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I believe that quote actually predates the modern era of AI! Here is an article from 2013 referring to it, as being in the then-current version of their textboo

by lacker 6y ago

I believe that quote actually predates the modern era of AI! Here is an article from 2013 referring to it, as being in the then-current version of their textbook: https://www.theatlantic.com/magazine/archive/2013/11/the-man-who-would-teach-machines-to-think/309529/ https://www.theatlantic.com/magazine/archive/2013/11/the-man... Look at the other claims in this article from 2013 for comparison: Consider that computers today still have trouble recognizing a handwritten A.... In Hofstadter’s mind, there is nothing to be surprised about. To know what all A’s have in common would be, he argued in a 1982 essay, to “understand the fluid nature of mental categories.” And that, he says, is the core of human intelligence. I would say the modern era of AI was kicked off with AlexNet in 2012 and hit its stride a few years later. So, I believe this quote and this article are really referring to pre-GPU AI techniques. Basically, the predictions from this era just underestimated the value of scaling the data. Modern AI has several impressive achievements. It can certainly recognize an A. Image recognition and voice recognition are now critical parts of real products that help people in their everyday lives. At the same time, it's true that current AI techniques might not get us all the way to AGI. We'll just have to wait and see. But I think it's important to recognize that we have had real progress in the modern AI era that has seriously outperformed the pessimistic expectations from ~2010.

StrictDabbler 6y ago
Modern AI cannot recognize the kind of A that Hofstadter wrote about in that essay. Hofstadter identifies novelty typefaces where A has none of the characteristics that you might associate with the letter. Often A is not a triangle, sure, but sometimes it has no upper bar, no left stroke, no right stroke. Sometimes the bars are defined by a transition from negative to positive space. Sometimes A is made of 20 strokes, or a pattern of dots, or an open eye where the half-circular upper lid defines the triangle and the pupil is the bar. A neural network trained on conventional As would not conclude that any of these are an A. These shapes wouldn't necessarily read as A in isolation. Sometimes they only read correctly when you're looking at text using a whole typeface defined on the same principles. Modern AI still cannot recognize that kind of A even though a human easily can. There are ways to make an A that require a human to understand an idea of "letterness" that's different than anything they've seen before. I'd recommend reading the original essay. The illustrations make this clear in a way my description can't.

by lacker 6y ago

StrictDabbler 6y ago

Modern AI cannot recognize the kind of A that Hofstadter wrote about in that essay. Hofstadter identifies novelty typefaces where A has none of the characteristics that you might associate with the letter. Often A is not a triangle, sure, but sometimes it has no upper bar, no left stroke, no right stroke. Sometimes the bars are defined by a transition from negative to positive space. Sometimes A is made of 20 strokes, or a pattern of dots, or an open eye where the half-circular upper lid defines the triangle and the pupil is the bar. A neural network trained on conventional As would not conclude that any of these are an A. These shapes wouldn't necessarily read as A in isolation. Sometimes they only read correctly when you're looking at text using a whole typeface defined on the same principles. Modern AI still cannot recognize that kind of A even though a human easily can. There are ways to make an A that require a human to understand an idea of "letterness" that's different than anything they've seen before. I'd recommend reading the original essay. The illustrations make this clear in a way my description can't.