To elaborate a little:
Since many people are unable to tell the difference between a “real human” and an AI, they have been documented “going rogue” and acting outside of parameters, they can lie, they can compose stories and pictures based on the training received. I can’t see AI as less than human at this point because of those points.
When I think about this, I think about that being the reason as to why we cannot create so called “AGI” because we have no proper example or understanding to create it and thus created what we knew. Us.
The “hallucinating” is interesting to me specifically because that seems what is different between the AI of the past, and modern models that acts like our own brains.
I think we really don’t want to accept what we have already accomplished because we don’t like looking into that mirror and seeing how simple our logical process’ are mechanically speaking.
AI is a very broad term that includes more than machine learning. Assuming you mean LLM
The differences are:
- it does not learn from experience like humans do. They learn by training, which is separate from conversation where context window is limited
- they only learn from text (thus name Language Model), so they do not understand other inputs like touch, sight, sound, taste, and many others
- they do not think critically, take all input at face value, particularly that LLM cannot corroborate input information with experience from real world
Also if you cannot tell difference between real human and AI it’s only because your interaction with AI is limited to text. If you can meet it like a real human, it’ll be obvious that it’s a computer not a person. If the image is blurry/pixelated enough, you couldn’t tell a car from a house, that doesn’t mean cars have become indistinguishable from houses.
To add to this, this is how llm sessions ‘get around’ the experience issue: with every query/command/whatever the whole context and passed conversation is sent to it to be reprocessed. This is why in long sessions it takes longer and longer to generate a new response and why it will forget everything it ‘learned’ from your session when starting a new one
I can’t tell if you are serious, but as someone with a master in CS and some basic experience in neuroscience, I want to clarify a two things:
- AIs can’t lie, because they neither know nor understand things
- AIs are not like us and you can’t make them like us yet, because we don’t even fully understand how we work
While the mechanisms of hallucination aren’t the same, it can absolutely and does happen with humans. Somebody ever tell you something they thought to be true and it wasn’t? I’m sure you’ve even done it yourself. Maybe later you realize that it might not be true or you got something confused with something else in your fleshy Rolodex.
Lies are deliberate, hallucinations are mistakes. Talking about lying AIs implies that they have something akin to free will or an intrinsic motivation, which they simply do not have. They are an emotionless tool designed to give good answers. It is comparable to claiming the mechanism for generating speech in the human brain comes up with lies, which it obviously doesn’t, it just articulates them.
I’m not saying humans can’t hullacinate, I am saying it is not the same as lying and that AIs can’t lie.
Look up a story from 2023 in which OpenAI researchers apparently experienced GPT4 lying to solve a captcha.
I think the difference comes from understanding. When we inferior, fleshy ones “make up” information, it’s usually based on our understanding (or misunderstanding) of the subject at hand. We will fill in the blanks in our knowledge with what we know about similar subjects.
An LLM doesn’t understand its output, though. All it knows is that word_string_x immediately follows word_string_y in 84.821% of its training data, so that’s what gets pasted next.
For us, making up false information comes from gaps in our cognition, from personal agendas, our own unique lived experiences, etc. For an LLM, these are just mathematical anomalies.
AI is being trained on human output. Has anyone given thought to how deranged human output is?
techbros: look at the em-dashes! Only AI’s give out that output
meanwhile in casually-dropping-50k-words-recreationally-land:
AI are trained on real world data, just not their data.
we don’t let AI sleep. of course it’s growing psychotic.
just turn the shit off for a few days and see if it helps. you’d want a nap too.
From a theoretical perspective, it is entirely possible for code to simulate the activity of a human brain by simulating every neuron. And there would be deep philosophical questions to ask about the nature of thought and consciousness, is an electronic brain truly any different from a flesh one?
From a practical perspective, current technology simply isn’t there yet. But it’s hard to even describe the gap between how a LLM operates and how we operate, because our understandings of both LLMs and ourselves are honestly both very poor. Hard to say more than just… no, they’re not alike. Best I can explain is just that LLMs are not written to behave like human brains to begin with, they’re an entirely different kind of thing, which results in a different set of shortcomings compared to our own shortcomings.
