Is artificial intelligence, rightly considered, an empirical science? If not, what is it?
Most of the empirical sciences you mentioned involve understanding things that already exist, rather than bringing new things into existence.
This sounds like a disguised query. What are you really asking about AI? Separate the definition from the implications; what’s an “empirical science”, what further properties does the definition imply about entities it describes, and does AI need those properties to accomplish its goal?
(I’d describe it more as a research program that draws from several empirical sciences (cognitive science, etc.) and sometimes motivates advances in those fields, in the same way that nuclear physics is an empirical science, while the projects to create nuclear weaponry were not empirical sciences in themselves but were research goals drawing heavily from scientific knowledge.)
Why can’t AI researchers formulate and test theories the way high-energy physicists do?
Within limited domains, they do. Face recognition, for instance. You are correct that it’s not a solved problem, but any new theory of face recognition, or in general any proposed computational model of the visual cortex, can be trivially tested: have it look through pictures and recognize faces, and compare it to how well untrained humans can recognize faces.
Also, reiterating a comment I made a couple days ago on a similar statement of yours:
I am not, of course, against mathematics per se. But the reason math is used in physics is because it describes reality. All too often in AI and computer vision, math seems to be used because it’s impressive.
I’d find it much more impressive if you could do anything useful in AI or computer vision without math.
You seem to be jumping from “AI isn’t a hard science like physics because we don’t know exactly what we’re trying to mathematically model” (since it studies minds-in-general rather than just the minds we can directly do experiments on) to “math isn’t useful in AI or computer vision”. Maybe computer vision can benefit from Riemannian manifolds, maybe it can’t, maybe it’s a privileged hypothesis that we shouldn’t even be bothering to ask about, but do you really expect that any technical solution will be non-mathematical?
You are correct that it’s not a solved problem, but any new theory of face recognition, or in general any proposed computational model of the visual cortex, can be trivially tested: have it look through pictures and recognize faces, and compare it to how well untrained humans can recognize faces.
Isn’t this rather like building a few cars, comparing how well they run, and calling it physics?
but any new theory of face recognition … can be trivially tested: have it look through pictures and recognize faces
Okay, but my suggestion is that this mode of empirical verification just isn’t good enough. People have been using this obvious method for decades, and we still can’t solve face detection (face recognition is presumably much harder than mere detection). This implies we need a non-obvious method of empirical evaluation.
but do you really expect that any technical solution will be non-mathematical?
No, but I do believe that over-mathematization is a real problem.
Okay, but my suggestion is that this mode of empirical verification just isn’t good enough. People have been using this obvious method for decades, and we still can’t solve face detection (face recognition is presumably much harder than mere detection). This implies we need a non-obvious method of empirical evaluation.
Okay, but non-”whatever scientists are currently doing” is not an epistemology! You need to say what specific alternate you believe would be better, and you haven’t done so. Instead, you’ve just made broad, sweeping generalizations about how foolish most scientists are, while constantly delaying the revelation of the superior method you think they should be using.
Please, please, just get to the point.
As for facial recognition, the error was in believing that it should be simple to explain what we’re doing to a blank slate. Our evolutionary history includes billions of years of friend-or-foe, kin-or-nonkin identification, which means the algorithm that analyzes images for faces will be labyrinthine and involve a bunch of hammered-together pieces. But I don’t see anything you’ve proposed that would lead to a faster solution of this problem; just casual dismissals that everyone’s doing it wrong because they use fancy math that just can’t be right.
FWIW, the best way to reverse-engineer a kind of cognition is to see what it gets wrong so you know what heuristics it’s using. For facial recognition, that means optical illusions regarding faces. For example, look at this.
The bottom two images are the same, but flipped. Yet one face looks thinner than the other. There’s a clue worth looking at.
The top two images are upside-down pictures of Margaret Thatcher that don’t seem all that different, but when you flip it over to see them right-side up on your photo-viewing tool (and consider this your WARNING), one looks hideously deformed. There’s another clue to look at.
Now, how would I have known to do that from the new, great epistemology you’re proposing?
How right you are about the special nature of face recognition. Another clue is the difficulty of describing a face in words so that another person can picture it. We use aspects of the face that do not register in consciousness to make the identification. Still another clue is that false faces ‘pop out’ in our vision. Cracks in pavement, clouds in the sky, anything vaguely like a face can pop out as a face. And again, faces are the first things that babies take an interest in. Their eyes will fasten on a plate with a simple happy face (two dots and a line) drawn on it. It is certainly not ordinary every day vision.
I’m asking because neither of the upside-down face illusions seem to “work” for me. (I was immediately startled by the deformed version of Margaret Thatcher.)
I’m asking because neither of the upside-down face illusions seem to “work” for me. (I was immediately startled by the deformed version of Margaret Thatcher.)
So the deformed Thatcher picture wasn’t any more startling upon turning the image over? Well, you may have some unusual aspects to your visual cognition. Try it with some more faces and see if the same thing comes up.
Okay, but my suggestion is that this mode of empirical verification just isn’t good enough. People have been using this obvious method for decades, and we still can’t solve face detection (face recognition is presumably much harder than mere detection). This implies we need a non-obvious method of empirical evaluation.
