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Progress in AI is due to data and computational power advances. I wonder what kind of advances are needed for AGI. 1. Biological brains are non-differentiable

Progress in AI is due to data and computational power advances. I wonder what kind of advances are needed for AGI. 1. Biological brains are non-differentiable spiking networks much more complicated than backpropagated ANNs. 2. Ion channels may or may not be affected by quantum effects. 3. The search space is huge (but organisms aren't optimal and natural selection is probably local search) 4. If it took ~3.8b years to get from cells to humans, how do we fast-forward: * brain mapping (replicating the biological "architecture") * gene editing on animal models to build tissues and/or brains that can be interfaced (and if such interface could exist how do we prevent someone from trying to use human slaves as computers? Using which tissues for computation is torture?) * simulation with computational models outside of ECT (quantum computers or some new physics phenomenon) Note: those 3.8b years are from a cell to human. We haven't built anything remotely similar to a cell. And I'm not claiming that an AGI system will need cells or spiking nets, most likely a lot of those are redundant. But the entropy and complexity of biological systems is huge and even rodents can outperform state of the art models at general tasks. IMHO, the quickest path to AGI would be to focus on climate change and making academia more appealing.

m0zg 7y ago
You forgot to mention, crucially, that neurons in close proximity affect each other, which is just one of the things that makes modeling of more than a few neurons in time domain a complete non-starter. It all results in enormous systems of PDEs which we don't know how to solve yet at all. You could say that we do not have the right mathematical apparatus to model any such thing.
- McTossOut 7y ago
  Pure physical modeling are likely a bad representation for the phenomena resulting in intelligence, especially granted we've simulated it with much simpler discrete structures. PDEs may even be disastrously bad, like trying to describe a line in space with a table of points, instead of the degrees of freedom. I would imagine that a PDE may cover diffuse behaviors governing say, how learning happens mechanically, but it is almost certainly a language/representational barrier, the relationship between the structure of the animal mind, learning, and seemingly simple phenomena, like afterimages. The molecules are arbitrary and the timescale doesn't matter.
- TaupeRanger 7y ago
  I don't follow that. What would prevent (perhaps quite slow) simulation of a larger system of such neurons? E.g. N-body problems are analytically beyond us, but can be simulated to arbitrary precision with certain trade-offs.
  - m0zg 7y ago
    Time domain solutions do not exist for more than a dozen neurons. At least they did not when I took a computational neuroscience MOOC a couple of years ago. State of the art at the time was the nervous system of an earthworm. That is, if you consider what you actually need to do to simulate how potentials will change in the brain over time give a certain starting state and stimuli, the math gets so complicated (and awkward) so quickly that it's not really tractable with the mathematical (or simulation) apparatus we currently have to go beyond such trivial systems.
catalogia 7y ago
From what I understand, quantum effects being essential to the process is a fringe belief. Penrose is probably the most famous 'serious person' (sorry Deepak Chopra) to espouse the idea, but I'm inclined to believe that might be a Linus Pauling/Vitamin C sort of scenario. Penrose started from the perspective of believing there must be quantum effects, then began fishing for physical evidence of it.
- hmmmhmmmhmmm 7y ago
  One interesting hypothesis, re: lithium isotopes in Posner molecules: https://www.kitp.ucsb.edu/sites/default/files/users/mpaf/p178a.pdf https://www.kitp.ucsb.edu/sites/default/files/users/mpaf/p17...
- whymauri 7y ago
  I was taught that the quantum theory of memory and cognition generally falls under Eric Schwartz's "neuro-bagging" fallacy [0]. That is: >You assert that an area of physics or mathematics familiar to few neuroscientists solves a fundamental problem in their field. Example: "The cerebellum is a tensor of rank 10^12; sensory and motor activity is contravariant and covariant vectors". So yeah, I feel that it's pretty fringe (as you suggested). [0] https://web.archive.org/web/20170828092031/http://cns-web.bu.edu/~eric/comp_neuro_tricks.html https://web.archive.org/web/20170828092031/http://cns-web.bu...
- pvarangot 7y ago
  Yeah, "quantum mechanics and cognition are very complex and therefore equivalent", sorry I don't know who to attribute the quote to.
  - pgcj_poster 7y ago
    I think you're recalling the end of this comic[1], which was on the front page of HN a couple weeks ago. So the quote is probably attributable to either Scott Aaronson or Zach Weinersmith. [1] https://www.smbc-comics.com/comic/the-talk-3 https://www.smbc-comics.com/comic/the-talk-3
wahern 7y ago
I'm sure neural nets will herald AI right after the mechanical gears and pneumatic pistons that were envisioned as the secret sauce during the turn of the last century. The key, of course, is redefining life and intelligence as whatever the current state-of-the-art accomplishes. (Cue explanations that the brain is just a giant pattern matcher.) It makes drawing parallels and prophesying advancements so much easier. Of all our sciences, that's perhaps the one thing we've perfected--the science of equivocation. And we perfected it long ago; perhaps even millennia ago.
akira2501 7y ago
