3 ms·

Hmm. Maybe you're right. But let me try to push back. If we're trying to draw human-consumable wisdom about the state of the world from data, simply capturing

Hmm. Maybe you're right. But let me try to push back. If we're trying to draw human-consumable wisdom about the state of the world from data, simply capturing a snapshot and then applying some basic analysis is one thing. Creating self-improving mechanisms for doing this is another, even if the latter use statistics in the process. Perhaps in a similar way that Statistics uses Algebra yet is distinct in its description and capabilities. I'm not convinced you're wrong here, just trying to talk through it.

rob-p 10y ago
I think that one of the problems I have with the description in the post is that it draws a line where, in reality, no real line (or at the very least, only a blurry mess) exists. For example, where do hierarchical models and Bayesian nonparametric models fit? Where does model selection fit? These notions have existed in statistics for some time. I'm not one of these particularly dogmatic people who believe that "it's all just statistics" or, conversely "machine learning is entirely new / different". In fact, I think it's the deep (and continuing) connection between these fields that make them both so interesting and powerful. However, I do tend to agree that the type of hype used in this post massively oversells ML while simultaneously underselling Stats, based, partly, on the false dichotomy drawn between them.
deleted 10y ago
[deleted]
wodenokoto 10y ago
What the parent is saying is that machine learning is a buzzword for statistics or at best another word for applied statistics. Saying that applied physics will be the new physics because it changes the real world, is nonsensical. In earlier days Google called their translation algorithms statistical, then they changed it to ML and now AI has come in favour again, so that is the word being used.

3 ms·

Hmm. Maybe you're right. But let me try to push back. If we're trying to draw human-consumable wisdom about the state of the world from data, simply capturing

by danielrm26 10y ago

rob-p 10y ago
I think that one of the problems I have with the description in the post is that it draws a line where, in reality, no real line (or at the very least, only a blurry mess) exists. For example, where do hierarchical models and Bayesian nonparametric models fit? Where does model selection fit? These notions have existed in statistics for some time. I'm not one of these particularly dogmatic people who believe that "it's all just statistics" or, conversely "machine learning is entirely new / different". In fact, I think it's the deep (and continuing) connection between these fields that make them both so interesting and powerful. However, I do tend to agree that the type of hype used in this post massively oversells ML while simultaneously underselling Stats, based, partly, on the false dichotomy drawn between them.
deleted 10y ago
[deleted]
wodenokoto 10y ago
What the parent is saying is that machine learning is a buzzword for statistics or at best another word for applied statistics. Saying that applied physics will be the new physics because it changes the real world, is nonsensical. In earlier days Google called their translation algorithms statistical, then they changed it to ML and now AI has come in favour again, so that is the word being used.