I can’t quite wrap my head around this, these systems were coded, written by humans to call functions, assign weights, parse data. How do we not know what it’s doing?
It’s a bit of “emergent properties” - so many things are happening under the hood they don’t understand exactly how it’s doing what it’s doing, why one type of mesh performs better on a particular class of problems than another.
The equations of the Lorenz attractor are simple, well studied, but it’s output is less than predictable and even those who study it are at a loss to explain “where it’s going to go next” with any precision.
Same way anesthesiology works. We don’t know. We know how to sedate people but we have no idea why it works. AI is much the same. That doesn’t mean it’s sentient yet but to call it merely a text predictor is also selling it short. It’s a black box under the hood.
Writing code to process data is absolutely not the same way anesthesiology works 😂 Comparing state specific logic bound systems to the messy biological processes of a nervous system is what gets this misattribution of ‘AI’ in the first place. Currently it is just glorified auto-correct working off statistical data about human language, I’m still not sure how a written program can have a voodoo spooky black box that does things we don’t understand as a core part of it.
The uncertainty comes from reverse-engineering how a specific output relates to the prompt input. It uses extremely fuzzy logic to compute the answer to “What is the closest planet to the Sun?” We can’t know which nodes in the neural network were triggered or in what order, so we can’t precisely say how the answer was computed.
Yeah, there’s a mysticism that’s sprung up around LLMs as if they’re some magic blackbox, rather than a well understood construct to the point where you can buy books from Amazon on how to write one from scratch.
It’s not like ChatGPT or Claude appeared from nowhere, the people who built them do talks about them all the time.
EDIT: Sorry, I’ll expand. When AI researchers give talks about how AI works, they say things like, “on a fundamental level, we don’t actually know what’s going on.”
Also, even if there are books available about how to write an AI from scratch(?) somehow, the basic understanding of what happens deep within the neural networks is still a “magic black box”. They’ll crack it open eventually, but not yet.
The ideas that people have that AI is simple and stupid & a passing fad are naive.
If these AI researchers really have no idea how these things work, then how can they possibly improve the models or techniques?
Like how they now claim all that after upgrades that now these LLMs can “reason” about problems, how did they actually go and add that if it’s a black box?
I can’t quite wrap my head around this, these systems were coded, written by humans to call functions, assign weights, parse data. How do we not know what it’s doing?
It’s a bit of “emergent properties” - so many things are happening under the hood they don’t understand exactly how it’s doing what it’s doing, why one type of mesh performs better on a particular class of problems than another.
The equations of the Lorenz attractor are simple, well studied, but it’s output is less than predictable and even those who study it are at a loss to explain “where it’s going to go next” with any precision.
Same way anesthesiology works. We don’t know. We know how to sedate people but we have no idea why it works. AI is much the same. That doesn’t mean it’s sentient yet but to call it merely a text predictor is also selling it short. It’s a black box under the hood.
Writing code to process data is absolutely not the same way anesthesiology works 😂 Comparing state specific logic bound systems to the messy biological processes of a nervous system is what gets this misattribution of ‘AI’ in the first place. Currently it is just glorified auto-correct working off statistical data about human language, I’m still not sure how a written program can have a voodoo spooky black box that does things we don’t understand as a core part of it.
The uncertainty comes from reverse-engineering how a specific output relates to the prompt input. It uses extremely fuzzy logic to compute the answer to “What is the closest planet to the Sun?” We can’t know which nodes in the neural network were triggered or in what order, so we can’t precisely say how the answer was computed.
Yeah, there’s a mysticism that’s sprung up around LLMs as if they’re some magic blackbox, rather than a well understood construct to the point where you can buy books from Amazon on how to write one from scratch.
It’s not like ChatGPT or Claude appeared from nowhere, the people who built them do talks about them all the time.
What a load of horseshit lol
EDIT: Sorry, I’ll expand. When AI researchers give talks about how AI works, they say things like, “on a fundamental level, we don’t actually know what’s going on.”
Also, even if there are books available about how to write an AI from scratch(?) somehow, the basic understanding of what happens deep within the neural networks is still a “magic black box”. They’ll crack it open eventually, but not yet.
The ideas that people have that AI is simple and stupid & a passing fad are naive.
If these AI researchers really have no idea how these things work, then how can they possibly improve the models or techniques?
Like how they now claim all that after upgrades that now these LLMs can “reason” about problems, how did they actually go and add that if it’s a black box?