A consciousness is not an “output” of a human brain.
Fair enough. Obviously consciousness is more complex than that. I should have put “efferent neural actions” first in that case, consciousness just being a side effect, something different yet composed of the same parts, an emergent phenomenon. How would you describe consciousness, though? I wish you would offer that instead of just saying “nuh uh” and calling me chatGPT :(
Not sure how you interpreted what I wrote in the rest of your comment though. I never mentioned humans teaching each other causal relations? I only compared the training of neural networks to evolutionary principles, where at one point we had entities that interacted with their environment in fairly simple and predictable ways (a “deterministic algorithm” if you will, as you said in another comment), and at some later point we had entities that we would call intelligent.
What I am saying is that at some point the pattern recognition “trained” by evolution (where inputs are environmental distress/eustress, and outputs are actions that are favorable to the survival of the organism) became so advanced that it became self-aware (higher pattern recognition on itself?) among other things. There was a point, though, some characteristic, self-awareness or not, where we call something intelligence as opposed to unintelligent. When I asked where you draw the line, I wanted to know what characteristic(s) need to be present for you to elevate something from the status of “pattern recognition” to “intelligence”.
It’s tough to decide whether more primitive entities were able to form causal relationships. When they saw predators, did they know that they were going to die if they didn’t run? Did they at least know something bad would happen to them? Or was it just a pre-programmed neural response that caused them to run? Most likely the latter.
Based on all that we know and observe, a dog (any animal, really) understands concepts and causal relations to varying degrees. That’s true intelligence.
From another comment, I’m not sure what you mean by “understands”. It could mean having knowledge about the nature of a thing, or it could mean interpreting things in some (meaningful) way, or it could mean something completely different.
To your last point, logical thinking is possible, but of course humans can’t do it on our own. We had to develop a system for logical thinking (which we call “logic”, go figure) as a framework because we are so bad at doing it ourselves. We had to develop statistical methods to determine causal relations because we are so bad at doing it on our own. So what does it mean to “understand” a thing? When you say an animal “understands” causal relations, do they actually understand it or is it just another form of pattern recognition (why I mentioned pavlov in my last comment)? When humans “understand” a thing, do they actually understand, or do we just encode it with the frameworks built on pattern recognition to help guide us? A scientific model is only a model, built on trial and error. If you “understand” the model you do not “understand” the thing that it is encoding. I know you said “to varying degrees”, and this is the sticking point. Where do you draw the line?
When you want to have artificial intelligence, even the most basic software can have some kind of limited understanding that actually fits this attempt at a definition - it’s just that the functionality will be very limited and pretty much appear useless.
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You could program image recognition using math to find certain shapes, which in turn - together with colour ranges and/or contrasts - could be used to associate object types, for which causal relations can be defined, upon which other parts of an AI could then base decision processes. This process has potential for error, but in a similar way that humans can mischaracterize the things we see - we also sometimes do not recognize an object correctly.
I recognize that you understand the point I am trying to make. I am trying to make the same point, just with a different perspective. Your description of an “actually intelligent” artificial intelligence closely matches how sensory data is integrated in the layers of the visual cortex, perhaps on purpose. My question still stands, though. A more primitive species would integrate data in a similar, albeit slightly less complex, way: take in (visual) sensory information, integrate the data to extract easier-to-process information such as brightness, color, lines, movement, and send it to the rest of the nervous system for further processing to eventually yield some output in the form of an action (or thought, in our case). Although in the process of integrating, we necessarily lose information along the way for the sake of efficiency, so what we perceive does not always match what we see, as you say. Image recognition models do something similar, integrating individual pixel information using convolutions and such to see how it matches an easier-to-process shape, and integrating it further. Maybe it can’t reason about what it’s seeing, but it can definitely see shapes and colors.
You will notice that we are talking about intelligence, which is a remarkably complex and nuanced topic. It would do some good to sit and think deeply about it, even if you already think you understand it, instead of asserting that whoever sounds like they might disagree with you is wrong and calling them chatbots. I actually agree with you that calling modern LLMs “intelligent” is wrong. What I ask is what you think would make them intelligent. Everything else is just context so that you understand where I’m coming from.
I had a bunch of sections of your comment that I wanted to quote, let’s see how much I can answer without copy-pasting too much.
First off, my apologies, I misunderstood your analogy about machine learning not as a comparison towards evolution, but towards how we learn with our developed brains. I concur that the process of evolution is similar, except a bit less targeted (and hence so much slower) than deep learning. The result however, is “cogito ergo sum” - a creature that started self-reflecting and wondering about it’s own consciousness. And this brings me to humans thinking logically:
As such a creature, we are able to form logical thoughts, which allow us to understand causality. To give an example of what I mean: Humans (and some animals) did not need the invention of logic or statistics in order to observe moving objects and realize that where something moves, something has moved it - and therefore when they see an inanimate object move, they will eventually suspect the most likely cause for the move in the direction that the object is coming from.
