4 ms·

Moravac (in the linked paper): "In both cases, the evidence for an intelligent mind lies in the machine's performance, not its makeup." Do you agree? I'm much

by jll29 3y ago

Moravac (in the linked paper): "In both cases, the evidence for an intelligent mind lies in the machine's performance, not its makeup." Do you agree? I'm much less keen to ascribe "intelligence" to large, pretrained language models given that I know how primitive their training regime is compared to a scenario where I might have been "blended" by their ability to "chat" (double quote here since I know ChatGPT and the likes do not have a memory, so all prior interactions have to be re-submitted with each turn of a conversation). Intuitively, I'd be more prone to ascribe intelligence based on convincing ways of construction that go along with intellignece-like performance, especially if the model also makes human-like errors.

awesomeMilou 3y ago
Reducing the capability of the human brain to performance alone is too simplistic, especially when looking at LLM's. Even if we would assign some intelligence to LLM's, they need a 400w GPU at inference time, and several orders of magnitude more of those at training time. The human brain runs constanly at ~20w. I highly doubt you'd be able to get even close to that kind of performance with current manufacturing processes. We'd need something entirely different from laser lithography for that to happen.
- psunavy03 3y ago
  > We'd need something entirely different from laser lithography for that to happen. Like, you know . . . nerve cells.
  - syndicatedjelly 3y ago
    Care to explain?
    - psunavy03 3y ago
      I was being facetious in that brains already exist.
- ben_w 3y ago
  The problem isn't the manufacturing process, but rather the architecture. At a low level: We take an analog component, then drive it in a way that lets us treat it as digital, then combine loads of them together so we can synthesise a low-resolution approximation of an analog process. At a higher level: We don't really understand how our brains are architected yet, just that it can make better guesses from fewer examples than our AI. Also, 400 W of electricity is generally cheaper than 20 W of calories (let alone the 38-100 W rest of body needed to keep the brain alive depending on how much of a couch potato the human is).
  - awesomeMilou 3y ago
    > Also, 400 W of electricity is generally cheaper than 20 W of calories Are you serious? I don't think you have to be an expert to see that the average human can perform more work per energy intake than the average GPU. > The problem isn't the manufacturing process, but rather the architecture. It's very much a problem, good luck trying to even emulate the 3D neural structure of the brain with lithography. And there are few other processes that can create structures at the required scale, with the required precision.
    - ben_w 3y ago
      > Are you serious? I don't think you have to be an expert to see that the average human can perform more work per energy intake than the average GPU. You're objecting to something I didn't say, which is extra weird because I'm just running with the same 400 W/20 W you yourself gave. All I'm doing here is pointing out that 400 W of electricity is cheaper than 20 W of calories especially as 20 W is a misleading number until we get brains in jars. To put numbers to the point, at $0.10/kWh * 400 W * 24h = $0.96, while the UN definition for abject poverty is $2.57 in 2023 dollars. As for my opinion on which can perform more work per unit of energy, that idea is simply too imprecise to answer without more detail — depending on what exactly you mean by "work", a first generation Pi Zero can beat all humans combined while the world's largest supercomputer can't keep up with one human. > It's very much a problem, good luck trying to even emulate the 3D neural structure of the brain with lithography. IIRC by volume a human brain mostly communication between neurones; the ridges are because most of your complexity is a thin layer on the surface, and ridges get you more surface. But that doesn't even matter, because it's a question of the connection graph, and each cell has about 10,000 synapses, and that connectivity be instantiated in many different ways even on a 2D chip. We don't have a complete example connectivity graph for a human brain. Got it for a rat, I think, but not a human, which is why I previously noted that we don't really understand how our brains are architected. > And there are few other processes that can create structures at the required scale, with the required precision. Litho vastly exceeds the required precision. Chemical synapses are 20-30 nm from one cell to the next, and even the more compact electrical synapses are 3.5 nm.
- pixl97 3y ago
  >The human brain runs constanly at ~20w. Most likely because any brains that required more energy died off at evolutionary time scales. And while there are some problems with burning massive amounts of energy to achieve a task (see: global warming) this is not likely a significant short falling that large scale AI models have to worry about. Seemingly there are plenty of humans willing to hook them up to power sources at this time. Also you might want to consider the 0-16 year training stages for human which have become more like 0-21 year training stages with at minimum 8 hours of downtime per day. This does adjust the power dynamics pretty considerably in that the time actually thinking daily drops to around 1/3rd the day boosting effective power use to 60w (as in you've wasted 2/3s the power eating, sleeping, and pooping). In addition that model you've spend a lot of power training is able to be duplicated across thousands/millions of instances in short order, where as you're praying that human you've trained doesn't step out in front of a bus. So yes, reducing the capability of a human brain/body to performance alone is far too simplistic.
