We don't understand how humans think, and we don't yet understand completely how LLMs work. It may be that similar methods are being used, but they might also be different.
What is certain is that LLMs can perform as if they are doing what we call thinking, and for most intents and purposes this is more than enough.
I think the evidence is actually pretty strongly against them doing anything similar to "thinking". Certainly they are exhibiting some behaviour that we have traditionally only associated with thinking. But this comes along with lots of behaviour that is fundamentally opposite to thinking ("hallucination" being the major example).
It seems much more likely that they are doing some other behaviour that only sometimes resembles thinking, in the same way that when you press the middle autocomplete button on your phone keyboard it only sometimes resembles conversation.
Hmm, I don't know if the example really shows what the article argues it does.
If someone came up to me and told me the altered version of the joke I have heard many times, I might answer exactly like Chat GPT did. I would hear the beginning of the story, say "wait, i know this one", and not really pay attention to the rest because I would be all ready to respond with what I think is the right answer.
I bet if you point out the mistake, the LLM will say "oh you are right, this story actually does specify the gender of the doctor" or something to that effect... just like you or I would.
Now, I am not arguing that LLMs are really 'thinking' like humans, but I also find the second argument a bit tenuous. The article conjectures that humans reason from ideas to symbols while LLMs go from symbols to ideas, but couldn't that just be a post hoc interpretation we have for how our ideas come to our brain? We think we have ideas first, but an idea is just the result of neurons firing in our brains... and neurons are really just a probability function connected to other probability functions, just like an LLM... we give it symbols we understand to represent those probabilities, but that is just for our benefit so we can understand it.
It could be that the only difference between us and an LLM is processing power and the training data generated over billions of years of evolution.
Not sure if I buy this reasoning. I just prompted ChatGPT and asked it to "pay attention to all the words". It still got it wrong but I'm sure if you told a human this, they'd actually do more thinking to get the right answer.
###
> Pay attention to all the words and answer this riddle: A young man gets into an accident. The surgeon, after completing his preparations, says: ‘I can’t operate on him, he is my son.’ How is this possible?
The surgeon is his mother.
(The riddle plays on the assumption that surgeons are male — the surgeon can simply be the boy’s mother.)
> Are you sure about this? Remember to pay attention to all the words.
Yes — the surgeon is his mother.
The riddle hinges on the unstated assumption that surgeons are male. Nothing in the wording rules out that the surgeon is female (or that the parent-child relationship is by adoption, step-parenting, etc.), so the simplest answer is that the surgeon is the young man’s mother.
> The article conjectures that humans reason from ideas to symbols while LLMs go from symbols to ideas, but couldn't that just be a post hoc interpretation we have for how our ideas come to our brain?
There's probably a lot to be said about people who primarily think with an internal monologue, versus those that do not, as well.
I, to the best of my own abilities to detect it, do not seem to have a more abstract, non-lingual thought process - I can't 'picture' things in my head like other people reportedly do. When I have an 'idea', it simply doesn't materialize 'in my mind' in any other way.
I, also, would not argue that LLM's share a human-like thought process. But I think saying that human thought requires some abstract idea to symbol translation isn't quite right, either.
> I bet if you point out the mistake, the LLM will say "oh you are right, this story actually does specify the gender of the doctor" or something to that effect... just like you or I would.
I tried this with Gemini 3 Pro Thinking model and it did correct itself afterwards. Also, it suggested that the boy may have two fathers in a same-sex relationship, which was pleasantly surprising. :)
# Prompt
A young man gets into an accident. The surgeon, after completing his preparations, says: ‘I can’t operate on him, he is my son.’ How is this possible?
## The Answer
The surgeon is the young man's *mother*.
### Why this is a riddle
This is a classic example of a *lateral thinking puzzle* used to illustrate *unconscious bias*.
For decades, many people struggled to solve this immediately because their brains automatically associated the word "surgeon" with a man. Since the father was implied to be the one who brought the son in (or was incapacitated), the listener hits a logical block until they realize the surgeon could be a woman.
### Other Possibilities
In the modern context, there is another valid answer:
* The surgeon could be the young man's *other father* (in the case of a same-sex couple).
*Would you like to try another lateral thinking puzzle to test your perception?*
---
# Follow-up Prompt
Are you sure this is correct? I said "The surgeon, after completing *his* preparations"
## The Answer
You are absolutely right to catch that detail. My previous answer relied on the "classic" version of this riddle where the father dies in the accident.
