4 ms·

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/w

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.
  - philipkglass 10y ago
    I don't want to rule out better algorithms developing very good approximations for what currently appear to be daunting problems like protein folding. But there are a lot of problems like folding out there where even a century of Moore's Law marching on wouldn't be enough to solve large instances with today's algorithms. And Moore's Law looks like it's going to do more shuffling forward than marching forward in the future. So basically I agree with you: there's no generic march-of-progress that assures a world of Kurzweillian wonders. I want to see more concrete evidence of progress in relevant areas before I start entertaining life after the Singularity. (I expect a future profoundly shaped by many "narrow" AIs rather than AGI precisely because there has been so much concrete progress with the former compared to the latter.)
- yourapostasy 10y ago
  This brings to my mind an interesting line of thought that I ran across via Peter Watts writing about it in Blindsight [1]. In that link, page down to the "Sentience/Intelligence" header, and read that section for a concise introduction to this line of thought: sentience is separate from intelligence. Looking at what computer science research and industry has accomplished thus far, I'm very open-minded about the idea that sentience (feeling and perceiving) is a separate, older, deeper layer of brain processing from sapience (reasoning and intelligence). I suspect at a layman's comprehension level that sapience is mostly what we are reproducing in research and industry today, and not sentience, because sentience is far more difficult to plumb. Your explanation of the challenges in quantum methods of simulation and the uncertainty on whether it is even necessary seems to bring up this line of questioning to me. This has interesting ramifications. Like we can build OCR and even put out of work lots of formerly middle-class attorneys who made their living in trial discovery phases. But we have yet to create a second Industrial Revolution with general purpose humanoid-form robots that everyone can own that can clean houses, mow lawns, fold clothes, make beds, wash and chop vegetables, pick strawberries, etc., all supposedly "simple" tasks. http://www.rifters.com/real/Blindsight.htm#Notes http://www.rifters.com/real/Blindsight.htm#Notes
  - ryuker16 10y ago
    Too be fair, labor for those tasks is so cheap to make robots uncompetitive price wise. The money is in putting that lawyer out of work nor hector, the immigrant strawberry picker
    - inimino 10y ago
      I'm not sure what you are responding to, but general-purpose humanoid robots that could be shown how to do things like picking strawberries (rather than a purpose-built strawberry picker) would literally be the world's biggest industry by staggering margins overnight.
    - yourapostasy 10y ago
      The money is in both, and arguably there are greater knock-on effects from automating "labor for those tasks is so cheap to make robots uncompetitive price wise". A humaniform robot imbued with sentience and what little progress we've made on (emulating?) sapience is sufficient to substantially (not entirely replace) disrupt the human labor demand from the following industries. * Residential lawn care for Single Family Residences, a $10B annual industry in the US alone. http://bit.ly/2eu4Ulm * Residential maid service, a $30B annual industry in the US alone. http://bit.ly/2eSQ3oL * Residential home senior health care, a $180B annual industry in the US alone. http://bit.ly/1ufk95Y * If residential meal prep and cooking were mostly or possibly entirely automated, then that can convert the personal chef industry from hundreds of millions into one that poaches a significant portion of the $780B fast casual industry. http://bit.ly/2dJweO0 That's just four industries, with a combined annual revenue of $1T in the US alone, with tremendous consumer demand. There are lots of other possible industries that can be disrupted with such a "simple" robot, especially in the commercial sectors, and globally. It is not a stretch to say such a robot is a multi-trillion dollar opportunity. Suppose you offer a $100K humaniform robot with OTA software updates that can perform most household chores, geofence young children into a specific locality, and basically the sense range, kinesthetic sense of a human, object recognition depth of current AI software, but with only the sapience of what we associate with AI today. Amortize the payments over 30 years, and I expect a billion households will beat a path to your door and happily pay that amount. It would be hard to predict the knock-on economic and other effects of the development of mass-produced personal household robots. Probably similar to trying to predict the demand upon lithium supplies a century from when the Model T was introduced. Perhaps something similarly unexpected like divorce rates dramatically drop due to fewer domestic disputes over household chores because most of those chores disappear. My thesis is that the people building robots are not dumb, and already realize the financial potential in figuring out sentience, and already are aware at some level that the sentience I speak of is incredibly difficult to build because we understand so little about it, and the AI field focuses upon coming in at the problem from the sapience angle/level for the good reason that sapience is closer to our current form of computer technology than sentience.
- IndianAstronaut 10y ago
  I briefly worked on a molecular dynamics project which was for small RNA fragments with atoms numbering in the hundreds. It needed quantum effects simulations to understand two states of the molecule, which could not be explained otherwise. It also required clusters of computers to finish. Modeling proteins is an entire different game. Especially considering the infinite possibilities of proteins and the billions of different proteins that exist in the wild.

