Yes, and this is how computer "scientists" think of problems -- but this isnt science, it's mathematics.
If you have a process, eg., points = sample(circle) which fully describes its target as n->inf (ie., points = circle as n->inf) you arent engaged in statistical infernece. You might be using some of the same formula, but the whole system of science and statistics has been created for a radically different problem with radically different semantics to everything you're doing.
eg., the height of mercury in a thermometer never becomes the liquid being measured.. it might seems insane/weird/obvious to mention this... but we literally have berkelian-style neoidealists in AI research who don't realise this...
Who think that because you can find representations of abstracta in other spaces they can be projected in.. that this therefore tells you anything at all about inference problems. As if it was the neural network algorithm itself (a series of multiplications and additions) that "revealed the truth" in all data given to it. This, of course, is pseudoscience.
It only applies on mathematical problems, for obvious reasons. If you use a function approximation alg to approximate a function, do not be suprised you can succeed. The issue is that the relationship between, say, the state of a theremometer and the state of the temperature of it's target system is not an abstract function which lives in the space of temperature readings.
More precisely, in the space of temperature readings the actual causal relationship between the height of the mecurary and the temperature of the target shows up as an infinite number of temperature distributions (with any given trained NN learning only one of these). None of which are a law of nature -- laws of nature are not given by distributions in measuring devices.
If you have a process, eg., points = sample(circle) which fully describes its target as n->inf (ie., points = circle as n->inf) you arent engaged in statistical infernece. You might be using some of the same formula, but the whole system of science and statistics has been created for a radically different problem with radically different semantics to everything you're doing.
eg., the height of mercury in a thermometer never becomes the liquid being measured.. it might seems insane/weird/obvious to mention this... but we literally have berkelian-style neoidealists in AI research who don't realise this...
Who think that because you can find representations of abstracta in other spaces they can be projected in.. that this therefore tells you anything at all about inference problems. As if it was the neural network algorithm itself (a series of multiplications and additions) that "revealed the truth" in all data given to it. This, of course, is pseudoscience.
It only applies on mathematical problems, for obvious reasons. If you use a function approximation alg to approximate a function, do not be suprised you can succeed. The issue is that the relationship between, say, the state of a theremometer and the state of the temperature of it's target system is not an abstract function which lives in the space of temperature readings.
More precisely, in the space of temperature readings the actual causal relationship between the height of the mecurary and the temperature of the target shows up as an infinite number of temperature distributions (with any given trained NN learning only one of these). None of which are a law of nature -- laws of nature are not given by distributions in measuring devices.