5 ms·

This is a lot of fun! I took a stab at doing something similar with virtualising an ML model for an arbitrary text classification task: https://mastodon.scot/@

This is a lot of fun! I took a stab at doing something similar with virtualising an ML model for an arbitrary text classification task: https://mastodon.scot/@danny/109452332918910028 https://mastodon.scot/@danny/109452332918910028 > I want you to act as a machine learning classifier. The training set consists of news topics and the label categories are “politics”, “celebrity” and “finance”. I will type text input and you will reply with what the classifier output would be. I want you to only reply with the output and nothing else. Do no write explanations. Example training data: Topic: Barack Obama Label: politics Topic: Kim Kardashian Label: celebrity Topic: Dow Jones Label: finance Topic: Tony Blair Label:

sgu999 4y ago
Very cool idea. That'd be a good way to see how it can generalize, actually. I suspect you'd quickly find its limits with something that isn't a toy example of an intro to machine learning.
- dvt 4y ago
  It even fails at pretty rudimentary toy examples. The over-hyping here is absurd. > what doesn't fit: red, blue, green, dog The word "dog" does not fit with the other three words because they are colors, while "dog" is a type of animal. > what doesn't fit: dog, 1, 2, 3 The number "1" does not fit with the other three words because the other three words are all types of animals, while "1" is a number.
  - johnfn 4y ago
    Works fine for me: > The word "dog" does not fit with the other words in the list because "1", "2", and "3" are numbers, while "dog" is a type of animal. The other words in the list are all examples of numbers, whereas "dog" is not a number.
    - dvt 4y ago
      Screenshot for proof: https://i.imgur.com/nw6rstc.png https://i.imgur.com/nw6rstc.png Slightly different second answer this time around, but still equally wrong.
  - comboy 4y ago
    GPT-3 has been trolling simple examples too. I think it comes from what it crawled. You will find very few conversations like that with just correct data, what gets popular and how jokes are made is when there is a twist. Your comment is an example of such data too.
  - Luz_K 4y ago
    It's true that there are a lot of limitations still, but I think you are underestimating how powerful it can get with the right input prompt. For example for your task I wrote: >You are a professional puzzle solver. Only answer with the element in the list that does not fit. Do not include an explanation. Do not write anything except the element that does not fit. Dog, 1, 2, 3 Dog >Computer, Phone, Tree, Microwave Tree >red, blue, green, dog dog It seems to perform quite a lot better in my short time of testing than before which seems quite extraordinary to me. Now that is not to say you can't find a bunch of examples where it fails or that it is even close to human level for this particular task. But this still seems like a huge technological advancement to me and I did not expect that ai systems would be at this level quite so soon.

sgu999 4y ago

Very cool idea. That'd be a good way to see how it can generalize, actually. I suspect you'd quickly find its limits with something that isn't a toy example of an intro to machine learning.

dvt 4y ago
It even fails at pretty rudimentary toy examples. The over-hyping here is absurd. > what doesn't fit: red, blue, green, dog The word "dog" does not fit with the other three words because they are colors, while "dog" is a type of animal. > what doesn't fit: dog, 1, 2, 3 The number "1" does not fit with the other three words because the other three words are all types of animals, while "1" is a number.
- johnfn 4y ago
  Works fine for me: > The word "dog" does not fit with the other words in the list because "1", "2", and "3" are numbers, while "dog" is a type of animal. The other words in the list are all examples of numbers, whereas "dog" is not a number.
  - dvt 4y ago
    Screenshot for proof: https://i.imgur.com/nw6rstc.png https://i.imgur.com/nw6rstc.png Slightly different second answer this time around, but still equally wrong.
- comboy 4y ago
  GPT-3 has been trolling simple examples too. I think it comes from what it crawled. You will find very few conversations like that with just correct data, what gets popular and how jokes are made is when there is a twist. Your comment is an example of such data too.
- Luz_K 4y ago
  It's true that there are a lot of limitations still, but I think you are underestimating how powerful it can get with the right input prompt. For example for your task I wrote: >You are a professional puzzle solver. Only answer with the element in the list that does not fit. Do not include an explanation. Do not write anything except the element that does not fit. Dog, 1, 2, 3 Dog >Computer, Phone, Tree, Microwave Tree >red, blue, green, dog dog It seems to perform quite a lot better in my short time of testing than before which seems quite extraordinary to me. Now that is not to say you can't find a bunch of examples where it fails or that it is even close to human level for this particular task. But this still seems like a huge technological advancement to me and I did not expect that ai systems would be at this level quite so soon.