4 ms·

>But if we want AIs that 'know' the same things we know, then we have to build them to perceive in a multi-modal way, and interact with stuff in the world, rath

>But if we want AIs that 'know' the same things we know, then we have to build them to perceive in a multi-modal way, and interact with stuff in the world, rather than just self-supervising on piles of internet data. In other words, a LLM that is tied to a GAN that generates images, produces an system that can both describe to you what is a cat verbally and show you a picture of a cat. Does it, then, know what "a cat" is? Edit: Furthermore, if you then tie this AI to a CV model with a camera which you can point at a cat and it will tell you that it is, indeed, a cat, and then it will also be able to produce a verbal description of a cat as well as show you an abstract picture of a cat or pick cats out of a random set of images, does this whole system know what "a cat" is? If you, then, make a robot with a camera and hands, attach to the system a more complex CV model that can see in 3D, ask the LLM to produce you a set of code instructions that can be parametrized to produce a motion that would pet the cat, input those instructions into the robot to make it pet a specific cat that has the specific 3D point cloud (I guess that's currently difficult but solveable), and the system will then indeed pet the cat, would it then know what "a cat" is?.. The underlying LLM is still the same in all these scenarios. Where is the boundary?

markmaglana 4y ago
At some point, when multiple components (including the LLM) have been connected to form a system that exhibits "knowing" (the way humans do), wouldn't the "intelligence" be distributed across the entire system rather than attributed primarily to the LLM? In other words, the LLM wouldn't be the equivalent of the human brain. Instead, it would just be equivalent to that part of the human brain that processes language.
- grugagag 4y ago
  Interesting point that seems quite valid to me. We use different modes of thinking from analytical to emotional, verbal to nonverbal, reactionary, etc. It is possible that LLMs are the key to one of brains modules responsible for producing/processing language but it does not involve or has any knowledge of the other modules necessary for getting closer to human intelligence.
abeppu 4y ago
> The underlying LLM is still the same in all these scenarios. Where is the boundary? No, it's not the same LLM; you'd have to change the LLM in all of those cases. How does it receive input from the GAN? The typical LLM is constructed to literally receive a sequence of encoded tokens. There are vision transformers, and they do chunk images into tokens, and there are multimodal transformers, but none of these are fairly described as an LLM, and they're structurally different than something like ChatGPT. And after the structural changes, it would need to be trained on some new data that associates text sequences and image sequences, and after being optimized in that context you have a _different model_. Does being able to identify images of cats mean the model knows what a cat is? No, and we could have said that a decade ago when deep learning for image classification was making its early first advances. Does being able to describe a cat from video mean you know what the cat is? Probably not, but maybe we're getting closer. Does knowing how to pet a cat mean you know what a cat is? Perhaps not if you need to be instructed to try to pet the cat. But suppose 10 years from now, I have a domestic robot that has a vision system, and a motor control system, and an ability to plan actions and interact with a rich environment. I would say the following would be strong evidence of knowing what a cat is: - it can not only identify or locate the cat, but can label parts of the cat, despite the cat having inconsistent shape. It can consistently pick up the cat in a way which is sensitive and considerate of the cat's anatomy (e.g. not by the head, by one paw, etc) - it can entertain the cat, e.g. with a laser pointer, and can infer whether the cat is engaged, playful, stressed, angry etc - it avoids placing fragile object near high edges, because it can anticipate that the cat is likely to knock them down, even if the cat is not currently near - it can anticipate the cat's behavior and adjust plans around it; e.g. avoid vacuuming the sunny spot by the window in the afternoon when the cat is likely to be napping there - it can anticipate the cat's reactions to stimuli, such as loud noises, a can of food opening, etc, and can incorporate these considerations into plans Note, _none_ of the above have anything to do with language. If I add to the robot a bunch of NLP systems to hear and understand commands or describe its actions or perceptions, it may now know that a cat is called "cat", and how to talk about a cat, but these are distinct from knowing what a cat is. Similarly, - a human with some serious aphasia may be unable to describe the cat, but they can clearly still know what a cat is - a dog can know what a cat is, in many important ways, despite having no language abilities

markmaglana 4y ago

At some point, when multiple components (including the LLM) have been connected to form a system that exhibits "knowing" (the way humans do), wouldn't the "intelligence" be distributed across the entire system rather than attributed primarily to the LLM? In other words, the LLM wouldn't be the equivalent of the human brain. Instead, it would just be equivalent to that part of the human brain that processes language.

grugagag 4y ago
Interesting point that seems quite valid to me. We use different modes of thinking from analytical to emotional, verbal to nonverbal, reactionary, etc. It is possible that LLMs are the key to one of brains modules responsible for producing/processing language but it does not involve or has any knowledge of the other modules necessary for getting closer to human intelligence.

abeppu 4y ago

> The underlying LLM is still the same in all these scenarios. Where is the boundary? No, it's not the same LLM; you'd have to change the LLM in all of those cases. How does it receive input from the GAN? The typical LLM is constructed to literally receive a sequence of encoded tokens. There are vision transformers, and they do chunk images into tokens, and there are multimodal transformers, but none of these are fairly described as an LLM, and they're structurally different than something like ChatGPT. And after the structural changes, it would need to be trained on some new data that associates text sequences and image sequences, and after being optimized in that context you have a _different model_. Does being able to identify images of cats mean the model knows what a cat is? No, and we could have said that a decade ago when deep learning for image classification was making its early first advances. Does being able to describe a cat from video mean you know what the cat is? Probably not, but maybe we're getting closer. Does knowing how to pet a cat mean you know what a cat is? Perhaps not if you need to be instructed to try to pet the cat. But suppose 10 years from now, I have a domestic robot that has a vision system, and a motor control system, and an ability to plan actions and interact with a rich environment. I would say the following would be strong evidence of knowing what a cat is: - it can not only identify or locate the cat, but can label parts of the cat, despite the cat having inconsistent shape. It can consistently pick up the cat in a way which is sensitive and considerate of the cat's anatomy (e.g. not by the head, by one paw, etc) - it can entertain the cat, e.g. with a laser pointer, and can infer whether the cat is engaged, playful, stressed, angry etc - it avoids placing fragile object near high edges, because it can anticipate that the cat is likely to knock them down, even if the cat is not currently near - it can anticipate the cat's behavior and adjust plans around it; e.g. avoid vacuuming the sunny spot by the window in the afternoon when the cat is likely to be napping there - it can anticipate the cat's reactions to stimuli, such as loud noises, a can of food opening, etc, and can incorporate these considerations into plans Note, _none_ of the above have anything to do with language. If I add to the robot a bunch of NLP systems to hear and understand commands or describe its actions or perceptions, it may now know that a cat is called "cat", and how to talk about a cat, but these are distinct from knowing what a cat is. Similarly, - a human with some serious aphasia may be unable to describe the cat, but they can clearly still know what a cat is - a dog can know what a cat is, in many important ways, despite having no language abilities