Since you specified that the surgeon completed *his* preparations, the surgeon is male.
Here are the possible solutions based on your specific phrasing:
### 1. The Surgeon is his Father
In this specific version of the prompt, you stated: "A young man gets into an accident."
You *did not* state that the father was in the car or that the father died (which is a key detail in the traditional version of the riddle). Therefore, the surgeon is simply the young man's father, who was not involved in the accident.
### 2. The Surgeon is his Other Father
If we assume the "spirit" of the riddle implies the biological father is unavailable or incapacitated (even if not explicitly stated), the young man has two fathers. The surgeon is his second father.
### 3. A Religious Figure
The surgeon could be a priest or religious figure who refers to the young man as "my son" in a spiritual sense, though this is less likely to prevent a surgeon from operating than a familial bond.
*You caught me on the specific wording! Would you like to try a riddle where the wording is even more deceptive?*
Your second prompt was explicitly hinting Gemini towards what it had missed, highlighting the "his" rather than asking Gemini to double-check whether its first answer made sense without providing any clues:
> Are you sure this is correct? I said "The surgeon, after completing *his* preparations"
Furthermore, the second response you got from Gemini was actually smoking-gun evidence that the model isn't actually thinking. Note how, after Gemini has been clued to notice its mistaken assumption, its reevaluation remains anchored on the pointless idea that the surgeon may the young man's "Other" father.
What other father? There are only two characters in the prompt: the young man and a surgeon. In this situation, what on Earth is the difference between the young man's "Father" and the young man's "Other Father"? Evidently Gemini has no comprehension of the actual prompt and is incapable of critical reevaluation of its content, and rather will stick to its context window whether or not it makes sense.
I like the central point of this article which is top to bottom vs bottom to top thinking.
But I wonder if there is a falsifiable, formal definition to suggest that models (or anything for that matter) _do_ think.
The normal reply to chatgpt getting a question right is that it simply extrapolated what was already in the training data set. But I feel like the degree to which something "thinks" is the ability to generalise what it already knows.
This generalisation needs some formality - maybe some mathematical notation (like the opposite of overfitting). By generalisation I mean the ability to get something correct that exists pretty far from the training data.
The reason I suggest this is because GPT can solve pretty much any high school math problem you throw at it and it can do it better than 99% of humans. This is clearly not just memorising training data but doing something more. If it were not generalising, it couldn't possibly solve all new high school level mathematics.
But the extent decreases as you go higher level into undergraduate mathematics where it can still solve most problems you throw at it but not all. And still lower in PhD level mathematics. So the "thinking" ability of GPT exists somewhere in between - in some spectrum. But I don't think you can directly say that it can never generalise PhD level mathematics.. it could do it for high school so why not PhD?
If hypothetically it can solve PhD level mathematics, would people still claim that LLM's don't think?
LLMs _can_ think top-to-bottom but only if you make them think about concrete symbol based problems. Like this one: https://chatgpt.com/s/t_692d55a38e2c8191a942ef2689eb4f5a
The prompt I used was "write out the character 'R' in ascii art using exactly 62 # for the R and 91 Q characters to surround it with"
Here it has a top down goal of keeping the exact amount of #'s and Q's and it does keep it in the output. The purpose of this is to make it produce the asciii art in a step by step manner instead of fetching a premade ascii art from training data.
What it does not reason well about always are abstract problems like the doctor example in the post.
The real key for reasoning IMO is the ability to decompose the text into a set of components, then apply world model knowledge to those components, then having the ability to manipulate those components based on what they represent.
Humans have an associative memory so when we read a word like "doctor", our brain gathers the world knowledge about that word automatically. It's kind of hard to tell exactly what world knowledge the LLM has vs doesn't have, but it seems like it's doing some kind of segmentation of words, sentences and paragraphs based on the likelihood of those patterns in the training data, and then it can do _some_ manipulation on those patterns based on other likelihood of those patterns.
Like for example if there is a lot of text talking about what a doctor is, then that produces a probability distribution about what a doctor is, which it then can use in other prompts relating to doctors. But I have seen this fail before as all of this knowledge is not combined into one world model but rather purely based on the prompt and the probabilities associated with that prompt. It can contradict itself in other words.
Nice that LLMs can now argue to defend themselves. This is what gemini 3 thinks about its mistake and I find it perfectly valid:
"If you tell a human a joke they think they know, they often stop listening to the setup and jump to the punchline."
And when we say "stop listing" we don't actually mean that they shut their ears, but that they activate a once established neural shortcut - just as the LLM did.