  - naveen99 3y ago
    Some of that 8 hrs of sleep includes fine tuning on recent experiences and simulation (dreams).
djokkataja 3y ago
I agree with Moravec. As he points out a bit later on: > Only on the outside, where they can be appreciated as a whole, will the impression of intelligence emerge. A human brain, too, does not exhibit the intelligence under a neurobiologist's microscope that it does participating in a lively conversation. We only have fuzzy definitions of "intelligence", not any essential, unambiguous things we can point to at a minute level, like a specific arrangement of certain atoms. Put another way, we've used the term "intelligent" to refer to people (or not) because we found it useful to describe a complex bundle of traits in a simple way. But now that we're training LLMs to do things that used to be assumed to be exclusively the capacity of humans, the term is getting stretched and twisted and losing some of its usefulness. Maybe it would be more useful to subdivide the term a bit by referring to "human intelligence" versus "LLM intelligence". And when some new developments in AI seem like they're different from "LLM intelligence", we can call them by whatever distinguishes them, like "Q* intelligence", for example.
- visarga 3y ago
  > The intelligence of a system is a measure of its skill-acquisition efficiency over a scope of tasks, concerning priors, experience, and generalization difficulty. (Chollet, 2019, https://arxiv.org/pdf/1911.01547.pdf https://arxiv.org/pdf/1911.01547.pdf) Priors here means how targeted is the model design to the task. Experience means how large is the necessary training set. Generalization difficulty is how hard is the task. So intelligence is defined as ability to learn a large number of tasks with as little experience and model selection as possible. If it's a skill only possible because your model already follows the structure of the problem, then it won't generalize. If it requires too much training data, it's not very intelligent. If it's just a set number of skills and can't learn new ones quickly, it's not intelligent.
  - ElectronCharge 3y ago
    Your final paragraph is a poor definition of human level intelligence. Yes, learning is an important aspect of human cognition. However, the key factor that humans possess that LLMs will never possess, is the ability to reason logically. That facility is necessary in order to make new discoveries based on prior logical frameworks like math, physics, and computer science. I believe LLMs are more akin to our subconscious processes like image recognition, or forming a sentence. What’s missing is an executive layer that has one or more streams of consciousness, and which can reason logically with full access to its corpus of knowledge. That would also add the ability for the AI to explain how it reached a particular conclusion. There are likely other nuances required (motivation etc.) for (super) human AI, but some form of conscious executive is a hard requirement.
pfdietz 3y ago
This is reminding me again of The Bitter Lesson. http://www.incompleteideas.net/IncIdeas/BitterLesson.html http://www.incompleteideas.net/IncIdeas/BitterLesson.html
- andoando 3y ago
  I am not a fan of this article. The vary foundation of computer science was an attempt to emulate a human mind processing data. Foundational changes are of course harder, but it does not mean we should drop it all together.
  - rerdavies 3y ago
    > The very foundation of computer science was an attempt to emulate a human mind processing data. The very foundation of computer science was an attempt to emulate a human mind mindlessly processing data. Fixed that for you. And I'm still not sure I agree. The foundation of computer science was at attempt to process data so that human minds didn't have to endure the drudgery of such mindless tasks.
    - andoando 3y ago
      Take a look at Turing words in his formulation of the Turing machine and I think it becomes quite clearly the man spent time thinking about what he is doing when he is doing computations. The tape is a piece of paper, the head is the human, who is capable of reading data from the tape and writing to it. The symbols are discernible things on the paper, like numbers. The movement of the tape ("scanning") is the eyes going back and forth. At each symbol, the machine decides which rule to apply. Its an inescapable fact that we are trying to get computers to 1. operate as close to how we think (as we are the ones who operate it) and 2. to produce results which resemble how we think. Abstractions, inheritence, objects, etc are no doubt all heavily influenced by thinking about how we think. If we still programmed using 1s and 0s, we wouldnt be where we are. It seems incredibly short sighted to me to believe that because a few decades of research hasnt panned out, that we should all together forget about it.
ben_w 3y ago
