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> The AI effect occurs when onlookers discount the behavior of an artificial intelligence program by arguing that it is not real intelligence. > Author Pamela

> The AI effect occurs when onlookers discount the behavior of an artificial intelligence program by arguing that it is not real intelligence. > Author Pamela McCorduck writes: "It's part of the history of the field of artificial intelligence that every time somebody figured out how to make a computer do something—play good checkers, solve simple but relatively informal problems—there was a chorus of critics to say, 'that's not thinking'." Researcher Rodney Brooks complains: "Every time we figure out a piece of it, it stops being magical; we say, 'Oh, that's just a computation.'" https://en.wikipedia.org/wiki/AI_effect https://en.wikipedia.org/wiki/AI_effect

stephc_int13 4y ago
I think that we're going to see that again and again. At some point, we should learn and try to manage expectations, by using less emotionally charged words.
goatlover 4y ago
Because it’s all narrow AI that’s often hyped as it’s science fiction counterpart. Augmented intelligence would have been a better term, because that’s what AI had been to this point. Leave the term Artificial Intelligence for AGI. If and when the day comes that we have Data, HAL, Skynet, then I don’t think the critics will be making the same complaint. They’ll have bigger things to worry about.
BrainVirus 4y ago
I hate this meme. Essentially, an arrogant and condescending straw man argument. Here is what really happens. People who are not familiar with programming assume that some mental faculty is necessary to solve a particular problem. Indeed, it is required for a person to succeed at the problem. That mental faculty is often general and can be applied to other tasks. It's some aspect of what we consider general intelligence. Instead, software engineers look at a problem and solve it by changing its representation to fit the capabilities of a pre-existing algorithm. The solution has nothing to do with mental faculties in the general population. The solutions ends up being specific. If you want to apply it to another problem, you need to transform that problem into some clever representation as well. People notice the lack of generality and say "no, this is not what we had in mind". It's an entirely reasonable sentiment.
- omegalulw 4y ago
  +1. You will find such positions often championed by people who don't understand how machine learning works - but maybe have tried out a fancy tool that used such a model (GPT-2, Stable Diffusion, etc). Classic example of "any sufficiently advanced technology is indistinguishable from magic" combined with Dunning–Kruger effect.
- melagonster 4y ago
  thanks your opinion, I usually see this but can't find a good reply.

stephc_int13 4y ago

I think that we're going to see that again and again. At some point, we should learn and try to manage expectations, by using less emotionally charged words.

goatlover 4y ago

Because it’s all narrow AI that’s often hyped as it’s science fiction counterpart. Augmented intelligence would have been a better term, because that’s what AI had been to this point. Leave the term Artificial Intelligence for AGI. If and when the day comes that we have Data, HAL, Skynet, then I don’t think the critics will be making the same complaint. They’ll have bigger things to worry about.

BrainVirus 4y ago

I hate this meme. Essentially, an arrogant and condescending straw man argument. Here is what really happens. People who are not familiar with programming assume that some mental faculty is necessary to solve a particular problem. Indeed, it is required for a person to succeed at the problem. That mental faculty is often general and can be applied to other tasks. It's some aspect of what we consider general intelligence. Instead, software engineers look at a problem and solve it by changing its representation to fit the capabilities of a pre-existing algorithm. The solution has nothing to do with mental faculties in the general population. The solutions ends up being specific. If you want to apply it to another problem, you need to transform that problem into some clever representation as well. People notice the lack of generality and say "no, this is not what we had in mind". It's an entirely reasonable sentiment.

omegalulw 4y ago
+1. You will find such positions often championed by people who don't understand how machine learning works - but maybe have tried out a fancy tool that used such a model (GPT-2, Stable Diffusion, etc). Classic example of "any sufficiently advanced technology is indistinguishable from magic" combined with Dunning–Kruger effect.
melagonster 4y ago
thanks your opinion, I usually see this but can't find a good reply.