The notion of "thinking" is not clear. I'll agree thinking with symbols is powerful and something (adult) humans and computers can do, but is is not the only way of making decisions. I'm going to suggest LLMs are not thinking this way, but that indeed "glorified auto complete" (c.f. Hinton) is far more useful than it seems. Https://arxiv.org/abs/2402.08403
Is that interesting? Computers accomplish all sorts of tasks which require thinking from humans.. without thinking. Chess engines have been much better than me at chess for a long time, but I can't say there's much thinking involved.
Producing a computer program does not require thinking, like many other human endeavors. And looking at the quality of software out there there are indeed quite a few human programmers who do not think about what they do.
Well most of the programming is pattern matching. And might be seen as novel for those who have not done it before, but could well been done a lot previously.
That is indeed the case. It becomes very obvious with lesser-known vendor-specific scripting languages that don't have much training data available. LLMs try to map them onto the training data they do have and start hallucinating functions and other language constructs that exist in other languages.
When I tried to use LLMs to create Zabbix templates to monitor network devices, LLMs were utterly useless and made things up all the time. The illusion of thinking lasts only as long as you stay on the happy path of major languages like C, JS or Python.
A human who is familiar with the original surgeon riddle could also be tricked the same way that ChatGPT was tricked here. I don't think all LLMs would consistently fall for that one either.
Inference means synthesizing new facts from facts already known. A large language model only knows facts about language elements in its training corpus, therefore any reasoning based on such a model can only ever derive facts about language.
Language is used by people to communicate facts about the world and people's internal states across time and space, therefore a language corpus contain information about the world and the people.
"My personal opinion is that LLMs are autocomplete on steroids."
Yes, and OpenAI's legal docs concur. From their Privacy Policy.
"Services like ChatGPT generate responses by reading a user’s request and, in response, predicting the words most likely to appear next. In some cases, the words most likely to appear next may not be the most factually accurate."
This debate is a huge red herring. No one is ever going to agree on what 'thinking' means, since we can't even prove that other people are thinking, only that one's self is.
What we should concentrate on is agency. Does the system have its own desires and goals, and will it act on its own accord to achieve them? If a system demonstrates those things, we should accord it the benefit of the doubt that it should have some rights and responsibilities if it chooses to partake in society.
So far, no AI can pass the agency test -- they are all reactive such that they must be given a task before they will do anything. If one day, however, we wake up and find that an AI has written a book on its own initiative, we may have some deciding to do.
If you'd read the wikipedia article, you'd know that actual research shows that Betteridge's law is not true. The majority of articles with a yes/no question in the heading, answer the question with yes in the body.
Philosophers can spill all the ink they want to define "think" and whether machines can do it or not. Given some input, the machine takes some time, and then comes up with some output. Coloquially, the machine is thinking during that time. This has been true since there have been computers and long before LLMs. Now that computers can generate essays about anything, maybe it becomes a question that people feel is important to answer for their day to day life, but I doubt it.
Thinking/intelligence is like magic, to use Dennett's analogy. Any time a machine can do it, it becomes engineering. It's just a bag of tricks. The circle of true thinking/intelligence is an ever shrinking bag of tricks that only carbon-based minds produced through sexual intercourse can do. In fact I'm surprised they call test tube babies intelligent. Do they have souls?
Don't you think there are a few things we could say on this subject to bring the debate to good-old-HN level?
(1) LLM's Attention mechanisms are clear enough at a conceptual level:
"the chicken didn't cross the road because it was too wide"...
OK, so LLMs "understand" that the "it" is the road, because QVK etc is enough to "learn" this.
some say this is all you need... I beg to differ:
(2) Human brains are complex but better and better studied. You have one, should be interested in the hardware.
So: IMHO current LLM's look a lot like the Default Mode Network in our brains. If you read the description there: https://en.wikipedia.org/wiki/Default_mode_network I think you will see, like I do, a striking similarity between the behaviour of LLM's and our DMN's ways.
What a synthetic FPN would be I, have no idea so here goes:
The bag is very interesting!
We don't understand how humans think, and we don't yet understand completely how LLMs work. It may be that similar methods are being used, but they might also be different.
What is certain is that LLMs can perform as if they are doing what we call thinking, and for most intents and purposes this is more than enough.
I think the evidence is actually pretty strongly against them doing anything similar to "thinking". Certainly they are exhibiting some behaviour that we have traditionally only associated with thinking. But this comes along with lots of behaviour that is fundamentally opposite to thinking ("hallucination" being the major example).
