Goldman Sachs: AI Is Overhyped, Wildly Expensive, and Unreliable
Goldman Sachs: AI Is Overhyped, Wildly Expensive, and Unreliable
One of the world's largest investment banks wonders if generative AI will be worth the huge investment and hype: "will this large spend ever pay off?"
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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.
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.
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.
LLMs are predictive associative token algorithms
Ah, so they produce parts of words instead of whole words at a time. Totally different.
with a degree of randomness and self reflection.
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.
A key aspect is that anything can be a token
...much like predictive text. Rarely will you find one that doesn't suggest punctuation on occasion.
they can self feed their own output
...much like predictive text.
as well as output control input for other algorithms.
Oh, so you can tell it to suggest certain tokens more or less often. How fancy.
It remains to be seen whether the core of human intelligence is much more than that.
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.