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I saw a presentation once (It was not recorded, so I can't link it) that said the difference between real intelligence and artificial intelligence is the abilit

I saw a presentation once (It was not recorded, so I can't link it) that said the difference between real intelligence and artificial intelligence is the ability to quickly learn. As an example, he said imagine if he invented a word. Poditon. And he told us that a poditon is any object that can fit on a podium. Instantly, you know whether or not any object can be classified as a poditon. A laptop is a poditon, but a car is not. We are not at the stage where we can just tell a program "Any object that can fit on a podium is a poditon" and then ask "Is X a poditon?" and get a correct answer. And we probably won't be there for another couple decades.

gjm11 5y ago
So, I tried this out with GPT-3 in the OpenAI Playground. (The model called text-davinci-002.) My prompt looked like this: Definition: A blorple is anything that is red and more or less round. Question: Is a tennis ball a blorple? Answer: No, because although tennis balls are round they aren't red. Question: Is a cherry a blorple? Answer: Yes, because cherries are red and approximately round. Definition: A poditon is anything that can fit on top of a podium. Question: Is a laptop computer a poditon? Answer: GPT-3 says: "Yes, because laptop computers are small enough to fit on top of a podium." Is a normal-sized automobile a poditon? "No, an automobile is too large to fit on top of a podium." Is the sun a poditon? "No, because the sun is too large to fit on top of a podium." Is a human being a poditon? "Yes, because human beings are small enough to fit on top of a podium." Is a house a poditon? "No, because a house is too large to fit on top of a podium." While generating those answers it also spontaneously answered the question for tennis balls (yes) and books (yes). Decades sure do go by quickly, these days.
- Sohcahtoa82 5y ago
  FWIW, the presentation was in 2018, and GPT-3 came out in 2020. But yeah, that's pretty amazing.
stevenhuang 5y ago
If you've seen examples of GPT3 you'd know this is already possible.
nl 5y ago
> We are not at the stage where we can just tell a program "Any object that can fit on a podium is a poditon" and then ask "Is X a poditon?" and get a correct answer. And we probably won't be there for another couple decades. If that presenter actually said that, they need to take a look at "Few shot learning in language models" (just Google the term and start reading the papers).

gjm11 5y ago

So, I tried this out with GPT-3 in the OpenAI Playground. (The model called text-davinci-002.) My prompt looked like this: Definition: A blorple is anything that is red and more or less round. Question: Is a tennis ball a blorple? Answer: No, because although tennis balls are round they aren't red. Question: Is a cherry a blorple? Answer: Yes, because cherries are red and approximately round. Definition: A poditon is anything that can fit on top of a podium. Question: Is a laptop computer a poditon? Answer: GPT-3 says: "Yes, because laptop computers are small enough to fit on top of a podium." Is a normal-sized automobile a poditon? "No, an automobile is too large to fit on top of a podium." Is the sun a poditon? "No, because the sun is too large to fit on top of a podium." Is a human being a poditon? "Yes, because human beings are small enough to fit on top of a podium." Is a house a poditon? "No, because a house is too large to fit on top of a podium." While generating those answers it also spontaneously answered the question for tennis balls (yes) and books (yes). Decades sure do go by quickly, these days.

Sohcahtoa82 5y ago
FWIW, the presentation was in 2018, and GPT-3 came out in 2020. But yeah, that's pretty amazing.

stevenhuang 5y ago

If you've seen examples of GPT3 you'd know this is already possible.

nl 5y ago

> We are not at the stage where we can just tell a program "Any object that can fit on a podium is a poditon" and then ask "Is X a poditon?" and get a correct answer. And we probably won't be there for another couple decades. If that presenter actually said that, they need to take a look at "Few shot learning in language models" (just Google the term and start reading the papers).