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The book’s premise is that in about a century, it will be possible to scan human brains at "fine enough spatial and chemical resolution," and to combine that wi

The book’s premise is that in about a century, it will be possible to scan human brains at "fine enough spatial and chemical resolution," and to combine that with good-enough models of signal-processing functions of brain cells, "to create a cell-by-cell dynamically executable model of the full brain in artificial hardware, a model whose signal input-output behavior is usefully close to that of the original brain." Like Bostrom and Kurzweil I think this guy is seriously underestimating the difficulty of biology. I don't believe in souls or non-computable woo going on in our brains. I do believe that biology is insanely complicated compared to computing or physics; physicists, engineers, and people from a computing background who've never worked in biology tend to handwave incredibly difficult problems. See also: the long and mostly-unsuccessful history of people who thought that they could replace animal/cell culture drug testing with models in silico. On top of the sheer audacious difficulty of creating whole brain emulations, there's the problem of competing with boring old "narrow" AI. Surely an economist of all people should recognize the feedback loop that makes narrow AI profitable first, which attracts more investment, which unlocks more capabilities without biological emulation... how many of today's jobs are going to be left un-done by narrow AI a century from now, even if whole brain emulations are finally practical then?

jessriedel 10y ago
You don't have to think that this is the most likely scenario to think this is a very important book. Even if the chance that em AI beats ab initio AI is only 5%, it's a dramatically different and important scenario to consider.
arthurjj 10y ago
He's stated elsewhere he thinks the chances of this scenario are only ~30% IIRC
- davidgerard 10y ago
  Based on what, though? The hazard of dealing with Bayesian epistemologists is that they will confidently state percentages that they literally made up.
  - Houshalter 10y ago
    Based on his own personal belief. Probabilities are a very useful way to precisely state the certainty of beliefs. It means that if you offered him betting odds that it would happen, of less than 1:2, he would take it. And he would bet against it at odds higher than 1:2. Of course betting on the far future is impractical, because money might not be worth anything if it does happen, but in principle that's how these numbers can be interpreted.
    - davidgerard 10y ago
      Yes, I know where Bayesian epistemologists get their numbers. The danger is that they then announce these numbers as if they have any real-world basis, and that they are taken as making a claim about objective reality. There's no evidence I know that pulling a number out of your backside and running it through Bayes' theorem produces better results than just pulling the number out directly and running with it. Indeed, the real-world track record of LessWrong/MIRI (to bring this back to topical relevance) strongly suggests it's less effective.
      - Houshalter 10y ago
        He didn't claim it had "real world basis" or "run it through Bayes' theorem". He just said he expects about a 30% chance he is right.
  - gohrt 10y ago
    Is that different from any other epistemologists?
dealiaro 10y ago
I think a lot of these predictors underestimate prediction itself. To their credit, there is a lot of emphasis on trends in economies or technologies. That's probably the best tool for predicting, but predicting is hard. I think it's hard to really know how complicated "biology" is, from the current perspective of mystery. Who knows what'll be known decades from now. I like Elon Musk's perspective as a starting point though. The trends that all these predictions rely on are man made. Technology improves, gets more powerful and more prevalent when smart people work on it hard. It's not a physical force, it's caused by people. Anyway, if you are expecting narrow AI to do so much, who knows what the relationship between that technology and "true" AI (whatever that means exactly) is. It may not matter much, as you say.
- BOOSTERHIDROGEN 10y ago
  > I like Elon Musk's perspective as a starting point though what is elon's perspective ?
abecedarius 10y ago
There are others who think it an important possibility: http://www.fhi.ox.ac.uk/wp-content/uploads/brain-emulation-roadmap-report1.pdf http://www.fhi.ox.ac.uk/wp-content/uploads/brain-emulation-r...
- philipkglass 10y ago
  I was involved with a research effort to predict certain cell biology features via molecular dynamics simulations between 2003 and 2006. It's what I wrote my thesis on. After school was done and my research group's grant ran out I started writing software for a living instead of looking for another research position. I still keep up with molecular and biological simulation research efforts because it's intrinsically interesting to me. The models are improving and better results are also possible due to the increase in computing power since 2006. But the progress appears slow compared with AI/ML research over the same period. Any time covalent bonds are made or broken, quantum effects are at work. Eliminate all the chemistry that involves forming or breaking bonds and you're throwing away most of the chemistry in a living organism. Getting quantum effects right with ad hoc modifications to molecular dynamics models based on classical physics is very difficult; there's a lot of risk of overfitting to what's easy to observe. Full quantum calculations have terrible scaling properties -- O(N^7) for CCSD(T), the most popular high accuracy approximation of QM, where N is proportional to the number of electrons in the system. Even the best, slowest quantum methods (CCSD(T) and even more exotic methods) still have many cases where they fail to reliably reproduce experimental results. It's possible that you don't need to simulate anything down to the molecular level to reproduce all the biologically interesting features of cells. I don't want to rule it out completely. But the enormous financial incentives to simulate biology in silico (for e.g. accelerating pharmaceutical development), the large research investments made so far, and the limited progress to date tend to make me think that all those tiny and extremely computationally expensive details at the molecular level are biologically relevant.