LLMs are radically unlike organic minds. Given their performance, I think it is important to pay attention to their weirdnesses — I can call them "intelligent" or "dumb" without contradiction depending on which specific point is under consideration. Transistors outpace biological synapses by the same degree to which a marathon runner outpaces continental drift. This speed difference is what allows computers to read the entire text content of the internet on a regular basis, whereas a human can't read all of just the current version of the English language Wikipedia once in their lifetime. But current AI is very sample-inefficient: if a human were to read as much as an LLM, they would be world experts at everything, not varying between "secondary school" and "fresh graduate" depending on the subject… but even that description is misleading, because humans have the System 1/System 2[0] distinction and limited attention[1], whereas LLMs pay attention to approximately everything in the context window and (seem to) be at a standard between our System 1 and System 2. If you're asking about an LLM's intelligence because you want to replace an intern, then the AI are intelligent; but if you're asking because you want to know how many examples they need in order to decode North Sentinelese or Linear A, then (from what I understand) these AI are extremely stupid. It doesn't matter if a submarine swims[2], it still isn't going to fit into a flooded cave. [0] https://en.wikipedia.org/wiki/Thinking,_Fast_and_Slow https://en.wikipedia.org/wiki/Thinking,_Fast_and_Slow [1] https://youtu.be/vJG698U2Mvo?si=omf3xleqPw5u6Y2k https://youtu.be/vJG698U2Mvo?si=omf3xleqPw5u6Y2k https://youtu.be/ubNF9QNEQLA?si=Ja-9Ak4iCbcxbWdh https://youtu.be/ubNF9QNEQLA?si=Ja-9Ak4iCbcxbWdh https://youtu.be/v3iPrBrGSJM?si=9cKHXEvEGl764Efa https://youtu.be/v3iPrBrGSJM?si=9cKHXEvEGl764Efa https://www.americanbar.org/groups/intellectual_property_law/publications/landslide/2014-15/march-april/that-s-wrap-online-agreements-gaming-kids/ https://www.americanbar.org/groups/intellectual_property_law... [2] https://www.goodreads.com/quotes/32629-the-question-of-whether-a-computer-can-think-is-no https://www.goodreads.com/quotes/32629-the-question-of-wheth...
- lukeschlather 3y ago
  > if a human were to read as much as an LLM, they would be world experts at everything It would take a human more than one lifetime to read everything most LLMs have read. I often have trouble remembering specifics of something I read an hour ago, never mind a decade ago. I can't imagine a human being an expert in something they read 300 years ago.
FrustratedMonky 3y ago
"ChatGPT and the likes do not have a memory" Can't we consider the context window to be memory? And eventually wont we have larger and larger context windows, and perhaps even individual training where part of the context window 'conversation' is also fed back into the training data? Seems like this will come to pass eventually.
gnramires 3y ago
For reference, I believe relatively rudimentary chatbots have been able to pass forms of the Turing Test for a while, while also being unable to do almost all the things humans can do. It turns out you can make a chatbot quite convincing for a very short conversation with someone you've never met over the internet[1]. I think there's a trend that fooling perception is significantly easier than having the right capabilities. Maybe with sufficiently advanced testing you can judge, but I don't think this is the case in general: in general, our thoughts may be significantly different than we can converse. One obvious example is simply people with disabilities (say a paraplegic) who can't talk at all (while having thoughts of their own): output capability need not necessarily reflect internal capability. Also, take this more advanced example: if you built a sufficiently large lookup table (of course, you need (possibly human) intelligence to build it, and it would be of impractical size), you can build a chatbot completely indistinguishable from a human that nonetheless doesn't seem like it really is intelligent (or conscious for that matter). The only operations it would perform would be some sort of decoding of the input into an astronomically large number to retrieve from the lookup table, and then using some (possibly rudimentary) seeking apparatus to retrieve the content that corresponds to the input. Your input size can be arbitrarily large enabling arbitrarily long conversations. It seems that to judge intelligence we really need to examine the internals and look at their structure. I have a hunch that our particular 'feeling of consciousness' stems from the massive interconnectivity of the brain. All sorts of neurons from around the brain have some activity all the time (I believe the brain's energy consumption doesn't vary greatly with activity, so we use it), and whatever we perceive goes through an enormous number of interconnected neurons (representing concepts, impressions, ideas, etc.); unlike typical CPU-based algorithms that process a somewhat large number (potentially 100s of millions) of steps sequentially. My intuition does seem to pair with modern neural architectures (i.e. they could have some consciousness?), but I really don't know how we could do this sort of judgement before understanding better the details of the brain and other properties of cognition. I think this is a very important research area, and I'm not sure we have enough people working on it or using the correct tools (it relies heavily on logic, philosophy and metaphysical arguments; as well as require personal insight from being conscious :) ). [1] From sources I can't remember, and from wiki: https://en.wikipedia.org/wiki/Turing_test#Loebner_Prize https://en.wikipedia.org/wiki/Turing_test#Loebner_Prize
idiotsecant 3y ago
Intelligence is what you call it when you don't know how it works yet.

