The stuff they’re calling ai now is just predictive text algorithms. I really can’t wait to stop hearing about this because it is all artificial with no intelligence.
You know it’s funny how many times I’ve heard that “it’s just predictive text algorithms!” As a dismissal that I’m beginning to think we’re just predictive text algorithms.
Once. They do not have the ability to learn or adapt on their own. They are created by humans through “deep learning”, but that is fundamentally different from continuously learning based on one’s own actions and experiences.
LLMs are predictive-associative token algorithms with a degree of randomness and some self-reflection. A key aspect is that anything can be a token, they can self-feed their own output, creating the basis for a thought cycle, as well as output control input for other algorithms. It remains to be seen whether the core of “(human) intelligence” is much more than that, and by how much.
Stable Diffusion is a random image generator that refines its output based on perceptual traits associated with a prompt. It’s like a “lite” version of human dreaming, only with a super-human training set. Kind of an “uncanny valley” version of dreaming.
It just so happens that both algorithms have been showcased at about the same time, and it’s the first time we can build a “set and forget” AI system that can both make decisions about its own next steps, and emulate human creativity… which has driven the hype into overdrive.
I don’t think we’ll stop hearing about it, but I do think there is much more to be done, and it’s pretty much impossible to feed any of the algorithms with human experience data, without registering at least one human learning cycle, as in over many years from inside a humanoid robot.
Not sure if you were unable or unwilling to understand anything of what I wrote, and I don’t like your tone. Feel free to come back with something more serious.
LLMs have been shown to have emergent math capabilities (that are the opposite of what is trained) so you’re simplifying way too much. Yes a lot is just “predictive text” but there’s a ton of “this was not the training and we don’t know how it knows this” as well.
Game of Life has cool emergent properties that are a lot more interesting and fun to play with than LLMs. LLMs also have emergent properties like, for instance, failing classification due to the manipulation of individual image pixels.
AI has been overhyped since it first played tic-tac-toe in the 1950s. One definition of “AI” is: “an algorithm that people don’t understand… yet” 🤷
The stuff they’re calling ai now is just predictive text algorithms. I really can’t wait to stop hearing about this because it is all artificial with no intelligence.
You know it’s funny how many times I’ve heard that “it’s just predictive text algorithms!” As a dismissal that I’m beginning to think we’re just predictive text algorithms.
We are prediction machines, but nothing like chatgpt. Current AI has no ability to learn, adapt, or even consider the future.
BS. The first two are all a neural net does.
Once. They do not have the ability to learn or adapt on their own. They are created by humans through “deep learning”, but that is fundamentally different from continuously learning based on one’s own actions and experiences.
Yeah, once they’re out of training, that’s true. It’s almost like we grow this semi-intelligence, and then run it in something like a deep coma.
I wouldn’t quite say it’s a one-time thing, though. It’s not only possible but typical to put it back in training to finetune it.
Yep. All the reasons cited could pretty much apply to a person as well. GPT-4 is pretty damn smart by every reasonable measure.
Not exactly.
LLMs are predictive-associative token algorithms with a degree of randomness and some self-reflection. A key aspect is that anything can be a token, they can self-feed their own output, creating the basis for a thought cycle, as well as output control input for other algorithms. It remains to be seen whether the core of “(human) intelligence” is much more than that, and by how much.
Stable Diffusion is a random image generator that refines its output based on perceptual traits associated with a prompt. It’s like a “lite” version of human dreaming, only with a super-human training set. Kind of an “uncanny valley” version of dreaming.
It just so happens that both algorithms have been showcased at about the same time, and it’s the first time we can build a “set and forget” AI system that can both make decisions about its own next steps, and emulate human creativity… which has driven the hype into overdrive.
I don’t think we’ll stop hearing about it, but I do think there is much more to be done, and it’s pretty much impossible to feed any of the algorithms with human experience data, without registering at least one human learning cycle, as in over many years from inside a humanoid robot.
Ah, so they produce parts of words instead of whole words at a time. Totally different.
And they’re hooked up to random number generators so if you give it the same input twice you’ll get different output. Totally makes it smarter.
…much like predictive text. Rarely will you find one that doesn’t suggest punctuation on occasion.
…much like predictive text.
Oh, so you can tell it to suggest certain tokens more or less often. How fancy.
I mean, I’d say the ability to visualize things and reason about scenarios it hasn’t experienced before are a good start.
Not sure if you were unable or unwilling to understand anything of what I wrote, and I don’t like your tone. Feel free to come back with something more serious.
LLMs have been shown to have emergent math capabilities (that are the opposite of what is trained) so you’re simplifying way too much. Yes a lot is just “predictive text” but there’s a ton of “this was not the training and we don’t know how it knows this” as well.
Game of Life has cool emergent properties that are a lot more interesting and fun to play with than LLMs. LLMs also have emergent properties like, for instance, failing classification due to the manipulation of individual image pixels.