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Does this address NLP skeptics' concerns that Transformer models don't "understand" language? If the AI can actually draw an image of a green block on a red bl

Does this address NLP skeptics' concerns that Transformer models don't "understand" language? If the AI can actually draw an image of a green block on a red block, and vice versa, then it clearly understands something about the concepts "red", "green", "block", and "on".

karmasimida 6y ago
I think it is safe to say that learning a joint distribution of vision + language, is fully possible at this stage, demonstrating by this work. But 'understanding' itself needs to be further specified, in order to be tested even. What strikes me most is the fidelity of those generated images, matching the SOTA from GAN literature with much more variety, without using the GAN objective. It seems Transformer model might be the best neural construct we have right now, to learn any distribution, assuming more than enough data.
tralarpa 6y ago
Try a large block on a small block. As the authors also have noted in their comments the success rate is nearly zero. One may wonder why. Maybe because that's something you see rarely in photos? At the end, it doesn't "understand" the meaning of the words.
- RealSpaceMonkey 6y ago
  Maybe because large/small are relative to each other here? Maybe it would be able to do "a block inside a block"
TigeriusKirk 6y ago
There are examples on twitter showing it doesn't really understand spatial relations very well. Stuff like "red block on top of blue block on top of green block" will generate red, green, and blue blocks, but not in the desired order. https://twitter.com/peabody124/status/1346565268538089483 https://twitter.com/peabody124/status/1346565268538089483
deleted 6y ago
[deleted]
dcolkitt 6y ago
The root-case of skepticism has always been that while Transformers do exceptionally well on finite-sized tasks, they lack any fully recursive understanding of the concepts.[0] A human can learn basic arithmetic, then generalize those principles to bigger number arithmetic, then go from there to algebra, then calculus, then so. Successively building on previously learned concepts in a fully recursive manner. Transformers are limited by the exponential size of their network. So GPT-3 does very well with 2-digit addition and okay with 2-digit multiplication, but can't abstract to 6-digit arithmetic. DALL-E is an incredible achievement, but doesn't really do anything to change this fact. GPT-3 can have an excellent understanding of a finite sized concept space, yet it's still architecturally limited at building recursive abstractions. So maybe it can understand "green block on a red block". But try to give it something like "a 32x16 checkerboard of green and red blocks surrounded by a gold border frame studded with blue triangles". I guarantee the architecture can't get that exactly correct. The point is that, in some sense, GPT-3 is a technical dead-end. We've had to exponentially scale up the size of the network (12B parameters) to make the same complexity gains that humans make with linear training. The fact that we've managed to push it this far is an incredible technical achievement, but it's pretty clear that we're still missing something fundamental. [0] https://arxiv.org/pdf/1906.06755.pdf https://arxiv.org/pdf/1906.06755.pdf
- codetrotter 6y ago
  > So GPT-3 does very well with 2-digit addition and okay with 2-digit multiplication, but can't abstract to 6-digit arithmetic. That sounds disappointing but what if instead of trying to teach it to do addition one would teach it to write source code for making addition and other maths operations instead? Then you can ask it to solve a problem but instead of it giving you the answer it would give you source code for finding the answer. So for example you ask it “what is the square root of five?” then it responds: fn main () { println!("{}", 5f64.sqrt()); }
- Veedrac 6y ago
  > So GPT-3 does very well with 2-digit addition and okay with 2-digit multiplication, but can't abstract to 6-digit arithmetic. This is false, GPT-3 can do 10-digit addition with ~60% accuracy, with comma separators. Without BPEs it would doubtlessly manage much better.
tikwidd 6y ago
if(adj == 'red') drawBlock(RED) According to your definition of understanding, this program understands something about the concept RED. But the code is just dealing with arbitrary values in memory (e.g. RED = 0xFF0000)
bra-ket 6y ago
nah, it's still big and dumb model with no idea what it's doing, deepfake 2.0. It looks like a variation on plain old image search engine, unreliable at that, as compared to exact matching. But it has obvious application in design as it can create these interesting combinations of objects & styles. And I loved the snail-harp.