by jll29 3y ago

awesomeMilou 3y ago

Reducing the capability of the human brain to performance alone is too simplistic, especially when looking at LLM's. Even if we would assign some intelligence to LLM's, they need a 400w GPU at inference time, and several orders of magnitude more of those at training time. The human brain runs constanly at ~20w. I highly doubt you'd be able to get even close to that kind of performance with current manufacturing processes. We'd need something entirely different from laser lithography for that to happen.

psunavy03 3y ago
> We'd need something entirely different from laser lithography for that to happen. Like, you know . . . nerve cells.
- syndicatedjelly 3y ago
  Care to explain?
  - psunavy03 3y ago
    I was being facetious in that brains already exist.
ben_w 3y ago
The problem isn't the manufacturing process, but rather the architecture. At a low level: We take an analog component, then drive it in a way that lets us treat it as digital, then combine loads of them together so we can synthesise a low-resolution approximation of an analog process. At a higher level: We don't really understand how our brains are architected yet, just that it can make better guesses from fewer examples than our AI. Also, 400 W of electricity is generally cheaper than 20 W of calories (let alone the 38-100 W rest of body needed to keep the brain alive depending on how much of a couch potato the human is).
- awesomeMilou 3y ago
  > Also, 400 W of electricity is generally cheaper than 20 W of calories Are you serious? I don't think you have to be an expert to see that the average human can perform more work per energy intake than the average GPU. > The problem isn't the manufacturing process, but rather the architecture. It's very much a problem, good luck trying to even emulate the 3D neural structure of the brain with lithography. And there are few other processes that can create structures at the required scale, with the required precision.
  - ben_w 3y ago
    > Are you serious? I don't think you have to be an expert to see that the average human can perform more work per energy intake than the average GPU. You're objecting to something I didn't say, which is extra weird because I'm just running with the same 400 W/20 W you yourself gave. All I'm doing here is pointing out that 400 W of electricity is cheaper than 20 W of calories especially as 20 W is a misleading number until we get brains in jars. To put numbers to the point, at $0.10/kWh * 400 W * 24h = $0.96, while the UN definition for abject poverty is $2.57 in 2023 dollars. As for my opinion on which can perform more work per unit of energy, that idea is simply too imprecise to answer without more detail — depending on what exactly you mean by "work", a first generation Pi Zero can beat all humans combined while the world's largest supercomputer can't keep up with one human. > It's very much a problem, good luck trying to even emulate the 3D neural structure of the brain with lithography. IIRC by volume a human brain mostly communication between neurones; the ridges are because most of your complexity is a thin layer on the surface, and ridges get you more surface. But that doesn't even matter, because it's a question of the connection graph, and each cell has about 10,000 synapses, and that connectivity be instantiated in many different ways even on a 2D chip. We don't have a complete example connectivity graph for a human brain. Got it for a rat, I think, but not a human, which is why I previously noted that we don't really understand how our brains are architected. > And there are few other processes that can create structures at the required scale, with the required precision. Litho vastly exceeds the required precision. Chemical synapses are 20-30 nm from one cell to the next, and even the more compact electrical synapses are 3.5 nm.
pixl97 3y ago
>The human brain runs constanly at ~20w. Most likely because any brains that required more energy died off at evolutionary time scales. And while there are some problems with burning massive amounts of energy to achieve a task (see: global warming) this is not likely a significant short falling that large scale AI models have to worry about. Seemingly there are plenty of humans willing to hook them up to power sources at this time. Also you might want to consider the 0-16 year training stages for human which have become more like 0-21 year training stages with at minimum 8 hours of downtime per day. This does adjust the power dynamics pretty considerably in that the time actually thinking daily drops to around 1/3rd the day boosting effective power use to 60w (as in you've wasted 2/3s the power eating, sleeping, and pooping). In addition that model you've spend a lot of power training is able to be duplicated across thousands/millions of instances in short order, where as you're praying that human you've trained doesn't step out in front of a bus. So yes, reducing the capability of a human brain/body to performance alone is far too simplistic.
- naveen99 3y ago
  Some of that 8 hrs of sleep includes fine tuning on recent experiences and simulation (dreams).