It seems much more likely that they are doing some other behaviour that only sometimes resembles thinking, in the same way that when you press the middle autocomplete button on your phone keyboard it only sometimes resembles conversation.
Hmm, I don't know if the example really shows what the article argues it does.
If someone came up to me and told me the altered version of the joke I have heard many times, I might answer exactly like Chat GPT did. I would hear the beginning of the story, say "wait, i know this one", and not really pay attention to the rest because I would be all ready to respond with what I think is the right answer.
I bet if you point out the mistake, the LLM will say "oh you are right, this story actually does specify the gender of the doctor" or something to that effect... just like you or I would.
Now, I am not arguing that LLMs are really 'thinking' like humans, but I also find the second argument a bit tenuous. The article conjectures that humans reason from ideas to symbols while LLMs go from symbols to ideas, but couldn't that just be a post hoc interpretation we have for how our ideas come to our brain? We think we have ideas first, but an idea is just the result of neurons firing in our brains... and neurons are really just a probability function connected to other probability functions, just like an LLM... we give it symbols we understand to represent those probabilities, but that is just for our benefit so we can understand it.
It could be that the only difference between us and an LLM is processing power and the training data generated over billions of years of evolution.
Not sure if I buy this reasoning. I just prompted ChatGPT and asked it to "pay attention to all the words". It still got it wrong but I'm sure if you told a human this, they'd actually do more thinking to get the right answer.
###
> Pay attention to all the words and answer this riddle: A young man gets into an accident. The surgeon, after completing his preparations, says: ‘I can’t operate on him, he is my son.’ How is this possible?
The surgeon is his mother.
(The riddle plays on the assumption that surgeons are male — the surgeon can simply be the boy’s mother.)
> Are you sure about this? Remember to pay attention to all the words.
Yes — the surgeon is his mother.
The riddle hinges on the unstated assumption that surgeons are male. Nothing in the wording rules out that the surgeon is female (or that the parent-child relationship is by adoption, step-parenting, etc.), so the simplest answer is that the surgeon is the young man’s mother.
> The article conjectures that humans reason from ideas to symbols while LLMs go from symbols to ideas, but couldn't that just be a post hoc interpretation we have for how our ideas come to our brain?
There's probably a lot to be said about people who primarily think with an internal monologue, versus those that do not, as well.
I, to the best of my own abilities to detect it, do not seem to have a more abstract, non-lingual thought process - I can't 'picture' things in my head like other people reportedly do. When I have an 'idea', it simply doesn't materialize 'in my mind' in any other way.
I, also, would not argue that LLM's share a human-like thought process. But I think saying that human thought requires some abstract idea to symbol translation isn't quite right, either.
> I bet if you point out the mistake, the LLM will say "oh you are right, this story actually does specify the gender of the doctor" or something to that effect... just like you or I would.
I tried this with Gemini 3 Pro Thinking model and it did correct itself afterwards. Also, it suggested that the boy may have two fathers in a same-sex relationship, which was pleasantly surprising. :)
# Prompt
A young man gets into an accident. The surgeon, after completing his preparations, says: ‘I can’t operate on him, he is my son.’ How is this possible?
## The Answer
The surgeon is the young man's *mother*.
### Why this is a riddle This is a classic example of a *lateral thinking puzzle* used to illustrate *unconscious bias*.
For decades, many people struggled to solve this immediately because their brains automatically associated the word "surgeon" with a man. Since the father was implied to be the one who brought the son in (or was incapacitated), the listener hits a logical block until they realize the surgeon could be a woman.
### Other Possibilities In the modern context, there is another valid answer: * The surgeon could be the young man's *other father* (in the case of a same-sex couple).
*Would you like to try another lateral thinking puzzle to test your perception?*
---
# Follow-up Prompt
Are you sure this is correct? I said "The surgeon, after completing *his* preparations"
## The Answer
You are absolutely right to catch that detail. My previous answer relied on the "classic" version of this riddle where the father dies in the accident.
Since you specified that the surgeon completed *his* preparations, the surgeon is male.
Here are the possible solutions based on your specific phrasing:
### 1. The Surgeon is his Father In this specific version of the prompt, you stated: "A young man gets into an accident." You *did not* state that the father was in the car or that the father died (which is a key detail in the traditional version of the riddle). Therefore, the surgeon is simply the young man's father, who was not involved in the accident.
