name of the specific model, but that’s a bit too much for an article title. Otherwise, the term AI is quite appropriate in this context and far more recognized than ML.
And me myself never say AI, I don’t like too much that it ingrains some feeling of intelligence; but for these articles i feel it is appropriate.
As someone else who works in ML but as a user for research purposes, I agree that there is no technical distinction but we’re talking about public communications here. You and I both know that AI and ML are the same thing. Most people do not. If I talked with my mom about AI she would assume I mean generative AI. She wouldn’t understand the nuance in your comment.
In fact, even as a researcher who uses both simple ML techniques and generative AI, I tend to make the assumption that when someone mentions AI they mean generative AI.
Common language changes based on the common understanding of society. The fact that the technical terms mean something different from the common understanding in society means that we need to be even more careful with the terms that we use, especially with something as polarizing as generative AI.
We have a different opinion here. I don’t think general public has an understanding of the difference between the two things. It may be useful to teach people about it, but I’m not sure how much it would help. People understand generative AI when they hear AI because it’s been the only thing they’ve been in direct contact with. Despite them using ML every day in different aspects of their life this is not something they get to know about. LLMs are different since they directly use the model.
I feel it is good people are getting to know ML and the fact that it is a useful technology; the fact that currently it is associated with LLMs is a side effect. It probably would be better if it wasn’t that way, but I do not see it as a big problem.
On the fact that it is polarizing, sure that is currently the case; probably it won’t be so in 10 years. That is nothing compared to the past 70 years in which ML has been used and applied in several fields with great success without anyone knowing it even existed.