Almost every LLM available can pass the Turing test, because they can indeed think. Some, like Gemini, will even give you a stream of consciousness as they think. However, many luddites expect perfection from the technology, so they will claim the thinking is inadequate, or that the test is flawed. Neither is true, they’re just very bitter about the technology for reasons unrelated to its capabilities.
Don’t get fooled by clever tricks from developers, LLMs are a mathematical function, where it gets the chain of numbers you give it and returns a new chain of numbers. LLMs are 100% predeterministic, programmers purposefully make them choose a random response within a degree of tolerance instead of picking the correct answer.
I saw you making this claim on another comment, this is COMPLETELY different from how humans/animals/plants think. LLMs are incapable of thought, incapable of learning, and incapable of understanding, that’s why they fail dumb tests like “how many Rs in strawberry”, they’re just average machines.
They’re not useless, they’re not intelligent, they’re a tool, you don’t think your calculator is intelligent because it can do math you can’t, and shouldn’t think an LLM is intelligent because it can aggregate texts that you can’t.
All that being said, you’re correct that LLMs do pass the Turing test, but that doesn’t mean what you think it does, it just means they’re very good at pretending to.
I would argue that humans are the same, we just don’t have access to our programming. If we did, and could measure the state of our brains, we would be entirely deterministic, as well.
That’s a very Newtonian way to look at the world. Even IF that was correct (which is not because of the uncertainty principle), if you go down that road you will get to the conclusion that everything is intelligent even a simple program that chooses an alternate greeting between Hello and Hi can be considered intelligent by that standard.
Yes, I know, and what you’re overlooking is that the uncertainty principle applies to LLM, as well, and even your example alternating algorithm.
That’s why a solid definition of intelligence is necessary, and my own is that the closer the number of relevant, comprehensibly potential responses approaches infinity, the more intelligent it is. On this scale modern AI is not as intelligent as humans, but it’s certainly more intelligent than your alternating greeting.
The uncertainty principle does NOT apply to LLMs and absolutely, unquestionably does NOT apply to my alternating algorithm. You need to understand the difference between “I don’t know” and “It’s unknowable”.
It most certainly does. Do you think that you know the position and state of all the electrons in a computer when a program is executing? It’s unknowable, and checking the status collapses the superposition, changing the measurement. It’s no different from the status of the synapses in our brains. Even your simple “Hi” vs “Hello” program has a non-zero probability of outputting neither, or both expected outputs.
I think that the position and state of every single electron is mostly irrelevant. My alternating greeting can be made with a paper having one side written each greeting and flipping it every time, you also don’t need to know the state of every subatomic particle there, even though there is a possibility that every single electron in that piece of paper suddenly moves away and the vacuum in electrical charge causes a rush of electricity that vaporizes the whole room… Yeah it’s possible, but you’re a dumbass if you think that possibility is worth calculating.
The same is true for a computer, and again you’re mixing up “I can’t possibly know that” with “it’s unknowable”. Knowing the electrical charge at each position of the computer is knowable, knowing the electrical charge at each position of a brain is also knowable, but while knowing that information on a computer allows you to predict its outcome, the same is not true for a brain.
You sound quite sure of yourself, but I believe you are mistaken. The state of the electrons does indeed matter when a program is executing.
What makes you think that we will never be able to predict the outcome of a brain it we had the same knowledge of it as we did of a comparable neural network? You’d have to be a dumbass if you think that the medium matters in knowability of a system. Whether biological or mechanical, every state is possible until it’s measured, and once it is, you can determine exactly how it will function for a short period of time. Complexity does not make something unknowable, only complex, and therefore difficult to know.
Unlike the position and state of subatomic particles in any system, including that of the host of an LLM, which are unknowable.
Your oversimplification is noted. I assume you believe humans are word predictors, too? Just biological, instead of mechanical. In both cases, using input and electrical signals to create an output.
If not, please pinpoint the salient difference empowering your dissent.
Plants and animals work in completely different ways, but they’re both alive. Just because something works differently doesn’t invalidate it’s results and existence.
If LLM didn’t think, it would be gibberish - just words related to the input. Instead, they are typically logical, sound, relevant responses; often with insight made by extrapolated data in the periphery of the prompt.
What you are expecting is consciousness, which they do not have yet. Thinking, though, yes.
Except 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 “reasoning” 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 little higher than chance.
Magic 8-balls sometimes give sound responses. Do they think? Where do we draw the line with this interpretation of “thinking”?
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 me 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 already 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.
I hold a MSc from what is arguably the most prestigious University in Europe
Good for you. Have a cookie, I guess?
LLM do not simply regurgitate existing content, and are in fact capable of creating wholly new content not seen before.
Citation needed.
Hallucinations occur when their context buffer is too small, and as time goes on, it will largely be a thing of the past.
A whole book of citations needed. That claim is wildly inconsistent with the consensus about AI hallucinations.
Magic Eight Balls, as I’m sure you’re aware, have a limited, predetermined number of responses.
You mean like how LLMs keep hallucinating the same passwords and nonexistent dependencies to the point that bad actors are using that fact to compromise vibe coded systems via techniques like slopsquatting?