  - narrator 10y ago
    This has been my main objection to brain inside of a computer thinking. If we could simulate the universe inside of a computer we could do all kinds of neat things, such as drug development in a computer, but it isn't algorithmically possible. The Kurzweillians are just assuming that we'll wave the magic wand and have efficient algorithms for everything such that computer simulation of reality will be trivial. If you have an exponential complexity algorithm that is in the middle of your problem like protein folding for example it will NEVER be practical to simulate in a computer on any non-trivial problem.
woodchuck64 10y ago
> On top of the sheer audacious difficulty of creating whole brain emulations, there's the problem of competing with boring old "narrow" AI. Surely an economist of all people should recognize the feedback loop that makes narrow AI profitable first, which attracts more investment, which unlocks more capabilities without biological emulation... how many of today's jobs are going to be left un-done by narrow AI a century from now, even if whole brain emulations are finally practical then? This is a curious blind spot. With deep learning algorithms making easy work of master Go players, there has to be enormous untapped potential in GPGPU silicon for AI.
bcgraham 10y ago
He talks about the time estimate in the book. He says he understood the need for a time estimate, and so gives the one he thinks is best. But the important thing is that he feels confident it will happen before artificial intelligence that isn't full-brain emulation, whether that's in one century or four centuries.
- RobinHanson 10y ago
  "confident" is too strong. I think the chance is high enough to make the premise worth considering in this detail.
mmusson 10y ago
I think you are missing the main thrust of the book. He is not trying to convince you that em-based AI is the likely future. He chose the less examined possibility as a starting point, but the main part of the book is to enumerate the predictions that flow from that premise. This is interesting and worth examining even if the premise is wrong. Sometimes examining a familiar topic in unfamiliar terrain yields new insights.
- paulddraper 10y ago
  "False premise" is a logical fallacy, not a virtue.
  - FreakLegion 10y ago
    A false premise isn't a logical fallacy. Arguments can be logically valid yet wrong, and vice versa.
    - tashi 10y ago
      Right, and taking a premise you believe to be false and mapping out its consequences has a long and noble history; e.g., non-Euclidean geometry.
    - paulddraper 10y ago
      Okay then...if not a logical fallacy, then just a fallacy ("mistaken belief"). The sort of prognostication that would do poorly on a debate team, because its premises are unsound.
deleted 10y ago
[deleted]
SubiculumCode 10y ago
I attended a primer lecture on tbe immune system. Holy hell. At least 200 different types of T cells. At least 200 other types of immune cells. Then there are all their interactions, signaling, locating, memory functions. I honestly dont know if one can ever get a handle on the complexity.
- AstralStorm 10y ago
  It is not complexity, but diversity. The functions those cells perform are mostly understood as are most mechanisms by which they work. There are many variants of a similar function and mechanism. That said, immunology is still quite young too, but compared to it neuroscience is not even born.
  - SubiculumCode 10y ago
    For a large part we understand how two particles can interact. Simple rules. Put together 200 of those particles, predictions become very difficult and complex.
- laughingman123 10y ago
  Alan Kay's inspiration for OOP and message passing seems to have come from working in microbiology. Maybe thinking/finding about how biology does its computation will be important for machines that can scale very well?
riprowan 10y ago
Yes, biology is terrifically complex and there exists a near-universal tendency to oversimplify it. However, the rate at which humans are improving our ability to understand terrifically complex systems is itself accelerating and 100 years is a very long time.
- CuriouslyC 10y ago
  The initial stages of a sigmoid curve resemble exponential growth. I'm pretty sure that the exponential character we've witnessed is the result of miniaturization of the transistor and the industrialization of large third-world economies, both of which are running out of steam. If we don't get some major new breakthroughs, expect to see the other side of that sigmoid soon enough. We may see another brief exponential curve with the development of carbon nano-computers, likely netting at least an order of magnitude additional computing performance. I have strong doubts about the feasibility of general purpose quantum computing (though it will definitely have niche uses).
inimino 10y ago
If you extrapolate from the last hundred years of progress to the next hundred, it's plausible that there will be at least as much technological progress as these ideas would require. The problem with such prognostication isn't so much that these things can't happen. It is that unexpected events that will happen first turn the future in a different direction.