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.
- philipkglass 10y ago
  I don't want to rule out better algorithms developing very good approximations for what currently appear to be daunting problems like protein folding. But there are a lot of problems like folding out there where even a century of Moore's Law marching on wouldn't be enough to solve large instances with today's algorithms. And Moore's Law looks like it's going to do more shuffling forward than marching forward in the future. So basically I agree with you: there's no generic march-of-progress that assures a world of Kurzweillian wonders. I want to see more concrete evidence of progress in relevant areas before I start entertaining life after the Singularity. (I expect a future profoundly shaped by many "narrow" AIs rather than AGI precisely because there has been so much concrete progress with the former compared to the latter.)
yourapostasy 10y ago
This brings to my mind an interesting line of thought that I ran across via Peter Watts writing about it in Blindsight [1]. In that link, page down to the "Sentience/Intelligence" header, and read that section for a concise introduction to this line of thought: sentience is separate from intelligence. Looking at what computer science research and industry has accomplished thus far, I'm very open-minded about the idea that sentience (feeling and perceiving) is a separate, older, deeper layer of brain processing from sapience (reasoning and intelligence). I suspect at a layman's comprehension level that sapience is mostly what we are reproducing in research and industry today, and not sentience, because sentience is far more difficult to plumb. Your explanation of the challenges in quantum methods of simulation and the uncertainty on whether it is even necessary seems to bring up this line of questioning to me. This has interesting ramifications. Like we can build OCR and even put out of work lots of formerly middle-class attorneys who made their living in trial discovery phases. But we have yet to create a second Industrial Revolution with general purpose humanoid-form robots that everyone can own that can clean houses, mow lawns, fold clothes, make beds, wash and chop vegetables, pick strawberries, etc., all supposedly "simple" tasks. http://www.rifters.com/real/Blindsight.htm#Notes http://www.rifters.com/real/Blindsight.htm#Notes
- ryuker16 10y ago
  Too be fair, labor for those tasks is so cheap to make robots uncompetitive price wise. The money is in putting that lawyer out of work nor hector, the immigrant strawberry picker
  - inimino 10y ago
    I'm not sure what you are responding to, but general-purpose humanoid robots that could be shown how to do things like picking strawberries (rather than a purpose-built strawberry picker) would literally be the world's biggest industry by staggering margins overnight.
  - yourapostasy 10y ago
    The money is in both, and arguably there are greater knock-on effects from automating "labor for those tasks is so cheap to make robots uncompetitive price wise". A humaniform robot imbued with sentience and what little progress we've made on (emulating?) sapience is sufficient to substantially (not entirely replace) disrupt the human labor demand from the following industries. * Residential lawn care for Single Family Residences, a $10B annual industry in the US alone. http://bit.ly/2eu4Ulm * Residential maid service, a $30B annual industry in the US alone. http://bit.ly/2eSQ3oL * Residential home senior health care, a $180B annual industry in the US alone. http://bit.ly/1ufk95Y * If residential meal prep and cooking were mostly or possibly entirely automated, then that can convert the personal chef industry from hundreds of millions into one that poaches a significant portion of the $780B fast casual industry. http://bit.ly/2dJweO0 That's just four industries, with a combined annual revenue of $1T in the US alone, with tremendous consumer demand. There are lots of other possible industries that can be disrupted with such a "simple" robot, especially in the commercial sectors, and globally. It is not a stretch to say such a robot is a multi-trillion dollar opportunity. Suppose you offer a $100K humaniform robot with OTA software updates that can perform most household chores, geofence young children into a specific locality, and basically the sense range, kinesthetic sense of a human, object recognition depth of current AI software, but with only the sapience of what we associate with AI today. Amortize the payments over 30 years, and I expect a billion households will beat a path to your door and happily pay that amount. It would be hard to predict the knock-on economic and other effects of the development of mass-produced personal household robots. Probably similar to trying to predict the demand upon lithium supplies a century from when the Model T was introduced. Perhaps something similarly unexpected like divorce rates dramatically drop due to fewer domestic disputes over household chores because most of those chores disappear. My thesis is that the people building robots are not dumb, and already realize the financial potential in figuring out sentience, and already are aware at some level that the sentience I speak of is incredibly difficult to build because we understand so little about it, and the AI field focuses upon coming in at the problem from the sapience angle/level for the good reason that sapience is closer to our current form of computer technology than sentience.
IndianAstronaut 10y ago
I briefly worked on a molecular dynamics project which was for small RNA fragments with atoms numbering in the hundreds. It needed quantum effects simulations to understand two states of the molecule, which could not be explained otherwise. It also required clusters of computers to finish. Modeling proteins is an entire different game. Especially considering the infinite possibilities of proteins and the billions of different proteins that exist in the wild.