karmasimida 6y ago

I think it is safe to say that learning a joint distribution of vision + language, is fully possible at this stage, demonstrating by this work. But 'understanding' itself needs to be further specified, in order to be tested even. What strikes me most is the fidelity of those generated images, matching the SOTA from GAN literature with much more variety, without using the GAN objective. It seems Transformer model might be the best neural construct we have right now, to learn any distribution, assuming more than enough data.

tralarpa 6y ago

Try a large block on a small block. As the authors also have noted in their comments the success rate is nearly zero. One may wonder why. Maybe because that's something you see rarely in photos? At the end, it doesn't "understand" the meaning of the words.

RealSpaceMonkey 6y ago
Maybe because large/small are relative to each other here? Maybe it would be able to do "a block inside a block"

TigeriusKirk 6y ago

There are examples on twitter showing it doesn't really understand spatial relations very well. Stuff like "red block on top of blue block on top of green block" will generate red, green, and blue blocks, but not in the desired order. https://twitter.com/peabody124/status/1346565268538089483 https://twitter.com/peabody124/status/1346565268538089483

deleted 6y ago

[deleted]

dcolkitt 6y ago

The root-case of skepticism has always been that while Transformers do exceptionally well on finite-sized tasks, they lack any fully recursive understanding of the concepts.[0] A human can learn basic arithmetic, then generalize those principles to bigger number arithmetic, then go from there to algebra, then calculus, then so. Successively building on previously learned concepts in a fully recursive manner. Transformers are limited by the exponential size of their network. So GPT-3 does very well with 2-digit addition and okay with 2-digit multiplication, but can't abstract to 6-digit arithmetic. DALL-E is an incredible achievement, but doesn't really do anything to change this fact. GPT-3 can have an excellent understanding of a finite sized concept space, yet it's still architecturally limited at building recursive abstractions. So maybe it can understand "green block on a red block". But try to give it something like "a 32x16 checkerboard of green and red blocks surrounded by a gold border frame studded with blue triangles". I guarantee the architecture can't get that exactly correct. The point is that, in some sense, GPT-3 is a technical dead-end. We've had to exponentially scale up the size of the network (12B parameters) to make the same complexity gains that humans make with linear training. The fact that we've managed to push it this far is an incredible technical achievement, but it's pretty clear that we're still missing something fundamental. [0] https://arxiv.org/pdf/1906.06755.pdf https://arxiv.org/pdf/1906.06755.pdf

codetrotter 6y ago
> So GPT-3 does very well with 2-digit addition and okay with 2-digit multiplication, but can't abstract to 6-digit arithmetic. That sounds disappointing but what if instead of trying to teach it to do addition one would teach it to write source code for making addition and other maths operations instead? Then you can ask it to solve a problem but instead of it giving you the answer it would give you source code for finding the answer. So for example you ask it “what is the square root of five?” then it responds: fn main () { println!("{}", 5f64.sqrt()); }
Veedrac 6y ago
> So GPT-3 does very well with 2-digit addition and okay with 2-digit multiplication, but can't abstract to 6-digit arithmetic. This is false, GPT-3 can do 10-digit addition with ~60% accuracy, with comma separators. Without BPEs it would doubtlessly manage much better.

tikwidd 6y ago

if(adj == 'red') drawBlock(RED) According to your definition of understanding, this program understands something about the concept RED. But the code is just dealing with arbitrary values in memory (e.g. RED = 0xFF0000)

bra-ket 6y ago

nah, it's still big and dumb model with no idea what it's doing, deepfake 2.0. It looks like a variation on plain old image search engine, unreliable at that, as compared to exact matching. But it has obvious application in design as it can create these interesting combinations of objects & styles. And I loved the snail-harp.