> gene editing Gene expression is often tied to the environment the organism is in. Mere possession a gene isn't enough to benefit from it. Some expressions don't take effect immediately, but rather activate in subsequent generations. Epigenetics is a whole equally large layer on top of this system. A single-focus approach may not be sufficient, and even if it is, it's not likely to cope with environmental entropy very well.
- wahern 7y ago
  If you can craft a gene[1] to express some particular phenotype (a big if), surely you can craft it to express itself without reliance on epigentic[2] chemistry. [1] I understand gene to mean some ill-defined, not necessarily contiguous set of genetic sequences (DNA, RNA, and analogs) with an identifiable, particularized expression that effects reproductive (specifically, replicative) success. I think over time "gene" has been redefined and narrowed in a way to make it easier to claim to have made supposedly model-breaking discoveries. [2] Some others on HN have made strong cases for why epigenetics isn't a meaningful departure from the classic genetic model; just a cautionary tail for eager reductivists who would draw unsupported conclusions from the classic model. See, also, note #1.
JaRail 7y ago
> Progress in AI is due to data and computational power advances. I think you'd be surprised how much progress is also being made outside those two factors. It's sort of like saying graphics only improve with more RAM and faster compute. We know there's more to it than that. In many cases, the cutting edge of a few years ago is easily bested by today's tutorial samples and 30 seconds of training. We're doing better with less data and orders of magnitude less compute.
- draw_down 7y ago
  I think it’s meant precisely in contrast to something like graphics, where the human element has obviously contributed alongside computational advances. “The Bitter Lesson”, basically. To the other point, aren’t computational advances the reason that it’s only 30 seconds of training?
- goatlover 7y ago
  But not towards AGI. We're just improving on narrow AI after recent breakthroughs thanks to the hardware being powerful enough and large datasets being available.
  - finebalance 7y ago
    The point the poster above is trying to make is that given the same amount of data, improvements in technique is leading to significant improvements in accuracy. An illustrative example comes from the first lesson in fastai's deep learning course: an image classifier that would have been SOTA as late as 2012/13, can be built by the hobbyist in like 30 seconds. That said, I don't disagree that this is all narrow AI, at best.
  - redisman 7y ago
    Having access to cheap and scalable compute and storage should be helpful for AGI too. It doesn't solve anything but it does give more access to more people.
buboard 7y ago
> even rodents can outperform state of the art models at general tasks Rodents can't play Go or a lot of other humanly-meaningful tasks. We don't need to build an artificial cell. A cell is too many components that by blind luck happened to find ways to work together, this is as far from efficient design as can be. The same way we don't build two-legged airplanes, we don't need anything that's close to the wet spiky mess that happens in human brains. It's more likely that we have all the ingredients already in ML, and we need to connect them in an ingenious way and amp up the parallelism.
- romwell 7y ago
  The problem with the analogy is that the car, by far, is not a general transportation device. Practically, most cars are solving a very constrained transportation problem: moving on roads that humans made. We don't have anything remotely close to a wetware-enabled transportation device, something that can move on flat land, climb mountains, swim in bodies of water, crawl in caves, hide in trees. Within the constrained problem, the machine exceeds humans. But generally, the wetware handles moving around much better. Same with AI: in a constrained problem, the AI can excel (beat humans in chess and go). But I doubt we will see a general AI any time soon.
  - buboard 7y ago
    > constrained problem human AI also evolved by solving constrained problems, one at a time. Life existed before the visual system , but once this was solved it moved on to do other things. In AI we have a number of sensory systems seemingly solved: Speech recognition, visual object recognition, and we are closing to certain output (motor) systems: NLP text synthesis systems seem a lot like the central pattern generators that control human gait, except for language. What seems to be missing is the "higher-level ", more abstract kernels that create intent, which are also difficult to train because we don't have a lot of meaningful datasets. Or maybe , we have too big datasets (the entirety of wikipedia) but we don't know how to encode it in a meaningful way for training. It's not clear however that these "integrating systems" are going to be fundamentally different to solve than other subsystems. It certainly doesn't seem to be so in the brain, since neocortex (which hosts both sensory and motor and higher level systems) is rather homogeneous. In any case, it seems we 're solving problems one after another without copying nature's designs, so it's not automatically true that we need to copy nature in order to keep solving more.
pron 7y ago
> even rodents can outperform state of the art models at general tasks. Rodents? Try insects [1]. In the late 40s and early 50s, when neural networks were first explored with great enthusiasm, some of the leading minds of that generation believed (were convinced, in fact) that artificial intelligence (or AGI in today's terms) is five/ten years away; the skeptics, like Alan Turing, thought it was fifty years away. Seventy years later and we've not achieved insect-level intelligence, we don't know what path would lead us to insect-level intelligence, and we don't know how long it would take to get there. [1]: To those saying that insects or rodents can't play Go or chess -- they can't sort numbers, either, and even early computers did it better than humans.
