Thanks for sharing your thoughts @philosophybear. I found it helpful to interact with your thoughts. Here are a couple of comments.
I think the Great Palm lacks only one thing, the capacity for continuous learning- the capacity to remember the important bits of everything it reads, and not just in its training period. If Great Palm (GPT-3+PaLM540B) had that ability, it would be an AGI.
Let’s see if I can find a counter-example to this claim.
Would Great Palm be capable of performing scientific advancement? If so, could you please outline how you’re expecting it to do that?
Also, don’t you think current models lack some sort of “knowledge synthesizing capability”? After all, GPT and PALM have been trained on a lot of text. There are novel insights to be had from having read tons of biology, mathematics, and philosophy that no one ever saw in that combination.
Also, would are you leaving out “proactive decision making” from your definition on purpose? I expect a general intelligence (in the AI safety-relevant context) to want to shape the world to achieve a goal through interacting with it.
Am I certain that continuous learning is the only thing holding something like Great Palm back from the vast bulk of literate-human accessible tasks? No, I’m not certain. I’m very open to counterexamples if you have any, put them in the comments. Nonetheless, PaLM can do a lot of things, GPT-3 can do a lot of things, and when you put them together, the only things that stand out to me as obviously and qualitatively missing in the domain of text input, and text output involve continuous learning
You talk a lot about continuous learning but fail to give a crisp definition of what that would mean. I have difficulty creating a mental image (prototypical example) of what you’re saying. Can you help me understand what you mean?
Also, what exactly do you mean by mixing GPT-3 with PALM? What fundamental differences in their method can you see that would enhance the respective other model if applied to it?
But to me, these aren’t really definitions of AGI. They’re definitions of visual, auditory and kinaesthetic sensory modality utilizing AGI. Putting this as the bar for AGI effectively excludes some disabled people from being general intelligences, which is not desirable!
It seems like the 2 definitions you’re summoning are concrete and easy to measure. In my view, they are valuable yardsticks by which we can measure our progress. You’re lamenting about these definitions but don’t seem to be providing one yourself. I appreciate that you pointed out the “shifting bar” phenomenon and think that this is a poignant observation. However, I’d like to see you come up with a crisper definition of your own.
Lastly, a case can be made that the bar isn’t actually shifting. It might just be the case that we didn’t have a good definition of a bar for AGI in the first place. Perhaps there was a problem with the definition of a bar for AGI not with its change.
Thanks for sharing your thoughts @philosophybear. I found it helpful to interact with your thoughts. Here are a couple of comments.
Let’s see if I can find a counter-example to this claim.
Would Great Palm be capable of performing scientific advancement? If so, could you please outline how you’re expecting it to do that?
Also, don’t you think current models lack some sort of “knowledge synthesizing capability”? After all, GPT and PALM have been trained on a lot of text. There are novel insights to be had from having read tons of biology, mathematics, and philosophy that no one ever saw in that combination.
Also, would are you leaving out “proactive decision making” from your definition on purpose? I expect a general intelligence (in the AI safety-relevant context) to want to shape the world to achieve a goal through interacting with it.
You talk a lot about continuous learning but fail to give a crisp definition of what that would mean. I have difficulty creating a mental image (prototypical example) of what you’re saying. Can you help me understand what you mean?
Also, what exactly do you mean by mixing GPT-3 with PALM? What fundamental differences in their method can you see that would enhance the respective other model if applied to it?
It seems like the 2 definitions you’re summoning are concrete and easy to measure. In my view, they are valuable yardsticks by which we can measure our progress. You’re lamenting about these definitions but don’t seem to be providing one yourself. I appreciate that you pointed out the “shifting bar” phenomenon and think that this is a poignant observation. However, I’d like to see you come up with a crisper definition of your own.
Lastly, a case can be made that the bar isn’t actually shifting. It might just be the case that we didn’t have a good definition of a bar for AGI in the first place. Perhaps there was a problem with the definition of a bar for AGI not with its change.