81–90 of 126 posts
One can casually observe that LLMs quite excel at composition though: gluing together pieces of knowledge in ways no one did before (examples: a program that does X using language Y, a painting that mashes up two themes). Most knowledge workers' activities aren't innovative or imitational - similarly, we compose stuff, so LLMs are a fair competitor.
As I see it the AI schism is more about the debate between functionalism/computationalism and the idea that the chinese room thought experiment was an argument for, "biological naturalism". There is a lot of effort dedicated to showing that AIs dont have some innate quality called "consciousnes" or "sentience" or what have you. There is not just a lot of effort to show that, but also to show that that is somehow a li…
Consciousness and sentience are just poorly defined vocabulary that delude people into thinking it's meaningful categorizations. There's just a bunch of traits related to intelligence and we categorize that if something has enough of those traits then it's "alive". But the words "consciousness" and "sentience" are so poorly defined that we can't pinpoint the formal grouping of what these traits actually are. So for a…
How could a statistical system trained on data display intelligence let alone innovation? It's just very good statistics at the end of the day. Artificial Perception is about as far as you can get with ml/dl tech if you point the sensors at the world of space-time. If you point it at words, as with LLM, you get statistics about words - that is, no actual understanding of what the words model in the minds of the origi…
Also I’ve just gave instructions to gpt4 rot13 encoded, and it followed the instructions. I don’t really care if “it” understood what it was doing, but the responses were good enough for me to be impressed.
Also I’ve got quite a lot of use cases where I get reliable value out of it, what were you trying to do that made you conclude it is “utterly unreliable”?
Earlier quoted context omitted.
All innovation is composition plus random generation. Which LLMs already do. LLMs have a rudimentary form of innovation. It's not quite as good as humans but it's getting there.
All innovation is composition plus random generation. That’s a fairly bold claim. What supporting data do you have to justify it?
One is data driven based on evidence.
The other is logic driven based on axioms and the logical implications of said axioms.
My statement is derived from the later. Therefore evidence is unnecessary. It is niave to blindly faith in all truth in the hands of data without understanding nuances between the relationship of data and logic.
If you have a pure function that takes the input, the output of that function has 3 possible outcomes.
1. The output of that function is some transformation of the input.
2. The output of the function has nothing to do with the input and is thus generated randomly generated.
3. The output of the function is a combination of both random generation and input transformation.
In this case your brain is the function. Input parameters are 100 percent of your existing knowledge. That includes genetic knowledge such as instinctual/rational processing behaviors evolved into the structure of your brain through evolution and learned knowledge such as what you gain when you read a book.
The result of the "innovation" operation performed by the brain includes novel output by definition. Thus by logic it must be must include components that are from a certain perspective randomly generated.
I guess at first glance it doesn't appear randomly generated because we vet the output and verify it and iterate over several pieces of randomly generated information. Additionally it's not completely random as we only try and test ideas within the realm of possibility. For example I'm not going to account for the possibility that my car will transform into a rock tomorrow that's just too random.
But make no mistake. Innovation must be partly randomly generated. Even the idea itself of composing two existing components of knowledge together is itself randomly generated.
That being said if we want to be pedantic, there's no real known way to randomly generate stuff via an algorithm. So I use the term "randomly generate" very loosely. Think of it in a similar way to random number generation on your computer: Random from a practical perspective but not technically random.
Earlier quoted context omitted.
As the query prompter you are in control of the feedback loop. You can ask the AI to re-examine it's output to catch errors just as a human would do for himself. Practically speaking this does work to a limited extent. Sometimes the AI just sticks with it's guns and runs with it just like a human might.
Sure, but I was addressing the issue of why "hallucinations" occur in the first place, and how to fix them. Having a human in the loop does seem pretty much required at the moment, but it doesn't help when asking the AI for the answer to something you don't know, and therefore not being able to realize that the confident answer was hallucinated and wrong. Of course multiple-answer quizzes are easier to get right by p…
Because the input is generic you can make this automated. When a user makes a query create a feedback loop and feed that back into the neural network multiple times with additional input requests to re-analyze analyze the query and resulting output more carefully. You can do this until you exhaust all input nodes and it "forgets" what you talked about previously.
Innovation is simply a result of trying to imitate, but adding errors. That's how humans do it. Add in some darwinism so that the best 'innovations' survive. Made a mistake in making food? Oh, that's a new recipe. Can't really remember how to tell the story? Well, that's a new story. Accidentally kicked a ball while trying to just walk? I just invented soccer. And so on.
Earlier quoted context omitted.
Sure, but I was addressing the issue of why "hallucinations" occur in the first place, and how to fix them. Having a human in the loop does seem pretty much required at the moment, but it doesn't help when asking the AI for the answer to something you don't know, and therefore not being able to realize that the confident answer was hallucinated and wrong. Of course multiple-answer quizzes are easier to get right by p…
Oh you misunderstand. When you query the LLM for the second iteration of the loop just do it regardless. Say something generic, ask it to reanalyze the answer more carefully. Ask it to compare it with existing known data and check for logical consistency. You can do this EVEN if you don't know whether or not the answer is wrong. Because the input is generic you can make this automated. When a user makes a query creat…
"found to Excel" on a positive note: at least HN didn't use an LLM on that headline
[EDIT: Now changed, so this thread makes less sense, but the original headline was "Artificial intelligence systems found to Excel at ..."]
One can casually observe that LLMs quite excel at composition though: gluing together pieces of knowledge in ways no one did before (examples: a program that does X using language Y, a painting that mashes up two themes). Most knowledge workers' activities aren't innovative or imitational - similarly, we compose stuff, so LLMs are a fair competitor.
One can casually observe that LLMs quite excel at composition though: gluing together pieces of knowledge in ways no one did before (examples: a program that does X using language Y, a painting that mashes up two themes). Most knowledge workers' activities aren't innovative or imitational - similarly, we compose stuff, so LLMs are a fair competitor.
All innovation is composition plus random generation. Which LLMs already do. LLMs have a rudimentary form of innovation. It's not quite as good as humans but it's getting there.
Human innovation can arise by using these experiences as source data for composition of text or images. LLMs, by contrast, are limited to training on text and images/video exclusively.