4 ms·

It's the difference between textual mimicry, and what we humans do, which is communication. We conceive of an idea, a concept, that we wish to communicate, and

It's the difference between textual mimicry, and what we humans do, which is communication. We conceive of an idea, a concept, that we wish to communicate, and the brain then pulls together appropriate words/symbols as well as the syntactic/semantic rules that we have internalized over decades of communicating to create a statement that accurately represents the idea. If I want to communicate the fact that X and Y are disparate things, I know that "not" is a symbol that can be used to signify this relationship. The core of the difference to me (admittedly not an AI researcher) is intentionality. Humans conceive of an intention, then a communication. This is not what models like GPT-3 do as there is no intentionality present. GPT-3 can create some truly freaky texts but most that I've seen longer than a few sentences suffer from a fairly pronounced uncanny valley effect due to that lack of intention. It's also why I (again, recognizing my lack of expertise) think expecting GPT-3 to do things like provide medical advice is a fool's errand.

tikwidd 5y ago
I think you're right that it's mimicry, but I'd like to offer a more precise distinction about the difference between humans and GPT-3. Threads, network adapters, web browsers and primitive cells communicate too. What I think humans do uniquely is create thoughts, such as "X and Y are disparate things". Those thoughts might be for communication, or just thinking about something. But AI models are only trained on the thoughts that happen to be communicated. More accurately, they are only trained on the externalised side effects of the thought function, i.e. what gets written down or spoken. It's like if you were building a physiological model of the human body using only skin and exterior features as training data. We would not expect the model to learn the structure and function of the spleen. By analogy we should not expect GPT-3 to learn the structure and function of thought.
- bangkoksbest 5y ago
  One thing I actually liked about the Stanford workshop that accompanied this white paper, was the emphasis on, what in physics we often summed up as, “More is different”[1] Basically, it's the principle that drastic qualitative change and stable structures commonly appear as you scale up base units, which would be almost impossible to predict when you just have a unit level understanding. I.e. qualitative structure that emerges, let's say, when a certain system has 100 million units does not do so linearly such that if you see a system as it scales from 1 unit to 1 million units you would have any evidence of the emergent behavior at 100 million. It is irrelevant when folks point out "but human cognition isn't any different at its base than the machine" because we can very clearly see there is a massive qualitative difference in behaviors, and there is a wide gulf in architectures and development that there is no reason whatsoever to expect a qualitatively unique (so far as we can tell) behavior as conscious language use to ever emerge in a computer model. It's pretty remarkable that the only other cluster of biological systems that can even physiologically mimic it are songbirds/parrots who come from a very very different part of the phylogenetic tree. Who could ever predict that aberrant homology if I just gave you the 4 nucleotides? More is different. You don't get complex structure by just crudely analogizing and reducing everything to base parts. [1] Anderson, Philip W. "More is different." Science 177, no. 4047 (1972): 393-396.

tikwidd 5y ago

I think you're right that it's mimicry, but I'd like to offer a more precise distinction about the difference between humans and GPT-3. Threads, network adapters, web browsers and primitive cells communicate too. What I think humans do uniquely is create thoughts, such as "X and Y are disparate things". Those thoughts might be for communication, or just thinking about something. But AI models are only trained on the thoughts that happen to be communicated. More accurately, they are only trained on the externalised side effects of the thought function, i.e. what gets written down or spoken. It's like if you were building a physiological model of the human body using only skin and exterior features as training data. We would not expect the model to learn the structure and function of the spleen. By analogy we should not expect GPT-3 to learn the structure and function of thought.

bangkoksbest 5y ago
One thing I actually liked about the Stanford workshop that accompanied this white paper, was the emphasis on, what in physics we often summed up as, “More is different”[1] Basically, it's the principle that drastic qualitative change and stable structures commonly appear as you scale up base units, which would be almost impossible to predict when you just have a unit level understanding. I.e. qualitative structure that emerges, let's say, when a certain system has 100 million units does not do so linearly such that if you see a system as it scales from 1 unit to 1 million units you would have any evidence of the emergent behavior at 100 million. It is irrelevant when folks point out "but human cognition isn't any different at its base than the machine" because we can very clearly see there is a massive qualitative difference in behaviors, and there is a wide gulf in architectures and development that there is no reason whatsoever to expect a qualitatively unique (so far as we can tell) behavior as conscious language use to ever emerge in a computer model. It's pretty remarkable that the only other cluster of biological systems that can even physiologically mimic it are songbirds/parrots who come from a very very different part of the phylogenetic tree. Who could ever predict that aberrant homology if I just gave you the 4 nucleotides? More is different. You don't get complex structure by just crudely analogizing and reducing everything to base parts. [1] Anderson, Philip W. "More is different." Science 177, no. 4047 (1972): 393-396.