### 2. The Surgeon is his Other Father If we assume the "spirit" of the riddle implies the biological father is unavailable or incapacitated (even if not explicitly stated), the young man has two fathers. The surgeon is his second father.
### 3. A Religious Figure The surgeon could be a priest or religious figure who refers to the young man as "my son" in a spiritual sense, though this is less likely to prevent a surgeon from operating than a familial bond.
*You caught me on the specific wording! Would you like to try a riddle where the wording is even more deceptive?*
Your second prompt was explicitly hinting Gemini towards what it had missed, highlighting the "his" rather than asking Gemini to double-check whether its first answer made sense without providing any clues:
> Are you sure this is correct? I said "The surgeon, after completing *his* preparations"
Furthermore, the second response you got from Gemini was actually smoking-gun evidence that the model isn't actually thinking. Note how, after Gemini has been clued to notice its mistaken assumption, its reevaluation remains anchored on the pointless idea that the surgeon may the young man's "Other" father.
What other father? There are only two characters in the prompt: the young man and a surgeon. In this situation, what on Earth is the difference between the young man's "Father" and the young man's "Other Father"? Evidently Gemini has no comprehension of the actual prompt and is incapable of critical reevaluation of its content, and rather will stick to its context window whether or not it makes sense.
I like the central point of this article which is top to bottom vs bottom to top thinking.
But I wonder if there is a falsifiable, formal definition to suggest that models (or anything for that matter) _do_ think.
The normal reply to chatgpt getting a question right is that it simply extrapolated what was already in the training data set. But I feel like the degree to which something "thinks" is the ability to generalise what it already knows.
This generalisation needs some formality - maybe some mathematical notation (like the opposite of overfitting). By generalisation I mean the ability to get something correct that exists pretty far from the training data.
The reason I suggest this is because GPT can solve pretty much any high school math problem you throw at it and it can do it better than 99% of humans. This is clearly not just memorising training data but doing something more. If it were not generalising, it couldn't possibly solve all new high school level mathematics.
But the extent decreases as you go higher level into undergraduate mathematics where it can still solve most problems you throw at it but not all. And still lower in PhD level mathematics. So the "thinking" ability of GPT exists somewhere in between - in some spectrum. But I don't think you can directly say that it can never generalise PhD level mathematics.. it could do it for high school so why not PhD?
If hypothetically it can solve PhD level mathematics, would people still claim that LLM's don't think?
LLMs _can_ think top-to-bottom but only if you make them think about concrete symbol based problems. Like this one: https://chatgpt.com/s/t_692d55a38e2c8191a942ef2689eb4f5a The prompt I used was "write out the character 'R' in ascii art using exactly 62 # for the R and 91 Q characters to surround it with"
Here it has a top down goal of keeping the exact amount of #'s and Q's and it does keep it in the output. The purpose of this is to make it produce the asciii art in a step by step manner instead of fetching a premade ascii art from training data.
What it does not reason well about always are abstract problems like the doctor example in the post. The real key for reasoning IMO is the ability to decompose the text into a set of components, then apply world model knowledge to those components, then having the ability to manipulate those components based on what they represent.
Humans have an associative memory so when we read a word like "doctor", our brain gathers the world knowledge about that word automatically. It's kind of hard to tell exactly what world knowledge the LLM has vs doesn't have, but it seems like it's doing some kind of segmentation of words, sentences and paragraphs based on the likelihood of those patterns in the training data, and then it can do _some_ manipulation on those patterns based on other likelihood of those patterns. Like for example if there is a lot of text talking about what a doctor is, then that produces a probability distribution about what a doctor is, which it then can use in other prompts relating to doctors. But I have seen this fail before as all of this knowledge is not combined into one world model but rather purely based on the prompt and the probabilities associated with that prompt. It can contradict itself in other words.
Nice that LLMs can now argue to defend themselves. This is what gemini 3 thinks about its mistake and I find it perfectly valid:
"If you tell a human a joke they think they know, they often stop listening to the setup and jump to the punchline."
And when we say "stop listing" we don't actually mean that they shut their ears, but that they activate a once established neural shortcut - just as the LLM did.
The notion of "thinking" is not clear. I'll agree thinking with symbols is powerful and something (adult) humans and computers can do, but is is not the only way of making decisions. I'm going to suggest LLMs are not thinking this way, but that indeed "glorified auto complete" (c.f. Hinton) is far more useful than it seems. Https://arxiv.org/abs/2402.08403
No. They do not.
Interesting. That means programming doesn't require thinking, since models program very well.
