4 ms·

What is intelligence or generalization if not "sophisticated, complex latent space" navigation?

eigenket 3y ago
As far as I know we don't have much real understanding of how human or even animal intelligence works. It may be that it is entirely based on "sophisticated, complex latent space" navigation or it may be that it has nothing whatsoever to do with that (or anything in-between).
K0balt 3y ago
I think this is exactly what most people are misunderstanding. LLMs are wonderful tools for navigating the sociolinguistic matrix, which embeds the majority of human knowledge both explicitly and covertly with a huge amount of contextual nuance. Transformers are capable of decoding and operating on this covert knowledge as easily as the encyclopedic knowledge that we tend to assume is the “main part” when we write something down. What llms demonstrate is that language covertly encodes logic and algorithmic knowledge that is at least as rich as the “factual” encoding that written content seems to be at face value. LLMs just make this knowledge accessible for computation. What is amazing is that this function alone is capable of producing a simulacrum of agency and intelligence all by itself. This suggests that the human cultural component is a pretty huge part of what we consider to be “human” and without it we’d just be clever apes. It’s the clever ape part that LLMs don’t have, but it’s possible that the transformer model might be able to “ape” most of that as well if applied to the task of existing as an embodied entity in the world (see transformer use in robotic task completion) I would not be terribly surprised if comprehensive multimodality, encompassing the entire spectrum of sensory experience as well as physical environment interactivity, gets us really close to something we could consider AGI. Just as LLMs extract the embedded relationships in written knowledge, the physical and sensory spaces encode an enormous quantity of information that can similarly be generalised to extrapolate a cornucopia of additional concepts (gravity, object permanence, physics in general, relativity, etc). Meaningfully “tokenizing” these spaces will likely be the key to making this work effectively.
- mirekrusin 3y ago
  I find this human-supremacy point of view simply false. Humans are just "aping" more (with large spread within its population by the way). There is nothing special about humans, we're just "aping" slightly more than other animals - computers will arrive and immediately surpass us at our "aping" = what we call "intelligence". This old, false argument with constant goalpost moving will run out of space sooner or later.
  - K0balt 3y ago
    I think you misinterpret my statement, as I too believe that human intelligence is based on the same principles as are being exploited in generative AI. I think there is ample evidence to support that viewpoint, even adversarial images that affect humans in the same way (to a lesser extent) as they do classifiers. I do not believe there is anything special about human intelligence, mostly just that it embodies more complexity than we currently have access to in our training data, and perhaps the hardware required might be expensive, or maybe not really. Interactive / bootstrapping learning is still something we will need to figure out.

As far as I know we don't have much real understanding of how human or even animal intelligence works. It may be that it is entirely based on "sophisticated, complex latent space" navigation or it may be that it has nothing whatsoever to do with that (or anything in-between).

K0balt 3y ago

I think this is exactly what most people are misunderstanding. LLMs are wonderful tools for navigating the sociolinguistic matrix, which embeds the majority of human knowledge both explicitly and covertly with a huge amount of contextual nuance. Transformers are capable of decoding and operating on this covert knowledge as easily as the encyclopedic knowledge that we tend to assume is the “main part” when we write something down. What llms demonstrate is that language covertly encodes logic and algorithmic knowledge that is at least as rich as the “factual” encoding that written content seems to be at face value. LLMs just make this knowledge accessible for computation. What is amazing is that this function alone is capable of producing a simulacrum of agency and intelligence all by itself. This suggests that the human cultural component is a pretty huge part of what we consider to be “human” and without it we’d just be clever apes. It’s the clever ape part that LLMs don’t have, but it’s possible that the transformer model might be able to “ape” most of that as well if applied to the task of existing as an embodied entity in the world (see transformer use in robotic task completion) I would not be terribly surprised if comprehensive multimodality, encompassing the entire spectrum of sensory experience as well as physical environment interactivity, gets us really close to something we could consider AGI. Just as LLMs extract the embedded relationships in written knowledge, the physical and sensory spaces encode an enormous quantity of information that can similarly be generalised to extrapolate a cornucopia of additional concepts (gravity, object permanence, physics in general, relativity, etc). Meaningfully “tokenizing” these spaces will likely be the key to making this work effectively.

mirekrusin 3y ago
I find this human-supremacy point of view simply false. Humans are just "aping" more (with large spread within its population by the way). There is nothing special about humans, we're just "aping" slightly more than other animals - computers will arrive and immediately surpass us at our "aping" = what we call "intelligence". This old, false argument with constant goalpost moving will run out of space sooner or later.
- K0balt 3y ago
  I think you misinterpret my statement, as I too believe that human intelligence is based on the same principles as are being exploited in generative AI. I think there is ample evidence to support that viewpoint, even adversarial images that affect humans in the same way (to a lesser extent) as they do classifiers. I do not believe there is anything special about human intelligence, mostly just that it embodies more complexity than we currently have access to in our training data, and perhaps the hardware required might be expensive, or maybe not really. Interactive / bootstrapping learning is still something we will need to figure out.