- jcims 7y ago
  This jumping spider has ~600k neurons in its brain - https://youtu.be/UDtlvZGmHYk https://youtu.be/UDtlvZGmHYk They are creepy smart.
  - hn_throwaway_99 7y ago
    Just wanted to say holy crap that video was amazing - exciting and suspenseful!
    - jcims 7y ago
      Here's another one for ya if you get stuck with a case of the nosleeps - https://www.youtube.com/watch?v=7wKu13wmHog https://www.youtube.com/watch?v=7wKu13wmHog Something about predatory nature of both insects seems to tune up their intelligence. Of course it never hurts having the BBC tell your story either.
      - shrimp_emoji 7y ago
        >Something about predatory nature of both insects seems to tune up their intelligence. Yep. To be a predator, you need to outwit your prey and think fast, so it's thought to be a natural INT grinder. `w´ Presumably, this could drive up the INT of prey too, but maybe it's cheaper to just be faster/harder to see? But you can't be THAT hard to see, and the speed only saves you in failed ambushes, so planning successful ambushes continues to reward the INT of predators (unless they just enter the speed arms race, like cheetahs or tiger beetles).
debt 7y ago
We still haven’t solved language nor intelligence. Like what is language, what is intelligence? Some of the smartest linguists and philosophers would proudly declare they have no fucking clue. Making Alexa turn on the lights or using Google Translate are cool party tricks though. Idc how many Doom games ya made, but I’m sorry to say a bunch of software engineers aren’t gonna crack this one.
- jodrellblank 7y ago
  > Some of the smartest linguists and philosophers would proudly declare they have no fucking clue. “to worship a phenomenon because it seems so wonderfully mysterious, is to worship your own ignorance” - https://www.lesswrong.com/posts/x4dG4GhpZH2hgz59x/joy-in-the-merely-real https://www.lesswrong.com/posts/x4dG4GhpZH2hgz59x/joy-in-the... Having no clue is not something to be proud (or ashamed) of. > I’m sorry to say a bunch of software engineers aren’t gonna crack this one. Doesn’t sound like you’re at all sorry, it sounds like you’re thrilling in putting these uppity tryhards in their place for daring to attack something you hold sacred.
air7 7y ago
This reminds me of a interesting armchair moral dilemma: Assume we have the tech to replicate/simulate a biological brain. Now say we want to study the effects of extreme pain/torture etc on the brain. Instead of studying living animals or humans we'd just simulate a brain, and simulate sending it pain signals and see what happens. But, if this is a 100% replicated brain, doesn't that mean its suffering is just as real as a real brain's suffering, and therefor just as cruel? And if not, what's the difference?
- lonelappde 7y ago
  https://en.m.wikipedia.org/wiki/Ethics_of_artificial_intelligence#Robot_rights https://en.m.wikipedia.org/wiki/Ethics_of_artificial_intelli...
- balfirevic 7y ago
  > But, if this is a 100% replicated brain, doesn't that mean its suffering is just as real as a real brain's suffering, and therefor just as cruel? Yes, it does.
  - ars 7y ago
    Or, assuming you don't believe in souls, "real" brain's suffering isn't real either. (The brain is just a machine, right?) This reminds me of the idea that free will doesn't exist, but that we have to act as if it were. So by analogy to that, maybe the AI isn't really suffering, but you have to act as if it were. More food for thought: Some surgery blocks memory but can be incredibly painful. Do we need to worry about that? Is the suffering that the brain can not remember "real"?
    - mercer 7y ago
      I think the word 'real' is way too vague in this context.
- rfhjt 7y ago
  Fwiw, after a certain amount of pain, brain "transcends it": everything disappears, there are some curious colors here and there, but there is no pain. Experienced that during an in ear infection.
Balgair 7y ago
> 2. Ion channels may or may not be affected by quantum effects. In a sense, everything is affected by quantum effects. However, with neurons, they are generally large enough that quantum effects do not dominate. Voltage gated channels are dozens to hundreds of amino-acids long. Generally, there are hundreds to millions of ion channels in a cell membrane and the quantum tunneling of a few sodium ions in or out of the cell will generally not affect gestalt behavior of the cell, let alone a nervous system's long term state. Suffice to say, ion channels are not dominated by quantum behavior. Largely, we have the building blocks to replicate neurons (as we currently understand them) in silico. However, as is typical with modeling, you get out what you put in. Meaning that how you set your models up will mostly determine what they do. Setting your net size, the parameters of you PDEs, boundary values, etc. are the most important things. Now, that gets you a result, and it's likely to take a fair bit of time to run through. To get it up to real time the limiting factor really ends up being heat. Silicon takes a LOT of energy as compared to our heads, ~10^4 more per 'neuron'. If we want to get to real time, we're gonna need to deal with the entropy.
andbberger 7y ago
> 1. Biological brains are non-differentiable spiking networks much more complicated than backpropagated ANNs. Actually it's not so obvious that the brain is not differentiable. If you do a cursory search, you'll find quite a lot of research into biologically plausible mechanism for backpropagation. Not saying the brain does backprop, we just don't know and it's not outside of the realm of plausibility