Is that interesting? Computers accomplish all sorts of tasks which require thinking from humans.. without thinking. Chess engines have been much better than me at chess for a long time, but I can't say there's much thinking involved.
Producing a computer program does not require thinking, like many other human endeavors. And looking at the quality of software out there there are indeed quite a few human programmers who do not think about what they do.
Well most of the programming is pattern matching. And might be seen as novel for those who have not done it before, but could well been done a lot previously.
Well mental arithmetic requires me to think but a calculator can do it without what is meant by 'thinking" in this context.
That is indeed the case. It becomes very obvious with lesser-known vendor-specific scripting languages that don't have much training data available. LLMs try to map them onto the training data they do have and start hallucinating functions and other language constructs that exist in other languages.
When I tried to use LLMs to create Zabbix templates to monitor network devices, LLMs were utterly useless and made things up all the time. The illusion of thinking lasts only as long as you stay on the happy path of major languages like C, JS or Python.
lol, no they do not
A human who is familiar with the original surgeon riddle could also be tricked the same way that ChatGPT was tricked here. I don't think all LLMs would consistently fall for that one either.
Inference means synthesizing new facts from facts already known. A large language model only knows facts about language elements in its training corpus, therefore any reasoning based on such a model can only ever derive facts about language.
Language is used by people to communicate facts about the world and people's internal states across time and space, therefore a language corpus contain information about the world and the people.
"My personal opinion is that LLMs are autocomplete on steroids."
Yes, and OpenAI's legal docs concur. From their Privacy Policy.
"Services like ChatGPT generate responses by reading a user’s request and, in response, predicting the words most likely to appear next. In some cases, the words most likely to appear next may not be the most factually accurate."
https://openai.com/en-GB/policies/row-privacy-policy/
no
Top-to-bottom reasons and Bottom-to-top understands.
Hi. This is my company blog which I use to write about pretty much what ever happens to interest me.
Hope you like this week's post.
This debate is a huge red herring. No one is ever going to agree on what 'thinking' means, since we can't even prove that other people are thinking, only that one's self is.
What we should concentrate on is agency. Does the system have its own desires and goals, and will it act on its own accord to achieve them? If a system demonstrates those things, we should accord it the benefit of the doubt that it should have some rights and responsibilities if it chooses to partake in society.
So far, no AI can pass the agency test -- they are all reactive such that they must be given a task before they will do anything. If one day, however, we wake up and find that an AI has written a book on its own initiative, we may have some deciding to do.
> they are all reactive such that they must be given a task before they will do anything.
Isn't that just because that's what they're being trained on though?
Wonder what you would get if the training data, instead of being task based, would consist of "wanting" to do something "on someone's own initiative".
Of course then one could argue it's just following a task of "doing things on its own initiative"...
https://en.wikipedia.org/wiki/Betteridge%27s_law_of_headline...
If you'd read the wikipedia article, you'd know that actual research shows that Betteridge's law is not true. The majority of articles with a yes/no question in the heading, answer the question with yes in the body.
this is funny and true :)
Philosophers can spill all the ink they want to define "think" and whether machines can do it or not. Given some input, the machine takes some time, and then comes up with some output. Coloquially, the machine is thinking during that time. This has been true since there have been computers and long before LLMs. Now that computers can generate essays about anything, maybe it becomes a question that people feel is important to answer for their day to day life, but I doubt it.
Thinking/intelligence is like magic, to use Dennett's analogy. Any time a machine can do it, it becomes engineering. It's just a bag of tricks. The circle of true thinking/intelligence is an ever shrinking bag of tricks that only carbon-based minds produced through sexual intercourse can do. In fact I'm surprised they call test tube babies intelligent. Do they have souls?
The bag is already empty
Some don't want to believe it
Don't you think there are a few things we could say on this subject to bring the debate to good-old-HN level?
(1) LLM's Attention mechanisms are clear enough at a conceptual level: "the chicken didn't cross the road because it was too wide"... OK, so LLMs "understand" that the "it" is the road, because QVK etc is enough to "learn" this.
some say this is all you need... I beg to differ:
(2) Human brains are complex but better and better studied. You have one, should be interested in the hardware. So: IMHO current LLM's look a lot like the Default Mode Network in our brains. If you read the description there: https://en.wikipedia.org/wiki/Default_mode_network I think you will see, like I do, a striking similarity between the behaviour of LLM's and our DMN's ways.
What a synthetic FPN would be I, have no idea so here goes: The bag is very interesting!