Because the whole thing is, a dog’s abstract mental capabilities are far below a human — thus why it would be ASTOUNDING that a dog could master even a primitive form of speaking English.
On the other hand, here we are brute forcing a solution from analyzing millions of man-years of published English speech, by using a huge array of computing power to precompute various answers and sift them.
It is a bit like “solving checkers” and then claiming “wow, a dog could have done this”. It is like making a cheat sheet for a test by analyzing all answers ever given, and then summarizing them in a vector of 102827 dimensions, and claiming that it is the same as coming up with clever and relevant one liners on the spot using the brain of a dog.
Excuse my presumption, but it seems that you arrive at the logical conclusion "it might be possible to simulate intelligence with a sufficiently big cheat sheet" - and then you disregard it because you're uncomfortable with it. We already know this is the case for specialized environments, so the "only" question left is how far does this generalize.
In my opinion, more ridiculous claims have already been proven by science (for example Quantum Mechanics).
Also you have to make a distinction between the optimizing process (evolution/training neural nets) and the intelligent agent itself (human/machine intelligence).
I don’t disregard it. It isn’t about discomfort. In fact, I think that “solving checkers” is very useful, if your goal is to get the highest quality answers in checkers.
The problem I have is comparing that to having a dog speak English. It’s totally wrong. You had access to all these computing resources, and the sum total of millions of work by humans. You didn’t bootstrap from nothing like AlphaZero did, but just remixed all possible interesting combinations, then selected the ones you liked. And you try to compare this “top down” approach to a bottom-up one?
The top down approach may give BETTER answers and be MORE intelligent. But the way it arrives at this is far less impressive. In fact, it would be rather expected.
The vocal apparatus is not there, but there is certainly more cognition than what people think dogs have (there's a question that I wonder if language enables thought or if thought enables language)
I'm not so certain. Seems like the owner is doing a lot of work to make sense out of those utterances. I'd like to see Bunny say what he's about to do, then do it. Or watch his owner do something with something, then describe it.
edit: or just have a conversation of any kind longer than a 2 minute video. Or one without the owner in the room, where she talks back with the dog using the same board. That would at least be amenable to Turing.
edit2: here's a test - put headphones on the owner and block her vision of the board. Sometimes pipe in the actual buttons the dog is pressing, other times pipe in arbitrary words. See if the owner makes sense of the random words.
I'm trying, but I don't see it at all with these examples.
1) just seemed like random pressing until the dog pressed "paw", then the owner repeated loudly "something in your paw?" The dog presented its paw, then the owner decided "hurt" "stranger" "paw" was some sort of splinter she found there. The dog wasn't even limping.
2) I didn't get any sense of the presses relating to anything the dog was doing, and since the owner was repeating loudly the thing she wanted the dog to find, I was a bit surprised. Then the dog presses "sound," the owner connects this with a sound I can't hear, then they go outside to look for something I can't see.
Billie the Cat: I simply saw no connection between the button presses and anything the cat did. The cat pressed "outside" but didn't want to go outside. The cat presses "ouch noise" and the owner asks if a sound I didn't hear hurt her ears. Then the cat presses "pets" and the owner asks if the cat wants a pet? The cat presses "noise" and the owner continues the monologue apologizing for the painful noise and offering to buy her cat a pet. Sorry to recount most of the thing, but I don't get it at all.
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Not trying to debunk talking pets, but I'm not seeing anything here. I at least expected the dog to be smart enough to press particular buttons for particular things, but I suspect the buttons are too close together for it to reliably distinguish them from each other. I'd be pretty easy to convince that you could teach a dog to press a button to go outside, a different button when they wanted a treat, and a different button when they wanted their belly rubbed. In fact I'd be tough to convince that you couldn't teach a dog to do that. Whatever's being claimed here, however, I'm not seeing.
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edit: to add a little more, I'm not even sure that *I* could reliably do what they're claiming the dog is doing. To remember which button is which without being able to read is like touch typing, but worse because the buttons seem to be mounted on re-arrangeable puzzle pieces. Maybe I could associate the color of those pieces with words, but that would only cover the center button on each piece.
If a dog were using specific buttons for language (or if I were doing the same thing) I'd expect the dog to press a lot of buttons, until he heard the sound he was looking for, then to press that button over and over. Not to just walk straight to a button and press.
I think the cat just presses the buttons when it wants the owner to come, and presses them again when the owner says something high pitched at the end and looks at it in expectation.
I saw a lot of videos about this Bunny dog on tiktok, but discarded it as a gimmick, not believing it's real. Your comment motivated me to look into it more (30 seconds of time).
This NYT article at least does not discredit it [0]. Have you looked more into it? Do you think it would be useful to train your dog to do it?
With my cat (I've got the buttons, haven't done anything with them yet) its would be useful to find out if he wants food, attention, or is complaining about the water bowl or litter box.
Even being able to distinguish those would be a "win".
Current machine learning models have around ~100B parameter, human brain has ~100T synapses. Assuming one DNN parameter is equivalent to 1 synapse, then the biggest models are still 1000 times smaller than human brain.
Cat or dog would have around ~10T synapses.
AlphaCode has ~50B parameters, that is 20 times less than number of synapses in a mouse brain ~1T. Honey bee has ~1B synapses.
So AlphaCode would be somewhere between a honey bee and a domestic mouse.
Because the whole thing is, a dog’s abstract mental capabilities are far below a human — thus why it would be ASTOUNDING that a dog could master even a primitive form of speaking English.
On the other hand, here we are brute forcing a solution from analyzing millions of man-years of published English speech, by using a huge array of computing power to precompute various answers and sift them.
It is a bit like “solving checkers” and then claiming “wow, a dog could have done this”. It is like making a cheat sheet for a test by analyzing all answers ever given, and then summarizing them in a vector of 102827 dimensions, and claiming that it is the same as coming up with clever and relevant one liners on the spot using the brain of a dog.
NO. It’s not nearly as astounding or impressive.