That depends on how hardcore of a fatalist you are.
If you’re purely a fatalist, then free will is an illusion, laws and punishment are immoral, consciousness is meaningless, and we nothing more than deterministic pattern matching machines, making us only different from LLMs in the details of our implementation and from the terrible optimization that evolution is known for.
But if you believe in some degree of free will, or you think there is value in consciousness, then we differ because LLMs are just auto-complete. They psudo-randomly choose from a weighted list of statistically likely words (actually token) that would come next given the context (which is the conversation history and prompt). There is no free will, no understanding any more than the man in the Chinese room understands Mandarin.
The whole conversation is so full of charged words because the LLM providers have intentionally anthropomorphized LLMs in their marketing, by using words like “reasoning”. The APIs from before LLMs blew up provide a far less emotionally charged description of what LLMs do, with terms like “completions”.
You wouldn’t compare a human mind to your phone keyboard word prediction, but it’s doing the same thing but scaled down. Where do you draw the line?Isn’t that sorta what humans do? Picking words based on the ones used before, taking into consideration the context of the conversation?
Not really. When asked a question, a human would think about the answer, and construct a sentence to try and express that point. An LLM doesn’t know what the answer is ahead of time, it’s not working towards a point, it’s just statistically guessing the next couple of letters over and over again. The human equivalent would just be making random mouth noises and hoping the other person interprets them as words
Only if, like I said, you’re a hardcore fatalist.
The term “hallucinate” is a euphemism being pushed by the AI peddlers.
It’s a computer program. It doesn’t “hallucinate”, it has errors.
In all cases of ML models being sold by companies, what you are actually looking at is poorly tested software that is not fit for purpose, and has far less actual capability then what the marketing promises.
“Hallucination” in the context of LLMs is marketing bullshit designed to deflect from the reality that none of these programs have been properly quality checked and are extremely error prone.
If Excel gave bad answers for calculations 20% of the time it wouldn’t be “hallucinating”, it would just be broken, buggy software that requires more development time before distribution as a useful product.
LLM “hallucinations” are only errors from a user expectations perspective. The actual purpose of these models is to generate natural-sounding language, not to provide factual answers. We often forget that - they were never designed as knowledge engines or reasoning tools.
The fact that they often get things right isn’t because they “know” anything - it’s a side effect of being trained on data that contains a lot of correct information. So when they get things wrong, it’s not a bug in the traditional sense - it’s just the model doing what it was designed to do: predict likely word sequences, not truth. Calling that a “hallucination” isn’t marketing spin - it’s a useful way to describe confident output that isn’t grounded in reality.
The difference is that a human often has to be held accountable when they make a mistake, so most humans will use logic and critical thinking when trying hard not to make mistakes, even if it takes longer than an LLM whose “reasoning” is more like a slot machine.
I would argue that AI should be held to account for the information it provides, and until AI is capable of having a personal bank account, damages should be paid by the company who created it.
The only reason I see that AI doesn’t “hold itself to account” is that it was never programmed to. Much like if you do not properly educate a young human, they will not be held accountable a lot of the time because we understand their actions are the result of how they were brought up and taught, or “programmed”.
You do bring up a good point, but I see that as a failing on the Humans making the AI and restricting it, not a demonstration that AI wouldn’t be capable of holding itself and its decisions to account if it was taught to like we need to be taught to.
The difference is in how the LLMs work vs animals brains work.
Animals brains use logic and reactions.
LLMs exclusively use statistics to generate their output. Even their “reasoning” is faked.
LLMs have more in common with humans than we tend to admit. In split-brain studies, humans have been shown to invent plausible-sounding explanations for their behavior - even when scientists know those explanations aren’t the real reason they acted a certain way. It’s not that these people are lying per se - they genuinely believe the explanations they’re coming up with. Lying implies they know what they’re saying is false.
LLMs are similar in that way. They generate natural-sounding language, but not everything they say is true - just like not everything humans say is true either.
It replicates the part of our brain responsible for bullshitting
No, it generates natural sounding language. That’s all it does.
Maybe that’s all we are doing too…?
