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We are still inferring a best fit based on available data, so “statistical modelling” is way more descriptive of what we are doing the “intelligence”. That does

We are still inferring a best fit based on available data, so “statistical modelling” is way more descriptive of what we are doing the “intelligence”. That doesn’t mean it isn’t impressive. These non-classical statistical inference are going beyond what we have been able to do with classic statistics, it is indeed impressive. Personally I only grow skeptical when claims are made about some vague Artificial General Intelligence or some other magic which no amount of inference is gonna give us.

machiaweliczny 5y ago
I think diff inference might give us that. Basically kinda like dreaming(generating) other samples and checking how they are different. This will help understand how thing is defined but maybe it works for all kind of things. Obviously it's tough to find such algorithm that works for all types of data/embeddings.
killerstorm 5y ago
Given that NN components are demonstrated to have universal computation properties, it is more like deriving a program from training data. Technically a program here is a 'curve' but it's insanely complex one. Do you consider programming math? Since technically you write a function which deals with 0s and 1s.
- runarberg 5y ago
  There is no need to reduce programing to math (even though you can technically do so) just as there is no need to reduce ‘talking’ to math just because speech can be represented with numbers. In fact you can represent most things (probably all things) with numbers. I do think it is interesting philosophically how much powerful machine learning algorithms are over the traditional linear models. And perhaps it is a good description to say that “best fit program” is inferred. However these algorithms are still limited to inference. And—while useful—calling it intelligent is not very accurate—at least in the way most people use the word—no matter how complected of a construct they are inferring.

machiaweliczny 5y ago

I think diff inference might give us that. Basically kinda like dreaming(generating) other samples and checking how they are different. This will help understand how thing is defined but maybe it works for all kind of things. Obviously it's tough to find such algorithm that works for all types of data/embeddings.

killerstorm 5y ago

Given that NN components are demonstrated to have universal computation properties, it is more like deriving a program from training data. Technically a program here is a 'curve' but it's insanely complex one. Do you consider programming math? Since technically you write a function which deals with 0s and 1s.

runarberg 5y ago
There is no need to reduce programing to math (even though you can technically do so) just as there is no need to reduce ‘talking’ to math just because speech can be represented with numbers. In fact you can represent most things (probably all things) with numbers. I do think it is interesting philosophically how much powerful machine learning algorithms are over the traditional linear models. And perhaps it is a good description to say that “best fit program” is inferred. However these algorithms are still limited to inference. And—while useful—calling it intelligent is not very accurate—at least in the way most people use the word—no matter how complected of a construct they are inferring.