m0zg 7y ago

You forgot to mention, crucially, that neurons in close proximity affect each other, which is just one of the things that makes modeling of more than a few neurons in time domain a complete non-starter. It all results in enormous systems of PDEs which we don't know how to solve yet at all. You could say that we do not have the right mathematical apparatus to model any such thing.

McTossOut 7y ago
Pure physical modeling are likely a bad representation for the phenomena resulting in intelligence, especially granted we've simulated it with much simpler discrete structures. PDEs may even be disastrously bad, like trying to describe a line in space with a table of points, instead of the degrees of freedom. I would imagine that a PDE may cover diffuse behaviors governing say, how learning happens mechanically, but it is almost certainly a language/representational barrier, the relationship between the structure of the animal mind, learning, and seemingly simple phenomena, like afterimages. The molecules are arbitrary and the timescale doesn't matter.
TaupeRanger 7y ago
I don't follow that. What would prevent (perhaps quite slow) simulation of a larger system of such neurons? E.g. N-body problems are analytically beyond us, but can be simulated to arbitrary precision with certain trade-offs.
- m0zg 7y ago
  Time domain solutions do not exist for more than a dozen neurons. At least they did not when I took a computational neuroscience MOOC a couple of years ago. State of the art at the time was the nervous system of an earthworm. That is, if you consider what you actually need to do to simulate how potentials will change in the brain over time give a certain starting state and stimuli, the math gets so complicated (and awkward) so quickly that it's not really tractable with the mathematical (or simulation) apparatus we currently have to go beyond such trivial systems.

