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 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.