jessriedel 10y ago

You don't have to think that this is the most likely scenario to think this is a very important book. Even if the chance that em AI beats ab initio AI is only 5%, it's a dramatically different and important scenario to consider.

arthurjj 10y ago

He's stated elsewhere he thinks the chances of this scenario are only ~30% IIRC

davidgerard 10y ago
Based on what, though? The hazard of dealing with Bayesian epistemologists is that they will confidently state percentages that they literally made up.
- Houshalter 10y ago
  Based on his own personal belief. Probabilities are a very useful way to precisely state the certainty of beliefs. It means that if you offered him betting odds that it would happen, of less than 1:2, he would take it. And he would bet against it at odds higher than 1:2. Of course betting on the far future is impractical, because money might not be worth anything if it does happen, but in principle that's how these numbers can be interpreted.
  - davidgerard 10y ago
    Yes, I know where Bayesian epistemologists get their numbers. The danger is that they then announce these numbers as if they have any real-world basis, and that they are taken as making a claim about objective reality. There's no evidence I know that pulling a number out of your backside and running it through Bayes' theorem produces better results than just pulling the number out directly and running with it. Indeed, the real-world track record of LessWrong/MIRI (to bring this back to topical relevance) strongly suggests it's less effective.
    - Houshalter 10y ago
      He didn't claim it had "real world basis" or "run it through Bayes' theorem". He just said he expects about a 30% chance he is right.
- gohrt 10y ago
  Is that different from any other epistemologists?

dealiaro 10y ago

I think a lot of these predictors underestimate prediction itself. To their credit, there is a lot of emphasis on trends in economies or technologies. That's probably the best tool for predicting, but predicting is hard. I think it's hard to really know how complicated "biology" is, from the current perspective of mystery. Who knows what'll be known decades from now. I like Elon Musk's perspective as a starting point though. The trends that all these predictions rely on are man made. Technology improves, gets more powerful and more prevalent when smart people work on it hard. It's not a physical force, it's caused by people. Anyway, if you are expecting narrow AI to do so much, who knows what the relationship between that technology and "true" AI (whatever that means exactly) is. It may not matter much, as you say.

BOOSTERHIDROGEN 10y ago
> I like Elon Musk's perspective as a starting point though what is elon's perspective ?

abecedarius 10y ago

There are others who think it an important possibility: http://www.fhi.ox.ac.uk/wp-content/uploads/brain-emulation-roadmap-report1.pdf http://www.fhi.ox.ac.uk/wp-content/uploads/brain-emulation-r...

philipkglass 10y ago
I was involved with a research effort to predict certain cell biology features via molecular dynamics simulations between 2003 and 2006. It's what I wrote my thesis on. After school was done and my research group's grant ran out I started writing software for a living instead of looking for another research position. I still keep up with molecular and biological simulation research efforts because it's intrinsically interesting to me. The models are improving and better results are also possible due to the increase in computing power since 2006. But the progress appears slow compared with AI/ML research over the same period. Any time covalent bonds are made or broken, quantum effects are at work. Eliminate all the chemistry that involves forming or breaking bonds and you're throwing away most of the chemistry in a living organism. Getting quantum effects right with ad hoc modifications to molecular dynamics models based on classical physics is very difficult; there's a lot of risk of overfitting to what's easy to observe. Full quantum calculations have terrible scaling properties -- O(N^7) for CCSD(T), the most popular high accuracy approximation of QM, where N is proportional to the number of electrons in the system. Even the best, slowest quantum methods (CCSD(T) and even more exotic methods) still have many cases where they fail to reliably reproduce experimental results. It's possible that you don't need to simulate anything down to the molecular level to reproduce all the biologically interesting features of cells. I don't want to rule it out completely. But the enormous financial incentives to simulate biology in silico (for e.g. accelerating pharmaceutical development), the large research investments made so far, and the limited progress to date tend to make me think that all those tiny and extremely computationally expensive details at the molecular level are biologically relevant.
- narrator 10y ago
  This has been my main objection to brain inside of a computer thinking. If we could simulate the universe inside of a computer we could do all kinds of neat things, such as drug development in a computer, but it isn't algorithmically possible. The Kurzweillians are just assuming that we'll wave the magic wand and have efficient algorithms for everything such that computer simulation of reality will be trivial. If you have an exponential complexity algorithm that is in the middle of your problem like protein folding for example it will NEVER be practical to simulate in a computer on any non-trivial problem.