catalogia 7y ago

From what I understand, quantum effects being essential to the process is a fringe belief. Penrose is probably the most famous 'serious person' (sorry Deepak Chopra) to espouse the idea, but I'm inclined to believe that might be a Linus Pauling/Vitamin C sort of scenario. Penrose started from the perspective of believing there must be quantum effects, then began fishing for physical evidence of it.

hmmmhmmmhmmm 7y ago
One interesting hypothesis, re: lithium isotopes in Posner molecules: https://www.kitp.ucsb.edu/sites/default/files/users/mpaf/p178a.pdf https://www.kitp.ucsb.edu/sites/default/files/users/mpaf/p17...
whymauri 7y ago
I was taught that the quantum theory of memory and cognition generally falls under Eric Schwartz's "neuro-bagging" fallacy [0]. That is: >You assert that an area of physics or mathematics familiar to few neuroscientists solves a fundamental problem in their field. Example: "The cerebellum is a tensor of rank 10^12; sensory and motor activity is contravariant and covariant vectors". So yeah, I feel that it's pretty fringe (as you suggested). [0] https://web.archive.org/web/20170828092031/http://cns-web.bu.edu/~eric/comp_neuro_tricks.html https://web.archive.org/web/20170828092031/http://cns-web.bu...
pvarangot 7y ago
Yeah, "quantum mechanics and cognition are very complex and therefore equivalent", sorry I don't know who to attribute the quote to.
- pgcj_poster 7y ago
  I think you're recalling the end of this comic[1], which was on the front page of HN a couple weeks ago. So the quote is probably attributable to either Scott Aaronson or Zach Weinersmith. [1] https://www.smbc-comics.com/comic/the-talk-3 https://www.smbc-comics.com/comic/the-talk-3

wahern 7y ago

I'm sure neural nets will herald AI right after the mechanical gears and pneumatic pistons that were envisioned as the secret sauce during the turn of the last century. The key, of course, is redefining life and intelligence as whatever the current state-of-the-art accomplishes. (Cue explanations that the brain is just a giant pattern matcher.) It makes drawing parallels and prophesying advancements so much easier. Of all our sciences, that's perhaps the one thing we've perfected--the science of equivocation. And we perfected it long ago; perhaps even millennia ago.

akira2501 7y ago

> gene editing Gene expression is often tied to the environment the organism is in. Mere possession a gene isn't enough to benefit from it. Some expressions don't take effect immediately, but rather activate in subsequent generations. Epigenetics is a whole equally large layer on top of this system. A single-focus approach may not be sufficient, and even if it is, it's not likely to cope with environmental entropy very well.

wahern 7y ago
If you can craft a gene[1] to express some particular phenotype (a big if), surely you can craft it to express itself without reliance on epigentic[2] chemistry. [1] I understand gene to mean some ill-defined, not necessarily contiguous set of genetic sequences (DNA, RNA, and analogs) with an identifiable, particularized expression that effects reproductive (specifically, replicative) success. I think over time "gene" has been redefined and narrowed in a way to make it easier to claim to have made supposedly model-breaking discoveries. [2] Some others on HN have made strong cases for why epigenetics isn't a meaningful departure from the classic genetic model; just a cautionary tail for eager reductivists who would draw unsupported conclusions from the classic model. See, also, note #1.

JaRail 7y ago

> Progress in AI is due to data and computational power advances. I think you'd be surprised how much progress is also being made outside those two factors. It's sort of like saying graphics only improve with more RAM and faster compute. We know there's more to it than that. In many cases, the cutting edge of a few years ago is easily bested by today's tutorial samples and 30 seconds of training. We're doing better with less data and orders of magnitude less compute.