I would disagree with you, and would suspect you are basing your assessment of their abilities on dated usage.
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.
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 something 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.
… arguments can’t be used against me.
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 excludes advanced LLM such as Fable?
Almost every LLM available can pass the Turing test, because they can indeed think. Some, like Gemini, will even give you a stream of consciousness as they think. However, many luddites expect perfection from the technology, so they will claim the thinking is inadequate, or that the test is flawed. Neither is true, they’re just very bitter about the technology for reasons unrelated to its capabilities.
Don’t get fooled by clever tricks from developers, LLMs are a mathematical function, where it gets the chain of numbers you give it and returns a new chain of numbers. LLMs are 100% predeterministic, programmers purposefully make them choose a random response within a degree of tolerance instead of picking the correct answer.
I saw you making this claim on another comment, this is COMPLETELY different from how humans/animals/plants think. LLMs are incapable of thought, incapable of learning, and incapable of understanding, that’s why they fail dumb tests like “how many Rs in strawberry”, they’re just average machines.
They’re not useless, they’re not intelligent, they’re a tool, you don’t think your calculator is intelligent because it can do math you can’t, and shouldn’t think an LLM is intelligent because it can aggregate texts that you can’t.
All that being said, you’re correct that LLMs do pass the Turing test, but that doesn’t mean what you think it does, it just means they’re very good at pretending to.
I would argue that humans are the same, we just don’t have access to our programming. If we did, and could measure the state of our brains, we would be entirely deterministic, as well.
That’s a very Newtonian way to look at the world. Even IF that was correct (which is not because of the uncertainty principle), if you go down that road you will get to the conclusion that everything is intelligent even a simple program that chooses an alternate greeting between Hello and Hi can be considered intelligent by that standard.
Yes, I know, and what you’re overlooking is that the uncertainty principle applies to LLM, as well, and even your example alternating algorithm.
That’s why a solid definition of intelligence is necessary, and my own is that the closer the number of relevant, comprehensibly potential responses approaches infinity, the more intelligent it is. On this scale modern AI is not as intelligent as humans, but it’s certainly more intelligent than your alternating greeting.
The uncertainty principle does NOT apply to LLMs and absolutely, unquestionably does NOT apply to my alternating algorithm. You need to understand the difference between “I don’t know” and “It’s unknowable”.
It most certainly does. Do you think that you know the position and state of all the electrons in a computer when a program is executing? It’s unknowable, and checking the status collapses the superposition, changing the measurement. It’s no different from the status of the synapses in our brains. Even your simple “Hi” vs “Hello” program has a non-zero probability of outputting neither, or both expected outputs.
I think that the position and state of every single electron is mostly irrelevant. My alternating greeting can be made with a paper having one side written each greeting and flipping it every time, you also don’t need to know the state of every subatomic particle there, even though there is a possibility that every single electron in that piece of paper suddenly moves away and the vacuum in electrical charge causes a rush of electricity that vaporizes the whole room… Yeah it’s possible, but you’re a dumbass if you think that possibility is worth calculating.
The same is true for a computer, and again you’re mixing up “I can’t possibly know that” with “it’s unknowable”. Knowing the electrical charge at each position of the computer is knowable, knowing the electrical charge at each position of a brain is also knowable, but while knowing that information on a computer allows you to predict its outcome, the same is not true for a brain.
You sound quite sure of yourself, but I believe you are mistaken. The state of the electrons does indeed matter when a program is executing.
What makes you think that we will never be able to predict the outcome of a brain it we had the same knowledge of it as we did of a comparable neural network? You’d have to be a dumbass if you think that the medium matters in knowability of a system. Whether biological or mechanical, every state is possible until it’s measured, and once it is, you can determine exactly how it will function for a short period of time. Complexity does not make something unknowable, only complex, and therefore difficult to know.
Unlike the position and state of subatomic particles in any system, including that of the host of an LLM, which are unknowable.
Word predictors don’t think any more than a magic 8-ball does.
Your oversimplification is noted. I assume you believe humans are word predictors, too? Just biological, instead of mechanical. In both cases, using input and electrical signals to create an output.
If not, please pinpoint the salient difference empowering your dissent.
No, humans are not word predictors, and my claim is absolutely not an oversimplification.
LLMs are word predictors. No amount of attention heads and backpropagation is going to change that. Scientific researchers agree.
The human brain works in a completely different way to how LLMs do and to conflate the two like you did is disingenuous.
Plants and animals work in completely different ways, but they’re both alive. Just because something works differently doesn’t invalidate it’s results and existence.
If LLM didn’t think, it would be gibberish - just words related to the input. Instead, they are typically logical, sound, relevant responses; often with insight made by extrapolated data in the periphery of the prompt.
What you are expecting is consciousness, which they do not have yet. Thinking, though, yes.
Except 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 “reasoning” 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 little higher than chance.
Magic 8-balls sometimes give sound responses. Do they think? Where do we draw the line with this interpretation of “thinking”?
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 me 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 already 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.
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.
You mean like how LLMs keep hallucinating the same passwords and nonexistent dependencies to the point that bad actors are using that fact to compromise vibe coded systems via techniques like slopsquatting?
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.
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 something 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 excludes advanced LLM such as Fable?