woodchuck64 10y ago

> On top of the sheer audacious difficulty of creating whole brain emulations, there's the problem of competing with boring old "narrow" AI. Surely an economist of all people should recognize the feedback loop that makes narrow AI profitable first, which attracts more investment, which unlocks more capabilities without biological emulation... how many of today's jobs are going to be left un-done by narrow AI a century from now, even if whole brain emulations are finally practical then? This is a curious blind spot. With deep learning algorithms making easy work of master Go players, there has to be enormous untapped potential in GPGPU silicon for AI.

bcgraham 10y ago

He talks about the time estimate in the book. He says he understood the need for a time estimate, and so gives the one he thinks is best. But the important thing is that he feels confident it will happen before artificial intelligence that isn't full-brain emulation, whether that's in one century or four centuries.

RobinHanson 10y ago
"confident" is too strong. I think the chance is high enough to make the premise worth considering in this detail.

mmusson 10y ago

I think you are missing the main thrust of the book. He is not trying to convince you that em-based AI is the likely future. He chose the less examined possibility as a starting point, but the main part of the book is to enumerate the predictions that flow from that premise. This is interesting and worth examining even if the premise is wrong. Sometimes examining a familiar topic in unfamiliar terrain yields new insights.

paulddraper 10y ago
"False premise" is a logical fallacy, not a virtue.
- FreakLegion 10y ago
  A false premise isn't a logical fallacy. Arguments can be logically valid yet wrong, and vice versa.
  - tashi 10y ago
    Right, and taking a premise you believe to be false and mapping out its consequences has a long and noble history; e.g., non-Euclidean geometry.
  - paulddraper 10y ago
    Okay then...if not a logical fallacy, then just a fallacy ("mistaken belief"). The sort of prognostication that would do poorly on a debate team, because its premises are unsound.

deleted 10y ago

[deleted]

SubiculumCode 10y ago

I attended a primer lecture on tbe immune system. Holy hell. At least 200 different types of T cells. At least 200 other types of immune cells. Then there are all their interactions, signaling, locating, memory functions. I honestly dont know if one can ever get a handle on the complexity.

AstralStorm 10y ago
It is not complexity, but diversity. The functions those cells perform are mostly understood as are most mechanisms by which they work. There are many variants of a similar function and mechanism. That said, immunology is still quite young too, but compared to it neuroscience is not even born.
- SubiculumCode 10y ago
  For a large part we understand how two particles can interact. Simple rules. Put together 200 of those particles, predictions become very difficult and complex.
laughingman123 10y ago
Alan Kay's inspiration for OOP and message passing seems to have come from working in microbiology. Maybe thinking/finding about how biology does its computation will be important for machines that can scale very well?

riprowan 10y ago

Yes, biology is terrifically complex and there exists a near-universal tendency to oversimplify it. However, the rate at which humans are improving our ability to understand terrifically complex systems is itself accelerating and 100 years is a very long time.

CuriouslyC 10y ago
The initial stages of a sigmoid curve resemble exponential growth. I'm pretty sure that the exponential character we've witnessed is the result of miniaturization of the transistor and the industrialization of large third-world economies, both of which are running out of steam. If we don't get some major new breakthroughs, expect to see the other side of that sigmoid soon enough. We may see another brief exponential curve with the development of carbon nano-computers, likely netting at least an order of magnitude additional computing performance. I have strong doubts about the feasibility of general purpose quantum computing (though it will definitely have niche uses).

inimino 10y ago

If you extrapolate from the last hundred years of progress to the next hundred, it's plausible that there will be at least as much technological progress as these ideas would require. The problem with such prognostication isn't so much that these things can't happen. It is that unexpected events that will happen first turn the future in a different direction.