djokkataja 3y ago

I agree with Moravec. As he points out a bit later on: > Only on the outside, where they can be appreciated as a whole, will the impression of intelligence emerge. A human brain, too, does not exhibit the intelligence under a neurobiologist's microscope that it does participating in a lively conversation. We only have fuzzy definitions of "intelligence", not any essential, unambiguous things we can point to at a minute level, like a specific arrangement of certain atoms. Put another way, we've used the term "intelligent" to refer to people (or not) because we found it useful to describe a complex bundle of traits in a simple way. But now that we're training LLMs to do things that used to be assumed to be exclusively the capacity of humans, the term is getting stretched and twisted and losing some of its usefulness. Maybe it would be more useful to subdivide the term a bit by referring to "human intelligence" versus "LLM intelligence". And when some new developments in AI seem like they're different from "LLM intelligence", we can call them by whatever distinguishes them, like "Q* intelligence", for example.

visarga 3y ago
> The intelligence of a system is a measure of its skill-acquisition efficiency over a scope of tasks, concerning priors, experience, and generalization difficulty. (Chollet, 2019, https://arxiv.org/pdf/1911.01547.pdf https://arxiv.org/pdf/1911.01547.pdf) Priors here means how targeted is the model design to the task. Experience means how large is the necessary training set. Generalization difficulty is how hard is the task. So intelligence is defined as ability to learn a large number of tasks with as little experience and model selection as possible. If it's a skill only possible because your model already follows the structure of the problem, then it won't generalize. If it requires too much training data, it's not very intelligent. If it's just a set number of skills and can't learn new ones quickly, it's not intelligent.
- ElectronCharge 3y ago
  Your final paragraph is a poor definition of human level intelligence. Yes, learning is an important aspect of human cognition. However, the key factor that humans possess that LLMs will never possess, is the ability to reason logically. That facility is necessary in order to make new discoveries based on prior logical frameworks like math, physics, and computer science. I believe LLMs are more akin to our subconscious processes like image recognition, or forming a sentence. What’s missing is an executive layer that has one or more streams of consciousness, and which can reason logically with full access to its corpus of knowledge. That would also add the ability for the AI to explain how it reached a particular conclusion. There are likely other nuances required (motivation etc.) for (super) human AI, but some form of conscious executive is a hard requirement.

pfdietz 3y ago

This is reminding me again of The Bitter Lesson. http://www.incompleteideas.net/IncIdeas/BitterLesson.html http://www.incompleteideas.net/IncIdeas/BitterLesson.html

andoando 3y ago
I am not a fan of this article. The vary foundation of computer science was an attempt to emulate a human mind processing data. Foundational changes are of course harder, but it does not mean we should drop it all together.
- rerdavies 3y ago
  > The very foundation of computer science was an attempt to emulate a human mind processing data. The very foundation of computer science was an attempt to emulate a human mind mindlessly processing data. Fixed that for you. And I'm still not sure I agree. The foundation of computer science was at attempt to process data so that human minds didn't have to endure the drudgery of such mindless tasks.
  - andoando 3y ago
    Take a look at Turing words in his formulation of the Turing machine and I think it becomes quite clearly the man spent time thinking about what he is doing when he is doing computations. The tape is a piece of paper, the head is the human, who is capable of reading data from the tape and writing to it. The symbols are discernible things on the paper, like numbers. The movement of the tape ("scanning") is the eyes going back and forth. At each symbol, the machine decides which rule to apply. Its an inescapable fact that we are trying to get computers to 1. operate as close to how we think (as we are the ones who operate it) and 2. to produce results which resemble how we think. Abstractions, inheritence, objects, etc are no doubt all heavily influenced by thinking about how we think. If we still programmed using 1s and 0s, we wouldnt be where we are. It seems incredibly short sighted to me to believe that because a few decades of research hasnt panned out, that we should all together forget about it.

