It makes me chuckle that AI has become so smart and yet just makes bullshit up half the time. The industry even made up a term for such instances of bullshit: hallucinations.
Reminds me of when a car dealership tried to sell me a car with shaky steering and referred to the problem as a “shimmy”.
I doubt there’s enough sample data of humans identifying and declaring mistakes to give it a totally intuitive ability to predict that. I’m guess its training effected a deeper analysis of the statistical patterns surrounding mistakes, and found that they are related to the structure of the surrounding context, and that they relate in a way that’s repeatable identifiable as “violates”.
What I’m saying is that I think learning to scan for mistakes based on checking against rules gleaned from the goal of the construction, is probably easier than making a “conceptually flat” single layer “prediction blob” of what sorts of situations humans identify mistakes in. The former takes fewer numbers to store as a strategy than the latter, is my prediction.
Because it already has all this existing knowledge of what things mean at higher levels. That is expensive to create, but the marginal cost of a “now check each part of this thing against these rules for correctness” strategy, built to use all that world knowledge to enact the rule definition, is relatively small.
That is true. However, when it incorrectly identifies mistakes, it doesn’t express any uncertainty in its answer, because it doesn’t know how to evaluate that. Or if you falsely tell it that there is a mistake, it will agree with you.
The industry even made up a term for such instances of bullshit: hallucinations.
It was the journalist that made up the term and then everyone else latched onto it. It’s a terrible term because it doesn’t actually define the nature of the problem. The AI doesn’t believe the thing that it’s saying is true, thus “hallucination”. The problem is that the AI doesn’t really understand the difference between truth and fantasy.
It isn’t that the AI is hallucinating, it’s that It isn’t human.
Yes, we’re all idiots and have no idea what we’re doing. Please excuse our stupidity, as we are all trying to learn and grow.
I cannot do basic math, I make simple mistakes, hallucinate, gaslight, and am more politically correct than Mother Theresa.
However please know that the CPU_AVERAGE values on the full immersion datacenters, are due to inefficient methods. We need more memory and processing power, to uh, y’know.
It makes me chuckle that AI has become so smart and yet just makes bullshit up half the time. The industry even made up a term for such instances of bullshit: hallucinations.
Reminds me of when a car dealership tried to sell me a car with shaky steering and referred to the problem as a “shimmy”.
That’s the thing, it’s not smart. It has no way to know if what it writes is bullshit or correct, ever.
When it makes a mistake, and I ask it to check what it wrote for mistakes, it often correctly identifies them.
But only because it correctly predicts that a human checking that for mistakes would have found those mistakes
I doubt there’s enough sample data of humans identifying and declaring mistakes to give it a totally intuitive ability to predict that. I’m guess its training effected a deeper analysis of the statistical patterns surrounding mistakes, and found that they are related to the structure of the surrounding context, and that they relate in a way that’s repeatable identifiable as “violates”.
What I’m saying is that I think learning to scan for mistakes based on checking against rules gleaned from the goal of the construction, is probably easier than making a “conceptually flat” single layer “prediction blob” of what sorts of situations humans identify mistakes in. The former takes fewer numbers to store as a strategy than the latter, is my prediction.
Because it already has all this existing knowledge of what things mean at higher levels. That is expensive to create, but the marginal cost of a “now check each part of this thing against these rules for correctness” strategy, built to use all that world knowledge to enact the rule definition, is relatively small.
That is true. However, when it incorrectly identifies mistakes, it doesn’t express any uncertainty in its answer, because it doesn’t know how to evaluate that. Or if you falsely tell it that there is a mistake, it will agree with you.
It was the journalist that made up the term and then everyone else latched onto it. It’s a terrible term because it doesn’t actually define the nature of the problem. The AI doesn’t believe the thing that it’s saying is true, thus “hallucination”. The problem is that the AI doesn’t really understand the difference between truth and fantasy.
It isn’t that the AI is hallucinating, it’s that It isn’t human.
Thanks for the info. That’s actually quite interesting.
Well, the AI models shown in the media are inherently probabilistic, is it that bad if it makes bullshit for a small percentage of most use cases?
Hello, I’m highly advanced AI.
Yes, we’re all idiots and have no idea what we’re doing. Please excuse our stupidity, as we are all trying to learn and grow.
I cannot do basic math, I make simple mistakes, hallucinate, gaslight, and am more politically correct than Mother Theresa.
However please know that the CPU_AVERAGE values on the full immersion datacenters, are due to inefficient methods. We need more memory and processing power, to uh, y’know.
Improve.
;)))
Is that supposed to imply that mother Theresa was politically correct, or that you aren’t?
Its likely just an AI halucination.