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> A three-dimensional representation of objects also provides the basis for learning compositional structure, i.e. how objects are composed of other objects arr

> A three-dimensional representation of objects also provides the basis for learning compositional structure, i.e. how objects are composed of other objects arranged in particular ways I found this particularly interesting. I have a young daughter and I find it fascinating how quickly and reliably she can learn an object and then recognise others in a way a computer just can’t do at all. Eg see very rough line drawing of an object, then recognise that animal in real life. It’s also interesting the way I can explain new objects or animals based on one she knows and then have her recognise them easily. Eg a tiger is like a lion but has no mane and does have stripes. This comes very naturally when teaching and learning and feels very uniquely ‘human’ compared to the rigidity of computer vision I’ve seen so far.

ckosidows 6y ago
Have you seen Open AI's DALL•E? https://openai.com/blog/dall-e/ https://openai.com/blog/dall-e/ Kind of does something similar to what you describe, right? This project blows my mind, by the way. I still think it's the coolest thing to come out of AI research so far.
- chrisweekly 6y ago
  WOW!! I'm astounded. Zero-shot visual reasoning?? As an emergent capability?? Um. wat. >"GPT-3 can be instructed to perform many kinds of tasks solely from a description and a cue to generate the answer supplied in its prompt, without any additional training. For example, when prompted with the phrase “here is the sentence ‘a person walking his dog in the park’ translated into French:”, GPT-3 answers “un homme qui promène son chien dans le parc.” This capability is called zero-shot reasoning. We find that DALL·E extends this capability to the visual domain, and is able to perform several kinds of image-to-image translation tasks when prompted in the right way."
- tablespoon 6y ago
  > Have you seen Open AI's DALL•E? https://openai.com/blog/dall-e/ https://openai.com/blog/dall-e/ Is there any way to try it out easily? Everything on that page looked like it was pre-rendered (constrained-choice mad libs).
  - ckosidows 6y ago
    I don't think so. It does appear pre-rendered and my guess is the blog might only showcase the use cases that worked the best. Or it might take a really long time to generate results.
    - drdeca 6y ago
      It is also my understanding that it as a whole is not released. However, iirc one of its two main components, has a smaller version of it released. This component is called CLIP . Given some text options and an image, CLIP will predict/guess which of the texts is a description of the image. It handles images in various art styles well? See : https://openai.com/blog/clip/ https://openai.com/blog/clip/
- Retric 6y ago
  Very cool and interesting that the green pentagon clock had several images of non pentagons. It’s getting the general flat sides correct, but not counting the number of vertices.
  - ummwhat 6y ago
    I feel like that's true to how the average person thinks. My aunt told me she got octagonal tiles for her bathroom. I said you can't tile octagons in two dimensions. After a short argument she showed me a picture. They were hexagons.
- jonplackett 6y ago
  This is amazing! The reasoning required for making the building blocks image is really impressive. It's funny isn't it how we're so amazed at these abilities, but even a fairly slow-witted child could do all this stuff.
- danielbarla 6y ago
  That's insanely cool. I wonder, if similarly, it could be set up to pump out 3D models of things. We'd be pretty close to having an "AI storyteller" for games, movies, etc - and one that can improvise!
jjk166 6y ago
Well it's not that surprising when you consider that the training data for your daughter's neural network is being generated by a network of the same architecture which has been optimized to do so. We are not just learning machines, we are teaching machines. The rough drawing of a lion is your distillation of the various visual features which you consider to be hallmarks of something being a lion. Your description of the difference between lions and tigers is a summary of the features you apply the most weight to when you try to distinguish them. Even data in the wild like pictures on the internet or books in the library are overwhelmingly generated by humans for humans. Computers on the other hand are fundamentally alone - they have no ancestors with similar minds to pass understanding down to them, and must figure out everything on their own. Really machine learning is a misnomer, what they are doing is far more analogous to discovery than learning.

ckosidows 6y ago

Have you seen Open AI's DALL•E? https://openai.com/blog/dall-e/ https://openai.com/blog/dall-e/ Kind of does something similar to what you describe, right? This project blows my mind, by the way. I still think it's the coolest thing to come out of AI research so far.

chrisweekly 6y ago
WOW!! I'm astounded. Zero-shot visual reasoning?? As an emergent capability?? Um. wat. >"GPT-3 can be instructed to perform many kinds of tasks solely from a description and a cue to generate the answer supplied in its prompt, without any additional training. For example, when prompted with the phrase “here is the sentence ‘a person walking his dog in the park’ translated into French:”, GPT-3 answers “un homme qui promène son chien dans le parc.” This capability is called zero-shot reasoning. We find that DALL·E extends this capability to the visual domain, and is able to perform several kinds of image-to-image translation tasks when prompted in the right way."
tablespoon 6y ago
> Have you seen Open AI's DALL•E? https://openai.com/blog/dall-e/ https://openai.com/blog/dall-e/ Is there any way to try it out easily? Everything on that page looked like it was pre-rendered (constrained-choice mad libs).
- ckosidows 6y ago
  I don't think so. It does appear pre-rendered and my guess is the blog might only showcase the use cases that worked the best. Or it might take a really long time to generate results.
  - drdeca 6y ago
    It is also my understanding that it as a whole is not released. However, iirc one of its two main components, has a smaller version of it released. This component is called CLIP . Given some text options and an image, CLIP will predict/guess which of the texts is a description of the image. It handles images in various art styles well? See : https://openai.com/blog/clip/ https://openai.com/blog/clip/
Retric 6y ago
Very cool and interesting that the green pentagon clock had several images of non pentagons. It’s getting the general flat sides correct, but not counting the number of vertices.
- ummwhat 6y ago
  I feel like that's true to how the average person thinks. My aunt told me she got octagonal tiles for her bathroom. I said you can't tile octagons in two dimensions. After a short argument she showed me a picture. They were hexagons.
jonplackett 6y ago
This is amazing! The reasoning required for making the building blocks image is really impressive. It's funny isn't it how we're so amazed at these abilities, but even a fairly slow-witted child could do all this stuff.
danielbarla 6y ago
That's insanely cool. I wonder, if similarly, it could be set up to pump out 3D models of things. We'd be pretty close to having an "AI storyteller" for games, movies, etc - and one that can improvise!

jjk166 6y ago

Well it's not that surprising when you consider that the training data for your daughter's neural network is being generated by a network of the same architecture which has been optimized to do so. We are not just learning machines, we are teaching machines. The rough drawing of a lion is your distillation of the various visual features which you consider to be hallmarks of something being a lion. Your description of the difference between lions and tigers is a summary of the features you apply the most weight to when you try to distinguish them. Even data in the wild like pictures on the internet or books in the library are overwhelmingly generated by humans for humans. Computers on the other hand are fundamentally alone - they have no ancestors with similar minds to pass understanding down to them, and must figure out everything on their own. Really machine learning is a misnomer, what they are doing is far more analogous to discovery than learning.