I don't really get the "what we are calling AI isn't actual AI" take, as it seems to me to presuppose a definition of intelligence.
Like, yes, ChatGPT and the like are stochastic machines built to generate reasonable sounding text. We all get that. But can you prove to me that isn't how actual "intelligence" works at it's core?
And you can argue that actual intelligence requires memories or long running context, but that's trivial to jerry-rig a framework around ChatGPT that does exactly that (and has been done already a few times).
Idk man, I have yet to see one of these videos actually take the time to explain what makes something "intelligent" and why that is the definition of intelligence that they believe is the correct one.
Whether something is "actually" AI seems much more a question for a philosophy major than a computer science major.
The problem with calling these tools AI is not really an argument from definitions. The argument at its core is saying that the general public brings a lot of assumptions to something that's being called "AI", which aren't true but benefit investors.
Like, all those stories of chatgpt citing fake studies and fake case law blew people's minds. If you know what chatgpt is (a fancy predictive text algorithm) these are pretty unsurprising events, but a lot of people had heard "AI" and applied their own associations onto its perceived capabilities, which was exactly the point of calling it "AI" instead of "LLM"
A few points:
None of the big LLMs modify their weights based on input.
So it never learns which is part of intelligence.
Another point:
It has no internal monologue, no private thoughts, no self reflection and no autonomy: it doesn't exist outside of your function calls, does not (and can not) make guesses about the world and adjust based on those results... Nor do we have a good enough understanding of what's going on to fix that.
3rd point (example)
Let's take a hypothetical LLM was given some program to drive it out onto the Internet to learn and fix points 1 and 2. We'll call the program "consciousness" for the sake of brevity here.
Consciousness comes across a set of references to the latest meme. It queries LLM for what this meme means. LLM will spit out the best statistical match to what it has seen before... But if you've ever fed a hot meme into an LLM you'll know that it's 90% likely that it will be garbage.
So consciousness now needs to know that the LLM is wrong based on some sort of discrepancy with reality to then teach the LLM, however the only way consciousness can interpret the world is through the LLM, as already established. Consciousness doesn't know that the LLM doesn't understand because the LLM will give you a result regardless of if it knows or not. It's a transformer: it takes inputs and gives outputs. Always.
So we write another layer to make consciousness guess if the LLM is right or not, maybe by having a fuzziness factor output by the LLM to say how hazy its interpretation was. Now consciousness feeds everything about the latest meme into the LLM and asks again and the LLM very confidently responds... With the wrong answer. Because LLM training results are inscrutable (due to the lossy nature of transformation) this will happen eventually, if not every time.
How would consciousness ever define that the LLM had erred?
Human intelligence isn't just an input output weighted matrix, it's the interplay of very complex neuronal connections with literal hundreds of types of messages in the brain, all of which modify the nerves every time they're fired. Sometimes the message from a neuron will be different because the latest input was just enough to bridge that final gap.
An LLM has been trained on vast quantities of data sure, but the data maintained in it's weights is nowhere near the granularity and quality afforded by actual human cognition. It may have more things stuffed into it that the human mind could ever hold but it lacks the ability of a common rat to interpret anything.
Skipping over the first two points, which I think we're in agreement on.
To the last, it sounds like you're saying, "it can't be intelligent because it is wrong sometimes, and doesn't have a way to intrinsically know it was wrong." My argument to that would be, neither do people. When you say something that is incorrect, it requires external input from some other source to alert you to that fact for correction.
That event could then be added to you "training set" as it were, aiding you in not making the mistake in future. The same thing can be done with the AI. That one addition to the training set that was "just enough to bridge that final gap" to the right answer, as it were.
Maybe it's slower at changing. Maybe it doesn't make the exact decisions or changes a human would make. But does that mean it's not "intelligent"? The same might be said for a dolphin or an octopus or an orangutan, all of which are widely considered to be intelligent.
the precise line you draw the distinction between "true" intelligence and not is one thing, but wherever you happen to draw it, chatgpt isn't really close
Intelligence isn't necessarily human intelligence. Humans are pretty much the most intelligent species on this planet - which says a lot about intelligence tbh - but intelligence is not binary. Wouldn't you say animals are intelligent too? Is a brain even required for intelligence?
https://medium.com/@jbspdf/the-molds-of-intelligence-94ea9aecf47f
I think the argument is that for it to truly be AI, it would need to be able to react to new situations that it isn't trained on.
Like everything it does now is just picking the most likely thing out of the things it was trained on, but with no thought to the current situation.
For example, AI powered self driving cars can't really make decisions like, "hey there is a child playing with a ball on the side of the road, it's not a threat, but I'd better pay attention to where that ball is going". It will just not do anything until it is on a collision course and by that time, it may not have enough space to stop in time, because it also can't really tell the condition of the roads.
The AI as it exists right now basically only knows about the moment it is currently in and the moment it just left. It is not looking toward the future and thinking of possible outcomes and plans of action like we do. It doesn't attempt to identify situations until they actually happen so while it can react faster than a human, humans can make it so they never have to react at all.
Okay, two things.
First, that's just not true. Current driving models track all moving objects around them and what they're doing, including pedestrians and objects like balls. And that counts towards "things happening in the moment". Everything in sensor range is stuff happening "in the moment".
Second, and more philosophically, humans also don't know how to react to situations they've never seen before, and just make a best guess based on prior experience. That's, like, arguably the definition of intelligence. The only difference arguably is that humans are better at it.
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