ben_w 3y ago

LLMs are radically unlike organic minds. Given their performance, I think it is important to pay attention to their weirdnesses — I can call them "intelligent" or "dumb" without contradiction depending on which specific point is under consideration. Transistors outpace biological synapses by the same degree to which a marathon runner outpaces continental drift. This speed difference is what allows computers to read the entire text content of the internet on a regular basis, whereas a human can't read all of just the current version of the English language Wikipedia once in their lifetime. But current AI is very sample-inefficient: if a human were to read as much as an LLM, they would be world experts at everything, not varying between "secondary school" and "fresh graduate" depending on the subject… but even that description is misleading, because humans have the System 1/System 2[0] distinction and limited attention[1], whereas LLMs pay attention to approximately everything in the context window and (seem to) be at a standard between our System 1 and System 2. If you're asking about an LLM's intelligence because you want to replace an intern, then the AI are intelligent; but if you're asking because you want to know how many examples they need in order to decode North Sentinelese or Linear A, then (from what I understand) these AI are extremely stupid. It doesn't matter if a submarine swims[2], it still isn't going to fit into a flooded cave. [0] https://en.wikipedia.org/wiki/Thinking,_Fast_and_Slow https://en.wikipedia.org/wiki/Thinking,_Fast_and_Slow [1] https://youtu.be/vJG698U2Mvo?si=omf3xleqPw5u6Y2k https://youtu.be/vJG698U2Mvo?si=omf3xleqPw5u6Y2k https://youtu.be/ubNF9QNEQLA?si=Ja-9Ak4iCbcxbWdh https://youtu.be/ubNF9QNEQLA?si=Ja-9Ak4iCbcxbWdh https://youtu.be/v3iPrBrGSJM?si=9cKHXEvEGl764Efa https://youtu.be/v3iPrBrGSJM?si=9cKHXEvEGl764Efa https://www.americanbar.org/groups/intellectual_property_law/publications/landslide/2014-15/march-april/that-s-wrap-online-agreements-gaming-kids/ https://www.americanbar.org/groups/intellectual_property_law... [2] https://www.goodreads.com/quotes/32629-the-question-of-whether-a-computer-can-think-is-no https://www.goodreads.com/quotes/32629-the-question-of-wheth...

lukeschlather 3y ago
> if a human were to read as much as an LLM, they would be world experts at everything It would take a human more than one lifetime to read everything most LLMs have read. I often have trouble remembering specifics of something I read an hour ago, never mind a decade ago. I can't imagine a human being an expert in something they read 300 years ago.

FrustratedMonky 3y ago

"ChatGPT and the likes do not have a memory" Can't we consider the context window to be memory? And eventually wont we have larger and larger context windows, and perhaps even individual training where part of the context window 'conversation' is also fed back into the training data? Seems like this will come to pass eventually.

gnramires 3y ago

For reference, I believe relatively rudimentary chatbots have been able to pass forms of the Turing Test for a while, while also being unable to do almost all the things humans can do. It turns out you can make a chatbot quite convincing for a very short conversation with someone you've never met over the internet[1]. I think there's a trend that fooling perception is significantly easier than having the right capabilities. Maybe with sufficiently advanced testing you can judge, but I don't think this is the case in general: in general, our thoughts may be significantly different than we can converse. One obvious example is simply people with disabilities (say a paraplegic) who can't talk at all (while having thoughts of their own): output capability need not necessarily reflect internal capability. Also, take this more advanced example: if you built a sufficiently large lookup table (of course, you need (possibly human) intelligence to build it, and it would be of impractical size), you can build a chatbot completely indistinguishable from a human that nonetheless doesn't seem like it really is intelligent (or conscious for that matter). The only operations it would perform would be some sort of decoding of the input into an astronomically large number to retrieve from the lookup table, and then using some (possibly rudimentary) seeking apparatus to retrieve the content that corresponds to the input. Your input size can be arbitrarily large enabling arbitrarily long conversations. It seems that to judge intelligence we really need to examine the internals and look at their structure. I have a hunch that our particular 'feeling of consciousness' stems from the massive interconnectivity of the brain. All sorts of neurons from around the brain have some activity all the time (I believe the brain's energy consumption doesn't vary greatly with activity, so we use it), and whatever we perceive goes through an enormous number of interconnected neurons (representing concepts, impressions, ideas, etc.); unlike typical CPU-based algorithms that process a somewhat large number (potentially 100s of millions) of steps sequentially. My intuition does seem to pair with modern neural architectures (i.e. they could have some consciousness?), but I really don't know how we could do this sort of judgement before understanding better the details of the brain and other properties of cognition. I think this is a very important research area, and I'm not sure we have enough people working on it or using the correct tools (it relies heavily on logic, philosophy and metaphysical arguments; as well as require personal insight from being conscious :) ). [1] From sources I can't remember, and from wiki: https://en.wikipedia.org/wiki/Turing_test#Loebner_Prize https://en.wikipedia.org/wiki/Turing_test#Loebner_Prize

idiotsecant 3y ago

Intelligence is what you call it when you don't know how it works yet.