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It doesn't have a mind. When you ask it "think of a number between 0 and 100", it delivers the most statistically likely (with some random variance) response to

by mithr 4y ago

It doesn't have a mind. When you ask it "think of a number between 0 and 100", it delivers the most statistically likely (with some random variance) response to your input text, based on what it's been trained on. In other words, it's seen (via its extremely large training set) that when asked that specific question, the response is most often a character from a particular set of characters, which happens to represent the numbers 0 through 100. It doesn't "understand" what that means in any real way. If the internet was full of examples of people answering "monkey" to that question, that is what it would sometimes answer.

ryanwaggoner 4y ago
It's more complicated than that, because it can competently answer many questions that it has a 0% chance of having seen before.
- mithr 4y ago
  I didn't mean to suggest that it's just a large mapping between exact inputs it's seen before and exact outputs — it's definitely complex! The size of the model allows it to infer statistical probabilities about related words even without having seen a particular sequence before. In a sense, it's able to take an "educated" guess at what is statistically likely to be the response you're looking for given the words in a particular input, in addition to the context it's seen in your interaction. To do that, it uses what it learned about the words, their sequences, their relationships to other words, etc. But at the end of the day, none of that means it has any "understanding" of what it's outputting. That's why there have been countless examples of it outputting very well-constructed, real-sounding descriptions of books/papers/etc that never existed — because it's really good at generating sentences that have the right "shape", but it has no way of knowing whether the contents of the sentence are actually true. It just knows that, given what it's seen in its training set (again, through a complex web of relationships), the response it generated is likely to look like something someone would have written if they were provided with the same input.
  - ryanwaggoner 4y ago
    I get what you’re saying, but can you define what “understanding” is, and when you’ll know that an AI is achieving it for a given task?

by mithr 4y ago

ryanwaggoner 4y ago

It's more complicated than that, because it can competently answer many questions that it has a 0% chance of having seen before.

mithr 4y ago
I didn't mean to suggest that it's just a large mapping between exact inputs it's seen before and exact outputs — it's definitely complex! The size of the model allows it to infer statistical probabilities about related words even without having seen a particular sequence before. In a sense, it's able to take an "educated" guess at what is statistically likely to be the response you're looking for given the words in a particular input, in addition to the context it's seen in your interaction. To do that, it uses what it learned about the words, their sequences, their relationships to other words, etc. But at the end of the day, none of that means it has any "understanding" of what it's outputting. That's why there have been countless examples of it outputting very well-constructed, real-sounding descriptions of books/papers/etc that never existed — because it's really good at generating sentences that have the right "shape", but it has no way of knowing whether the contents of the sentence are actually true. It just knows that, given what it's seen in its training set (again, through a complex web of relationships), the response it generated is likely to look like something someone would have written if they were provided with the same input.
- ryanwaggoner 4y ago
  I get what you’re saying, but can you define what “understanding” is, and when you’ll know that an AI is achieving it for a given task?