Sure, there is a chance the exact question had been asked before, and answered, but we are talking remote possibilities here.
that any description provided for it didn’t map it to another object that would behave similarly when stacking.
If it has to say ‘this item is like that other item and thus I can use what I’ve learned about stacking that other item to stack this item’ then I would absolutely argue that it is reasoning and not just “predicting text” (or, again, predicting text might be the equivalent of reasoning).
Stacking things isn’t a novel problem.
Sure, stacking things is not a novel problem, which is why we have the word “stack” because it describes something we do. But stacking that list of things is (almost certainly) a novel problem. It’s just you use what you’ve learned and apply that knowledge to this new problem. A non-novel problem is if I say “2+2 = 4” and then turn around and ask you “what does 2 + 2 equal?” (Assuming you have no data set) If I then ask you “what’s 2 + 3?” that is a novel problem, even if it’s been answered before.
I mean, I can’t dismiss that it isn’t doing something more complex, but examples like that don’t convince me that it is. It is capable of very impressive things, and even if it needs to regurgitate every answer it gives, few problems we want to solve day to day are truly novel, so regurgitating previous discussions plus a massive set of associations means that it can map a pretty large problem space to a large solution space with high accuracy.
How are you convinced that humans are reasoning creatures? This honestly sounds like you could be describing 99.99% of human thought, meaning we almost never reason (if not actually never). Are we even reasonable?
I don’t mean it needs to be the exact question, just something with equivalence. If someone talked about stacking boards and other things, the board could go on the bottom and then maybe someone else talked about stacking balls and books that way, so it used that because “eggs” were associated with “round”. Follow up with the nail thing from another conversation.
It’s definitely a form of intelligence, but I don’t think it’s anywhere close to 99.9% of human thought. I think it’s missing entire dimensions of thought.
I’m not saying it’s 99.9% of human intelligence, I’m saying you’re describing 99.9% of human thought.
This is what humans do, we hear about something thing and then we learn how to apply it to another. You even mention here “stacking balls” and then making the connection that eggs are also round and would need to be stacked in the same way to prevent rolling. This is reasoning, using what you’ve learned and applying it to a novel problem.
What you are describing as novel problems are really just doing the same thing at a completely different level. Like I play soccer, but no matter how much I trained, there is no way I would ever reach Messi’s skill, because he was just born with special skill in that area, but still just human like the rest of us.
And remember I’m mostly just pointing to the “text predictor” claim. I’m not convinced it’s not, and I think that appeared true for early models, but not so easy to apply to current models.
Yeah, it is hard to say if the “glorified text predictor” is completely accurate, since the sheer size of the model allows for some pretty deep connections.
And, thinking about it since making that post, it’s hard to say for sure that even Einstein or Newton were doing anything differently or were just the first/most famous to put those particular things together.
Sure, there is a chance the exact question had been asked before, and answered, but we are talking remote possibilities here.
If it has to say ‘this item is like that other item and thus I can use what I’ve learned about stacking that other item to stack this item’ then I would absolutely argue that it is reasoning and not just “predicting text” (or, again, predicting text might be the equivalent of reasoning).
Sure, stacking things is not a novel problem, which is why we have the word “stack” because it describes something we do. But stacking that list of things is (almost certainly) a novel problem. It’s just you use what you’ve learned and apply that knowledge to this new problem. A non-novel problem is if I say “2+2 = 4” and then turn around and ask you “what does 2 + 2 equal?” (Assuming you have no data set) If I then ask you “what’s 2 + 3?” that is a novel problem, even if it’s been answered before.
How are you convinced that humans are reasoning creatures? This honestly sounds like you could be describing 99.99% of human thought, meaning we almost never reason (if not actually never). Are we even reasonable?
I don’t mean it needs to be the exact question, just something with equivalence. If someone talked about stacking boards and other things, the board could go on the bottom and then maybe someone else talked about stacking balls and books that way, so it used that because “eggs” were associated with “round”. Follow up with the nail thing from another conversation.
It’s definitely a form of intelligence, but I don’t think it’s anywhere close to 99.9% of human thought. I think it’s missing entire dimensions of thought.
I’m not saying it’s 99.9% of human intelligence, I’m saying you’re describing 99.9% of human thought.
This is what humans do, we hear about something thing and then we learn how to apply it to another. You even mention here “stacking balls” and then making the connection that eggs are also round and would need to be stacked in the same way to prevent rolling. This is reasoning, using what you’ve learned and applying it to a novel problem.
What you are describing as novel problems are really just doing the same thing at a completely different level. Like I play soccer, but no matter how much I trained, there is no way I would ever reach Messi’s skill, because he was just born with special skill in that area, but still just human like the rest of us.
And remember I’m mostly just pointing to the “text predictor” claim. I’m not convinced it’s not, and I think that appeared true for early models, but not so easy to apply to current models.
Yeah, it is hard to say if the “glorified text predictor” is completely accurate, since the sheer size of the model allows for some pretty deep connections.
And, thinking about it since making that post, it’s hard to say for sure that even Einstein or Newton were doing anything differently or were just the first/most famous to put those particular things together.
It’s a weird world and cool to think about. Thanks for the civil and interesting discussion.
Yeah, it was a good talk. Funny watching the sentiment of the voters throughout, too.
IMO, one of the best QoL updates for Lemmy is to make the votes invisible.
I suggest the personal firmware update of stopping caring about downvotes. Then they just become interesting data points.
I dont personally care about downvotes, but those data points sure do shake my faith in humanity sometimes.