draw_down 7y ago
I think it’s meant precisely in contrast to something like graphics, where the human element has obviously contributed alongside computational advances. “The Bitter Lesson”, basically. To the other point, aren’t computational advances the reason that it’s only 30 seconds of training?
goatlover 7y ago
But not towards AGI. We're just improving on narrow AI after recent breakthroughs thanks to the hardware being powerful enough and large datasets being available.
- finebalance 7y ago
  The point the poster above is trying to make is that given the same amount of data, improvements in technique is leading to significant improvements in accuracy. An illustrative example comes from the first lesson in fastai's deep learning course: an image classifier that would have been SOTA as late as 2012/13, can be built by the hobbyist in like 30 seconds. That said, I don't disagree that this is all narrow AI, at best.
- redisman 7y ago
  Having access to cheap and scalable compute and storage should be helpful for AGI too. It doesn't solve anything but it does give more access to more people.

buboard 7y ago

> even rodents can outperform state of the art models at general tasks Rodents can't play Go or a lot of other humanly-meaningful tasks. We don't need to build an artificial cell. A cell is too many components that by blind luck happened to find ways to work together, this is as far from efficient design as can be. The same way we don't build two-legged airplanes, we don't need anything that's close to the wet spiky mess that happens in human brains. It's more likely that we have all the ingredients already in ML, and we need to connect them in an ingenious way and amp up the parallelism.

romwell 7y ago
The problem with the analogy is that the car, by far, is not a general transportation device. Practically, most cars are solving a very constrained transportation problem: moving on roads that humans made. We don't have anything remotely close to a wetware-enabled transportation device, something that can move on flat land, climb mountains, swim in bodies of water, crawl in caves, hide in trees. Within the constrained problem, the machine exceeds humans. But generally, the wetware handles moving around much better. Same with AI: in a constrained problem, the AI can excel (beat humans in chess and go). But I doubt we will see a general AI any time soon.
- buboard 7y ago
  > constrained problem human AI also evolved by solving constrained problems, one at a time. Life existed before the visual system , but once this was solved it moved on to do other things. In AI we have a number of sensory systems seemingly solved: Speech recognition, visual object recognition, and we are closing to certain output (motor) systems: NLP text synthesis systems seem a lot like the central pattern generators that control human gait, except for language. What seems to be missing is the "higher-level ", more abstract kernels that create intent, which are also difficult to train because we don't have a lot of meaningful datasets. Or maybe , we have too big datasets (the entirety of wikipedia) but we don't know how to encode it in a meaningful way for training. It's not clear however that these "integrating systems" are going to be fundamentally different to solve than other subsystems. It certainly doesn't seem to be so in the brain, since neocortex (which hosts both sensory and motor and higher level systems) is rather homogeneous. In any case, it seems we 're solving problems one after another without copying nature's designs, so it's not automatically true that we need to copy nature in order to keep solving more.

pron 7y ago

> even rodents can outperform state of the art models at general tasks. Rodents? Try insects [1]. In the late 40s and early 50s, when neural networks were first explored with great enthusiasm, some of the leading minds of that generation believed (were convinced, in fact) that artificial intelligence (or AGI in today's terms) is five/ten years away; the skeptics, like Alan Turing, thought it was fifty years away. Seventy years later and we've not achieved insect-level intelligence, we don't know what path would lead us to insect-level intelligence, and we don't know how long it would take to get there. [1]: To those saying that insects or rodents can't play Go or chess -- they can't sort numbers, either, and even early computers did it better than humans.

jcims 7y ago
This jumping spider has ~600k neurons in its brain - https://youtu.be/UDtlvZGmHYk https://youtu.be/UDtlvZGmHYk They are creepy smart.
- hn_throwaway_99 7y ago
  Just wanted to say holy crap that video was amazing - exciting and suspenseful!
  - jcims 7y ago
    Here's another one for ya if you get stuck with a case of the nosleeps - https://www.youtube.com/watch?v=7wKu13wmHog https://www.youtube.com/watch?v=7wKu13wmHog Something about predatory nature of both insects seems to tune up their intelligence. Of course it never hurts having the BBC tell your story either.
    - shrimp_emoji 7y ago
      >Something about predatory nature of both insects seems to tune up their intelligence. Yep. To be a predator, you need to outwit your prey and think fast, so it's thought to be a natural INT grinder. `w´ Presumably, this could drive up the INT of prey too, but maybe it's cheaper to just be faster/harder to see? But you can't be THAT hard to see, and the speed only saves you in failed ambushes, so planning successful ambushes continues to reward the INT of predators (unless they just enter the speed arms race, like cheetahs or tiger beetles).

