Comment on Has anyone or anything ever passed the Turring Test? If so how and why?
dandi8@fedia.io 2 weeks agoExcept LLM output is largely gibberish. Just confident gibberish. There's a reason we call it "AI slop".
LLM responses are only ever "sound" when they're regurgitating existing information they were trained on. Beyond some simple transformations, they are unable to create original ideas. They very frequently break down on somewhat unique tasks, as evidenced by the ever-prevalent code-slop which is eroding our software.
They don't have a memory of previous conversations (unless you literally copy-paste it into the prompt), they don't learn (Claude "memories" is literally just copy-pasting a summary into the prompt, only automatically). They don't have any "thoughts" of their own between prompts (OpenClaw just keeps prompting them to pretend they are autonomous).
The underlying implementation of "thinking" in LLMs is literally "hallucinate some more text which vaguely looks like thoughts and hope that influences the answer". LLMs are probabilistic models which we figured out how to make so they produce somewhat correct-looking answers at a rate a lityle higher than chance.
Magic 8-balls sometimes give sound responses. Do they think? Where do we draw the line with this interpretation of "thinking"?
Epp@lemmus.org 2 weeks ago
I would disagree with you, and would suspect you are basing your assessment of their abilities on dated usage. I hold a MSc from what is arguably the most prestigious University in Europe, in regards to computer science, and my major was in AI. Believe my when I say I know exactly how they function.
I still assert you are oversimplifying their current capabilities, and seem to be conflating LLM with Markov Chains. LLM do not simply regurgitate existing content, and are in fact capable of creating wholly new content not seen before. Hallucinations occur when their context buffer is too small, and as time goes on, it will largely be a thing of the past.
Magic Eight Balls, as I’m sure you’re aware, have a limited, predetermined number of responses. They are in no way comparable. LLM use the equivalent of synapses, just digital whereas we use biological, but the function is the same. Modern AI is distinguishable only by the medium used, silicon versus organic material. As the number of input parameters, and context windows grows, the difference between them and our own brains will shrink until the medium is the only remaining difference.
We’re not there yet, but I would argue they are capable of thought if we define that to mean reasoning towards a response using all available information, instead of taking a predetermined or random path to one. We draw the line at biological life and LLM, nothing else we are aware of can think.
dandi8@fedia.io 2 weeks ago
Good for you. Have a cookie, I guess?
Citation needed.
A whole book of citations needed. That claim is wildly inconsistent with the consensus about AI hallucinations.
In fact, I keep experimenting with frontier models (including Fable when it was available) just so that the "but we've made so much progress in the past few months" argument can't be used against me. You're wildly overselling their capabilities.
Epp@lemmus.org 2 weeks ago
Thanks, I like cookies. You should have one too for participating in this discourse, Internet stranger. Help yourself!
I’m citing myself. If you have information to the contrary of someone I’ve said, feel free to provide it.
Those examples are no different than instincts in people. The “training data” will shine through, but it doesn’t preclude new behavior.
This reads to me like you went into the interaction with a prescribed expectation, and were using it only to validate your prejudice. If you used Fable, and didn’t think any thought was occurring, you’re either being purposefully obtuse or we have wildly different definitions of thinking.
Why don’t you give me your definition that includes people but excluded advanced LLM such as Fable?
dandi8@fedia.io 2 weeks ago
I think the Wikipedia definition of thought is quite good.
However, I have a feeling whatever definition I came up with, you'd just claim LLMs fit into it because their output is sometimes somewhat coherent.
You can claim that technically LLMs "think" because the output text sometimes contains conclusions, and sometimes they're even rational, even though the LLMs still struggle with counting Rs in "strawberry".
I find that disingenuous because it implies that the LLM is in any way aware of anything, that it can passively form ideas.
Most importantly, it implies that you can trust it for even basic reasoning. That you can trust the plagiarism machine that tells you that you should put glue on your pizza, eat rocks and walk to the car wash instead of driving, or that you will be able to trust it at some point in the future.
Whatever definition of thinking we use, it should include a simple rule - that the allegedly thinking entity should demonstrate that intelligence by being able to reliably answer simple queries correctly. Humans, by and large, can do that. LLMs fail at it miserably. If the LLMs were truly thinking, that should be shocking. Understanding the underlying technology - and that it is not truly reasoning - makes it obvious and expected.
Even OpenAI admitted hallucinations are an unfixable mathematical inevitability - something you handwaved as a matter of time to fix. No, the fact that humans can have hallucinations is not comparable.