Then, when we do not find the cause (someone throwing something) there, we will investigate further (if curious enough) and look for a cause. That’s how curiosity turns into science. But it’s very much targeted, nothing a deep learning system can do. And that’s kind of what I would also expect from something that calls itself “AI”: a systematic analysis / categorization of the input data for the purpose of processing that the system was built for. And for a general AI, also the ability to analyze phenomena to understand their root cause.
Of course, logic is often not the same as our intuitive thoughts, but we are still able to correct our intuitive assumptions based on outcome, but then understand the actual causal relation (unlike a deep learning system) based on our corrected “model” of whatever we observed. In the end, that’s also how science works: We describe reality with a model, and when we discover a discrepancy, we aim to update the model. But we always have a model.
With regards to some animals understanding objects / causal relations, I believe - beyond having a concept of an object - defining what I mean by “understanding” is not really helpful, considering that the spectrum of intelligence among animals overlaps with that of humans. Some of the more clever animals clearly have more complex thoughts and you can interact with them in a more meaningful way than some of the humans with less developed brains, be it due to infancy, or a disability or psychological condition.
How would you describe consciousness, though? I wish you would offer that instead of just saying “nuh uh” and calling me chatGPT :(
First off, I meant the LLM comment seriously - I am considering already to stop participating in internet debates because LLMs have become so sophisticated that I will no longer be able to know whether I am arguing with a human, or whether some LLM is wasting my precious life time.
As for how to describe consciousness, that’s a largely philosophical topic and strongly linked to whether or not free will exists (IMO), although theoretically it would be possible to be conscious but not have any actual free will. I can not define the “sense of self” better than philosophers are doing it, because our language does not have the words to even properly structure our thoughts on that.
I can however, tell you how I define free will:
assuming you could measure every atom, sub-atomic particle, impulse & spin thereof, energy field and whatever else physical properties there are in a human being and it’s environment
when that individual moves a limb, you would be able to trace - based on what we know:
the movement of the limb back to the muscles contracting
movement of the muscles back to electrical signals in some nerves
the nerve signals back to some neurons firing in the brain
if you trace that chain of “causes” further and further, eventually, if free will exists, it would be impossible to find a measurable cause for some “lowest level trigger event”
And this lowest level trigger event - by some researchers attributed to quantum decay - might be / could be influenced by our free will, even if - because we have this “brain lag” - the actual decision happened quite some time earlier, and even if for some decisions, they are hardwired (like reflexes, which can also be trained).
My personal model how I would like consciousness to be: An as-of-yet undiscovered property of matter, that every atom has, but only combined with an organic computer that is complex enough to process and store information would such a property actually exhibit a consciousness.
In other words: If you find all the subatomic particles (or most of them) that made up a person in history at a given point in time, and reassemble them in the exact same pattern, you would, in effect, re-create that person, including their consciousness at that point in time.
If you duplicate them from other subatomic particles with the exact same properties (as far as we can measure) - who knows? Because we couldn’t measure nor observe the “consciousness property”, how would we know if that would be equal among all particles that are equal in the properties we can measure. That would be like assuming atoms of a certain element were all the same, because we do not see chemical differences for other isotopes.
Fair enough. Obviously consciousness is more complex than that. I should have put “efferent neural actions” first in that case, consciousness just being a side effect, something different yet composed of the same parts, an emergent phenomenon. How would you describe consciousness, though? I wish you would offer that instead of just saying “nuh uh” and calling me chatGPT :(
Not sure how you interpreted what I wrote in the rest of your comment though. I never mentioned humans teaching each other causal relations? I only compared the training of neural networks to evolutionary principles, where at one point we had entities that interacted with their environment in fairly simple and predictable ways (a “deterministic algorithm” if you will, as you said in another comment), and at some later point we had entities that we would call intelligent.
What I am saying is that at some point the pattern recognition “trained” by evolution (where inputs are environmental distress/eustress, and outputs are actions that are favorable to the survival of the organism) became so advanced that it became self-aware (higher pattern recognition on itself?) among other things. There was a point, though, some characteristic, self-awareness or not, where we call something intelligence as opposed to unintelligent. When I asked where you draw the line, I wanted to know what characteristic(s) need to be present for you to elevate something from the status of “pattern recognition” to “intelligence”.
It’s tough to decide whether more primitive entities were able to form causal relationships. When they saw predators, did they know that they were going to die if they didn’t run? Did they at least know something bad would happen to them? Or was it just a pre-programmed neural response that caused them to run? Most likely the latter.
From another comment, I’m not sure what you mean by “understands”. It could mean having knowledge about the nature of a thing, or it could mean interpreting things in some (meaningful) way, or it could mean something completely different.
