Nov 24, 2014 02:37 AM
http://edge.org/conversation/the-myth-of-ai
EXCERPT: [...] In the history of organized religion, it's often been the case that people have been disempowered precisely to serve what was perceived to be the needs of some deity or another, where in fact what they were doing was supporting an elite class that was the priesthood for that deity. ... That looks an awful lot like the new digital economy to me, where you have (natural language) translators and everybody else who contributes to the corpora that allows the data schemes to operate, contributing to the fortunes of whoever runs the computers. You're saying, "Well, but they're helping the AI, it's not us, they're helping the AI." It reminds me of somebody saying, "Oh, build these pyramids, it's in the service of this deity," and, on the ground, it's in the service of an elite. It's an economic effect of the new idea. The new religious idea of AI is a lot like the economic effect of the old idea, religion...
[...] For three decades, the AI world was trying to create an ideal, little, crystalline algorithm that could take two dictionaries for two languages and turn out translations between them. Intellectually, this had its origins particularly around MIT and Stanford. Back in the 50s, because of Chomsky's work, there had been a notion of a very compact and elegant core to language. It wasn't a bad hypothesis, it was a legitimate, perfectly reasonable hypothesis to test. But over time, the hypothesis failed because nobody could do it.
Finally, in the 1990s, researchers at IBM and elsewhere figured out that the way to do it was with what we now call big data, where you get a very large example set, which interestingly, we call a corpus—call it a dead person. That's the term of art for these things. If you have enough examples, you can correlate examples of real translations phrase by phrase with new documents that need to be translated. You mash them all up, and you end up with something that's readable. It's not perfect, is not artful, it's not necessarily correct, but suddenly it's usable. And you know what? It's fantastic. I love the idea that you can take some memo, and instead of having to find a translator and wait for them to do the work, you can just have something approximate right away, because that's often all you need. That's a benefit to the world. I'm happy it's been done. It's a great thing.
The thing that we have to notice though is that, because of the mythology about AI, the services are presented as though they are these mystical, magical personas. IBM makes a dramatic case that they've created this entity that they call different things at different times—Deep Blue and so forth. The consumer tech companies, we tend to put a face in front of them, like a Cortana or a Siri. The problem with that is that these are not freestanding services.
In other words, if you go back to some of the thought experiments from philosophical debates about AI from the old days, there are lots of experiments, like if you have some black box that can do something—it can understand language—why wouldn't you call that a person? There are many, many variations on these kinds of thought experiments, starting with the Turing test, of course, through Mary the color scientist, and a zillion other ones that have come up.
This is not one of those. What this is, is behind the curtain, is literally millions of human translators who have to provide the examples. The thing is, they didn't just provide one corpus once way back. Instead, they're providing a new corpus every day, because the world of references, current events, and slang does change every day. We have to go and scrape examples from literally millions of translators, unbeknownst to them, every single day, to help keep those services working.
The problem here should be clear, but just let me state it explicitly: we're not paying the people who are providing the examples to the corpora—which is the plural of corpus—that we need in order to make AI algorithms work. In order to create this illusion of a freestanding autonomous artificial intelligent creature, we have to ignore the contributions from all the people whose data we're grabbing in order to make it work. That has a negative economic consequence.
This, to me, is where it becomes serious. Everything up to now, you can say, "Well, look, if people want to have an algorithm tell them who to date, is that any stupider than how we decided who to sleep with when we were young, before the Internet was working?" Doubtful, because we were pretty stupid back then. I doubt it could have that much negative consequence.
This is all of a sudden a pretty big deal. If you talk to translators, they're facing a predicament, which is very similar to some of the other early victim populations, due to the particular way we digitize things. It's similar to what's happened with recording musicians, or investigative journalists—which is the one that bothers me the most—or photographers. What they're seeing is a severe decline in how much they're paid, what opportunities they have, their long-term prospects...
EXCERPT: [...] In the history of organized religion, it's often been the case that people have been disempowered precisely to serve what was perceived to be the needs of some deity or another, where in fact what they were doing was supporting an elite class that was the priesthood for that deity. ... That looks an awful lot like the new digital economy to me, where you have (natural language) translators and everybody else who contributes to the corpora that allows the data schemes to operate, contributing to the fortunes of whoever runs the computers. You're saying, "Well, but they're helping the AI, it's not us, they're helping the AI." It reminds me of somebody saying, "Oh, build these pyramids, it's in the service of this deity," and, on the ground, it's in the service of an elite. It's an economic effect of the new idea. The new religious idea of AI is a lot like the economic effect of the old idea, religion...
[...] For three decades, the AI world was trying to create an ideal, little, crystalline algorithm that could take two dictionaries for two languages and turn out translations between them. Intellectually, this had its origins particularly around MIT and Stanford. Back in the 50s, because of Chomsky's work, there had been a notion of a very compact and elegant core to language. It wasn't a bad hypothesis, it was a legitimate, perfectly reasonable hypothesis to test. But over time, the hypothesis failed because nobody could do it.
Finally, in the 1990s, researchers at IBM and elsewhere figured out that the way to do it was with what we now call big data, where you get a very large example set, which interestingly, we call a corpus—call it a dead person. That's the term of art for these things. If you have enough examples, you can correlate examples of real translations phrase by phrase with new documents that need to be translated. You mash them all up, and you end up with something that's readable. It's not perfect, is not artful, it's not necessarily correct, but suddenly it's usable. And you know what? It's fantastic. I love the idea that you can take some memo, and instead of having to find a translator and wait for them to do the work, you can just have something approximate right away, because that's often all you need. That's a benefit to the world. I'm happy it's been done. It's a great thing.
The thing that we have to notice though is that, because of the mythology about AI, the services are presented as though they are these mystical, magical personas. IBM makes a dramatic case that they've created this entity that they call different things at different times—Deep Blue and so forth. The consumer tech companies, we tend to put a face in front of them, like a Cortana or a Siri. The problem with that is that these are not freestanding services.
In other words, if you go back to some of the thought experiments from philosophical debates about AI from the old days, there are lots of experiments, like if you have some black box that can do something—it can understand language—why wouldn't you call that a person? There are many, many variations on these kinds of thought experiments, starting with the Turing test, of course, through Mary the color scientist, and a zillion other ones that have come up.
This is not one of those. What this is, is behind the curtain, is literally millions of human translators who have to provide the examples. The thing is, they didn't just provide one corpus once way back. Instead, they're providing a new corpus every day, because the world of references, current events, and slang does change every day. We have to go and scrape examples from literally millions of translators, unbeknownst to them, every single day, to help keep those services working.
The problem here should be clear, but just let me state it explicitly: we're not paying the people who are providing the examples to the corpora—which is the plural of corpus—that we need in order to make AI algorithms work. In order to create this illusion of a freestanding autonomous artificial intelligent creature, we have to ignore the contributions from all the people whose data we're grabbing in order to make it work. That has a negative economic consequence.
This, to me, is where it becomes serious. Everything up to now, you can say, "Well, look, if people want to have an algorithm tell them who to date, is that any stupider than how we decided who to sleep with when we were young, before the Internet was working?" Doubtful, because we were pretty stupid back then. I doubt it could have that much negative consequence.
This is all of a sudden a pretty big deal. If you talk to translators, they're facing a predicament, which is very similar to some of the other early victim populations, due to the particular way we digitize things. It's similar to what's happened with recording musicians, or investigative journalists—which is the one that bothers me the most—or photographers. What they're seeing is a severe decline in how much they're paid, what opportunities they have, their long-term prospects...
