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Everyone knows that the term machine learning doesn't actually have anything to do with human learning, right? It's just a set of words that were catchier than

Everyone knows that the term machine learning doesn't actually have anything to do with human learning, right? It's just a set of words that were catchier than gradient descent or curve fitting. Asserting legal rights based on a naming quirk seems pretty unfounded. Let's try that statement again: 'It is the same as executing gradient descent over multiple artists works' That sounds a heck of a lot closer to running a Photoshop filter than learning anything now doesn't it.

thomashop 4y ago
I fail to see how that last sentence makes it sound like a photoshop filter. Do you mean if you can express it as an algorithm it cannot be compared to human learning? What if we can reduce human learning to making predictions based on training data? Then it's not human learning anymore but more like a photoshop filter? "Everyone knows that the term machine learning doesn't actually have anything to do with human learning, right?" Do you have sources for that? They're not equal but I see a lot of parallels.
- greysphere 4y ago
  When someone successfully reduces human learning to predictions on training data, I will celebrate the Nobel prizes they are awarded for medicine, philosophy, and possibly physics and economics. Until then maybe we shouldn't just assert that it's true.
  - thomashop 4y ago
    Until then, we shouldn't just assert that it's false either. GPT-3 is coming quite close to being able to write novel convincing research papers. With GPT-4 and GPT-5 on the horizon I don't see why language models couldn't be significantly involved in winning a Nobel prize in the future. At least I wouldn't categorically exclude that possibility.
sethd 4y ago
> Everyone knows that the term machine learning doesn't actually have anything to do with human learning, right Unfortunately the answer is definitely no. People have heard the terms “learning” and “train” in this context and have anthropomorphized weak AI. The level of ignorance on display is sad to witness.
- thomashop 4y ago
  Deep learning is definitely inspired by biological learning and also shows surprising parallels in terms of for example how language rules are learned by children. "Machine learning doesn't have anything to do with human learning" is just not true. There are many parallels but sure they are not equal.
  - thomashop 4y ago
    Just happened across this paper: Theory of Mind May Have Spontaneously Emerged in Large Language Models Theory of mind (ToM), or the ability to impute unobservable mental states to others, is central to human social interactions, communication, empathy, self-consciousness, and morality. We administer classic false-belief tasks, widely used to test ToM in humans, to several language models, without any examples or pre-training. Our results show that models published before 2022 show virtually no ability to solve ToM tasks. Yet, the January 2022 version of GPT-3 (davinci-002) solved 70% of ToM tasks, a performance comparable with that of seven-year-old children. Moreover, its November 2022 version (davinci-003), solved 93% of ToM tasks, a performance comparable with that of nine-year-old children. These findings suggest that ToM-like ability (thus far considered to be uniquely human) may have spontaneously emerged as a byproduct of language models' improving language skills.
dnh44 4y ago
Actually I think I use gradient descent in a lot of situations. Consider the act of adding salt to chicken broth for example but I can quite easily think of dozens of other things.
- greysphere 4y ago
  Absolutely. Gradient descent is a fantastic technique for a lot of problems. This and many other algorithms can be performed by both humans and computers and give useful results. Notice neither your or my comments connect any of this to learning. After numerous times making chicken broth, something will change about your brain/body and we will say 'you learned how to season broth.' We have basically no idea what this process is, or whether it relies on gradient descent or anything else.
  - dnh44 4y ago
    Hmm okay I see your point now.

thomashop 4y ago

I fail to see how that last sentence makes it sound like a photoshop filter. Do you mean if you can express it as an algorithm it cannot be compared to human learning? What if we can reduce human learning to making predictions based on training data? Then it's not human learning anymore but more like a photoshop filter? "Everyone knows that the term machine learning doesn't actually have anything to do with human learning, right?" Do you have sources for that? They're not equal but I see a lot of parallels.

greysphere 4y ago
When someone successfully reduces human learning to predictions on training data, I will celebrate the Nobel prizes they are awarded for medicine, philosophy, and possibly physics and economics. Until then maybe we shouldn't just assert that it's true.
- thomashop 4y ago
  Until then, we shouldn't just assert that it's false either. GPT-3 is coming quite close to being able to write novel convincing research papers. With GPT-4 and GPT-5 on the horizon I don't see why language models couldn't be significantly involved in winning a Nobel prize in the future. At least I wouldn't categorically exclude that possibility.

sethd 4y ago

> Everyone knows that the term machine learning doesn't actually have anything to do with human learning, right Unfortunately the answer is definitely no. People have heard the terms “learning” and “train” in this context and have anthropomorphized weak AI. The level of ignorance on display is sad to witness.

thomashop 4y ago
Deep learning is definitely inspired by biological learning and also shows surprising parallels in terms of for example how language rules are learned by children. "Machine learning doesn't have anything to do with human learning" is just not true. There are many parallels but sure they are not equal.
- thomashop 4y ago
  Just happened across this paper: Theory of Mind May Have Spontaneously Emerged in Large Language Models Theory of mind (ToM), or the ability to impute unobservable mental states to others, is central to human social interactions, communication, empathy, self-consciousness, and morality. We administer classic false-belief tasks, widely used to test ToM in humans, to several language models, without any examples or pre-training. Our results show that models published before 2022 show virtually no ability to solve ToM tasks. Yet, the January 2022 version of GPT-3 (davinci-002) solved 70% of ToM tasks, a performance comparable with that of seven-year-old children. Moreover, its November 2022 version (davinci-003), solved 93% of ToM tasks, a performance comparable with that of nine-year-old children. These findings suggest that ToM-like ability (thus far considered to be uniquely human) may have spontaneously emerged as a byproduct of language models' improving language skills.

dnh44 4y ago

Actually I think I use gradient descent in a lot of situations. Consider the act of adding salt to chicken broth for example but I can quite easily think of dozens of other things.

greysphere 4y ago
Absolutely. Gradient descent is a fantastic technique for a lot of problems. This and many other algorithms can be performed by both humans and computers and give useful results. Notice neither your or my comments connect any of this to learning. After numerous times making chicken broth, something will change about your brain/body and we will say 'you learned how to season broth.' We have basically no idea what this process is, or whether it relies on gradient descent or anything else.
- dnh44 4y ago
  Hmm okay I see your point now.