If we’re talking about “empirical verification”/”empirical evaluation”, then we’re talking about how to check if a possible solution is correct, not about how to come up with possible solutions. This method of verification keeps coming up negative because we haven’t solved face detection, not the other way around.
So if we’re doing something wrong, it’s in the way we’re trying to formulate answers, not in the way we’re trying to check them. And even there, saying we need more “non-obvious” thinking has the same problem as saying that something needs complexity: it’s not an inherently desirable quality on its own, and it doesn’t tell us anything new about how to do it (presumably people already tried the obvious answers, so all the current attempts will be non-obvious anyway… hence Riemannian manifolds). If you have some specific better method that happens to be non-obvious, then just say what it is.
Most of the empirical sciences you mentioned involve understanding things that already exist, rather than bringing new things into existence.
This sounds like a disguised query. What are you really asking about AI? Separate the definition from the implications; what’s an “empirical science”, what further properties does the definition imply about entities it describes, and does AI need those properties to accomplish its goal?
(I’d describe it more as a research program that draws from several empirical sciences (cognitive science, etc.) and sometimes motivates advances in those fields, in the same way that nuclear physics is an empirical science, while the projects to create nuclear weaponry were not empirical sciences in themselves but were research goals drawing heavily from scientific knowledge.)
Within limited domains, they do. Face recognition, for instance. You are correct that it’s not a solved problem, but any new theory of face recognition, or in general any proposed computational model of the visual cortex, can be trivially tested: have it look through pictures and recognize faces, and compare it to how well untrained humans can recognize faces.
Also, reiterating a comment I made a couple days ago on a similar statement of yours:
You seem to be jumping from “AI isn’t a hard science like physics because we don’t know exactly what we’re trying to mathematically model” (since it studies minds-in-general rather than just the minds we can directly do experiments on) to “math isn’t useful in AI or computer vision”. Maybe computer vision can benefit from Riemannian manifolds, maybe it can’t, maybe it’s a privileged hypothesis that we shouldn’t even be bothering to ask about, but do you really expect that any technical solution will be non-mathematical?
Isn’t this rather like building a few cars, comparing how well they run, and calling it physics?
Okay, but my suggestion is that this mode of empirical verification just isn’t good enough. People have been using this obvious method for decades, and we still can’t solve face detection (face recognition is presumably much harder than mere detection). This implies we need a non-obvious method of empirical evaluation.
No, but I do believe that over-mathematization is a real problem.
Okay, but non-”whatever scientists are currently doing” is not an epistemology! You need to say what specific alternate you believe would be better, and you haven’t done so. Instead, you’ve just made broad, sweeping generalizations about how foolish most scientists are, while constantly delaying the revelation of the superior method you think they should be using.
Please, please, just get to the point.
As for facial recognition, the error was in believing that it should be simple to explain what we’re doing to a blank slate. Our evolutionary history includes billions of years of friend-or-foe, kin-or-nonkin identification, which means the algorithm that analyzes images for faces will be labyrinthine and involve a bunch of hammered-together pieces. But I don’t see anything you’ve proposed that would lead to a faster solution of this problem; just casual dismissals that everyone’s doing it wrong because they use fancy math that just can’t be right.
FWIW, the best way to reverse-engineer a kind of cognition is to see what it gets wrong so you know what heuristics it’s using. For facial recognition, that means optical illusions regarding faces. For example, look at this.
The bottom two images are the same, but flipped. Yet one face looks thinner than the other. There’s a clue worth looking at.
The top two images are upside-down pictures of Margaret Thatcher that don’t seem all that different, but when you flip it over to see them right-side up on your photo-viewing tool (and consider this your WARNING), one looks hideously deformed. There’s another clue to look at.
Now, how would I have known to do that from the new, great epistemology you’re proposing?
How right you are about the special nature of face recognition. Another clue is the difficulty of describing a face in words so that another person can picture it. We use aspects of the face that do not register in consciousness to make the identification. Still another clue is that false faces ‘pop out’ in our vision. Cracks in pavement, clouds in the sky, anything vaguely like a face can pop out as a face. And again, faces are the first things that babies take an interest in. Their eyes will fasten on a plate with a simple happy face (two dots and a line) drawn on it. It is certainly not ordinary every day vision.
Can you easily read upside down?
I’m asking because neither of the upside-down face illusions seem to “work” for me. (I was immediately startled by the deformed version of Margaret Thatcher.)
Yes, pretty easily (just checked).
So the deformed Thatcher picture wasn’t any more startling upon turning the image over? Well, you may have some unusual aspects to your visual cognition. Try it with some more faces and see if the same thing comes up.
Another possibility would be to look at visual recognition systems in other species.
If we’re talking about “empirical verification”/”empirical evaluation”, then we’re talking about how to check if a possible solution is correct, not about how to come up with possible solutions. This method of verification keeps coming up negative because we haven’t solved face detection, not the other way around.
So if we’re doing something wrong, it’s in the way we’re trying to formulate answers, not in the way we’re trying to check them. And even there, saying we need more “non-obvious” thinking has the same problem as saying that something needs complexity: it’s not an inherently desirable quality on its own, and it doesn’t tell us anything new about how to do it (presumably people already tried the obvious answers, so all the current attempts will be non-obvious anyway… hence Riemannian manifolds). If you have some specific better method that happens to be non-obvious, then just say what it is.