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Wait a minute.... Because the role of radiologists is not as simple as trained classification, we can conclude that "No, AGI isn't going to take over every soci

Wait a minute.... Because the role of radiologists is not as simple as trained classification, we can conclude that "No, AGI isn't going to take over every social system when GPT5 comes out"? Author starts off by saying that you can't generalize conclusions based on observations in a limited domain: > Geoff made a classic error that technologists often make, which is to observe a particular behavior (identifying some subset of radiology scans correctly) against some task (identifying hemorrhage on CT head scans correctly), and then to extrapolate based on that task alone. Author then goes on to suggest that there are a number of issues with radiology which AI will not solve. Then, based on this lack of ML's complete coverage of the aspects of radiology that he finds interesting, he then extrapolates: > Should you be worried GPT5 is going to interact with social systems and destroy our society single-handedly? No absolutely not. I don't see how the conclusion remotely follows from the argument. And more to the point, the refuting argument is embodied in the article itself.

skybrian 3y ago
Yes, it goes a bit far, but the basic problem is treating this as all-or-nothing: Either the article is true or false. Either AI is going to take over, or it will have little effect. I believe the article raises relevant issues about why deploying AI-based systems is harder and will take longer than it might appear. Another relevant example might be driverless cars. It's taking many years and deployment is still quite limited. Some companies gave up. But I'm not going to count Waymo out yet. Similarly, it's possible that Hinton got the timeline wrong for radiology, but it might still have significant affects on radiologist employment in the end. (Note that this sort of reasoning by analogy is useful for imagining plausible scenarios, but not for ruling things out.)
- naijaboiler 3y ago
  Driving is another place where people wrongfully think that solving the technical problem means the problem has been solved. Driving is a social problem. We are a long long way from solving it.
naijaboiler 3y ago
Correct. Social systems are far much more complex than technical problems. So nany technology optimists make the mistake of confusing a social problem as a technical problem, and think solving one means solving another. Wrong!

skybrian 3y ago

Yes, it goes a bit far, but the basic problem is treating this as all-or-nothing: Either the article is true or false. Either AI is going to take over, or it will have little effect. I believe the article raises relevant issues about why deploying AI-based systems is harder and will take longer than it might appear. Another relevant example might be driverless cars. It's taking many years and deployment is still quite limited. Some companies gave up. But I'm not going to count Waymo out yet. Similarly, it's possible that Hinton got the timeline wrong for radiology, but it might still have significant affects on radiologist employment in the end. (Note that this sort of reasoning by analogy is useful for imagining plausible scenarios, but not for ruling things out.)

naijaboiler 3y ago
Driving is another place where people wrongfully think that solving the technical problem means the problem has been solved. Driving is a social problem. We are a long long way from solving it.

naijaboiler 3y ago

Correct. Social systems are far much more complex than technical problems. So nany technology optimists make the mistake of confusing a social problem as a technical problem, and think solving one means solving another. Wrong!