debt 7y ago

We still haven’t solved language nor intelligence. Like what is language, what is intelligence? Some of the smartest linguists and philosophers would proudly declare they have no fucking clue. Making Alexa turn on the lights or using Google Translate are cool party tricks though. Idc how many Doom games ya made, but I’m sorry to say a bunch of software engineers aren’t gonna crack this one.

jodrellblank 7y ago
> Some of the smartest linguists and philosophers would proudly declare they have no fucking clue. “to worship a phenomenon because it seems so wonderfully mysterious, is to worship your own ignorance” - https://www.lesswrong.com/posts/x4dG4GhpZH2hgz59x/joy-in-the-merely-real https://www.lesswrong.com/posts/x4dG4GhpZH2hgz59x/joy-in-the... Having no clue is not something to be proud (or ashamed) of. > I’m sorry to say a bunch of software engineers aren’t gonna crack this one. Doesn’t sound like you’re at all sorry, it sounds like you’re thrilling in putting these uppity tryhards in their place for daring to attack something you hold sacred.

air7 7y ago

This reminds me of a interesting armchair moral dilemma: Assume we have the tech to replicate/simulate a biological brain. Now say we want to study the effects of extreme pain/torture etc on the brain. Instead of studying living animals or humans we'd just simulate a brain, and simulate sending it pain signals and see what happens. But, if this is a 100% replicated brain, doesn't that mean its suffering is just as real as a real brain's suffering, and therefor just as cruel? And if not, what's the difference?

lonelappde 7y ago
https://en.m.wikipedia.org/wiki/Ethics_of_artificial_intelligence#Robot_rights https://en.m.wikipedia.org/wiki/Ethics_of_artificial_intelli...
balfirevic 7y ago
> But, if this is a 100% replicated brain, doesn't that mean its suffering is just as real as a real brain's suffering, and therefor just as cruel? Yes, it does.
- ars 7y ago
  Or, assuming you don't believe in souls, "real" brain's suffering isn't real either. (The brain is just a machine, right?) This reminds me of the idea that free will doesn't exist, but that we have to act as if it were. So by analogy to that, maybe the AI isn't really suffering, but you have to act as if it were. More food for thought: Some surgery blocks memory but can be incredibly painful. Do we need to worry about that? Is the suffering that the brain can not remember "real"?
  - mercer 7y ago
    I think the word 'real' is way too vague in this context.
rfhjt 7y ago
Fwiw, after a certain amount of pain, brain "transcends it": everything disappears, there are some curious colors here and there, but there is no pain. Experienced that during an in ear infection.

Balgair 7y ago

> 2. Ion channels may or may not be affected by quantum effects. In a sense, everything is affected by quantum effects. However, with neurons, they are generally large enough that quantum effects do not dominate. Voltage gated channels are dozens to hundreds of amino-acids long. Generally, there are hundreds to millions of ion channels in a cell membrane and the quantum tunneling of a few sodium ions in or out of the cell will generally not affect gestalt behavior of the cell, let alone a nervous system's long term state. Suffice to say, ion channels are not dominated by quantum behavior. Largely, we have the building blocks to replicate neurons (as we currently understand them) in silico. However, as is typical with modeling, you get out what you put in. Meaning that how you set your models up will mostly determine what they do. Setting your net size, the parameters of you PDEs, boundary values, etc. are the most important things. Now, that gets you a result, and it's likely to take a fair bit of time to run through. To get it up to real time the limiting factor really ends up being heat. Silicon takes a LOT of energy as compared to our heads, ~10^4 more per 'neuron'. If we want to get to real time, we're gonna need to deal with the entropy.

andbberger 7y ago

> 1. Biological brains are non-differentiable spiking networks much more complicated than backpropagated ANNs. Actually it's not so obvious that the brain is not differentiable. If you do a cursory search, you'll find quite a lot of research into biologically plausible mechanism for backpropagation. Not saying the brain does backprop, we just don't know and it's not outside of the realm of plausibility