To your last point, logical thinking is possible, but of course humans can’t do it on our own. We had to develop a system for logical thinking (which we call “logic”, go figure) as a framework because we are so bad at doing it ourselves. We had to develop statistical methods to determine causal relations because we are so bad at doing it on our own. So what does it mean to “understand” a thing? When you say an animal “understands” causal relations, do they actually understand it or is it just another form of pattern recognition (why I mentioned pavlov in my last comment)? When humans “understand” a thing, do they actually understand, or do we just encode it with the frameworks built on pattern recognition to help guide us? A scientific model is only a model, built on trial and error. If you “understand” the model you do not “understand” the thing that it is encoding. I know you said “to varying degrees”, and this is the sticking point. Where do you draw the line?
I recognize that you understand the point I am trying to make. I am trying to make the same point, just with a different perspective. Your description of an “actually intelligent” artificial intelligence closely matches how sensory data is integrated in the layers of the visual cortex, perhaps on purpose. My question still stands, though. A more primitive species would integrate data in a similar, albeit slightly less complex, way: take in (visual) sensory information, integrate the data to extract easier-to-process information such as brightness, color, lines, movement, and send it to the rest of the nervous system for further processing to eventually yield some output in the form of an action (or thought, in our case). Although in the process of integrating, we necessarily lose information along the way for the sake of efficiency, so what we perceive does not always match what we see, as you say. Image recognition models do something similar, integrating individual pixel information using convolutions and such to see how it matches an easier-to-process shape, and integrating it further. Maybe it can’t reason about what it’s seeing, but it can definitely see shapes and colors.
You will notice that we are talking about intelligence, which is a remarkably complex and nuanced topic. It would do some good to sit and think deeply about it, even if you already think you understand it, instead of asserting that whoever sounds like they might disagree with you is wrong and calling them chatbots. I actually agree with you that calling modern LLMs “intelligent” is wrong. What I ask is what you think would make them intelligent. Everything else is just context so that you understand where I’m coming from.
I had a bunch of sections of your comment that I wanted to quote, let’s see how much I can answer without copy-pasting too much.
First off, my apologies, I misunderstood your analogy about machine learning not as a comparison towards evolution, but towards how we learn with our developed brains. I concur that the process of evolution is similar, except a bit less targeted (and hence so much slower) than deep learning. The result however, is “cogito ergo sum” - a creature that started self-reflecting and wondering about it’s own consciousness. And this brings me to humans thinking logically: As such a creature, we are able to form logical thoughts, which allow us to understand causality. To give an example of what I mean: Humans (and some animals) did not need the invention of logic or statistics in order to observe moving objects and realize that where something moves, something has moved it - and therefore when they see an inanimate object move, they will eventually suspect the most likely cause for the move in the direction that the object is coming from. Then, when we do not find the cause (someone throwing something) there, we will investigate further (if curious enough) and look for a cause. That’s how curiosity turns into science. But it’s very much targeted, nothing a deep learning system can do. And that’s kind of what I would also expect from something that calls itself “AI”: a systematic analysis / categorization of the input data for the purpose of processing that the system was built for. And for a general AI, also the ability to analyze phenomena to understand their root cause.
Of course, logic is often not the same as our intuitive thoughts, but we are still able to correct our intuitive assumptions based on outcome, but then understand the actual causal relation (unlike a deep learning system) based on our corrected “model” of whatever we observed. In the end, that’s also how science works: We describe reality with a model, and when we discover a discrepancy, we aim to update the model. But we always have a model.
With regards to some animals understanding objects / causal relations, I believe - beyond having a concept of an object - defining what I mean by “understanding” is not really helpful, considering that the spectrum of intelligence among animals overlaps with that of humans. Some of the more clever animals clearly have more complex thoughts and you can interact with them in a more meaningful way than some of the humans with less developed brains, be it due to infancy, or a disability or psychological condition.
First off, I meant the LLM comment seriously - I am considering already to stop participating in internet debates because LLMs have become so sophisticated that I will no longer be able to know whether I am arguing with a human, or whether some LLM is wasting my precious life time.
As for how to describe consciousness, that’s a largely philosophical topic and strongly linked to whether or not free will exists (IMO), although theoretically it would be possible to be conscious but not have any actual free will. I can not define the “sense of self” better than philosophers are doing it, because our language does not have the words to even properly structure our thoughts on that. I can however, tell you how I define free will:
And this lowest level trigger event - by some researchers attributed to quantum decay - might be / could be influenced by our free will, even if - because we have this “brain lag” - the actual decision happened quite some time earlier, and even if for some decisions, they are hardwired (like reflexes, which can also be trained).
My personal model how I would like consciousness to be: An as-of-yet undiscovered property of matter, that every atom has, but only combined with an organic computer that is complex enough to process and store information would such a property actually exhibit a consciousness.
In other words: If you find all the subatomic particles (or most of them) that made up a person in history at a given point in time, and reassemble them in the exact same pattern, you would, in effect, re-create that person, including their consciousness at that point in time.
If you duplicate them from other subatomic particles with the exact same properties (as far as we can measure) - who knows? Because we couldn’t measure nor observe the “consciousness property”, how would we know if that would be equal among all particles that are equal in the properties we can measure. That would be like assuming atoms of a certain element were all the same, because we do not see chemical differences for other isotopes.