3 ms·

If you are interested in biologically plausible models of cognition check out 'vector symbolic architectures' and 'associative memory' research of the 90s.

tw1010 9y ago
Biological plausibility is just silly. What it mostly does is just put artificial constraints on the problem that doesn't need to be there. It's all mathematics. However you want to interpret or metaphorize the mathematics is up to each math-phobic field, but constraining yourself to what randomly evolved serves little good.
- 52-6F-62 9y ago
  While it's likely true that there are other ways to approach the problem, we're yet unsure of how to get there. The existing biological model has as least proven itself to work to some level, at least insofar as Descartes has lead us to believe.
  - tw1010 9y ago
    I would bet a lot of money that engineers testing model after model in a state of flow, without any concern for biological validity, will yield a better result than any biologically inspired thing will. Time efficiency is also an important factor here. The model that will win is the one that is first to "market" (academic paper).
    - 52-6F-62 9y ago
      Can't say I'm disagreeing with you. In fact, I'm not. To illustrate, I think it is far more likely we'll have "Jarvis" before we are building our own analogues of the human brain (or better). I do want it to happen, though. And I think it will, in spite of whatever model wins. It's just too interesting a problem. How that takes shape, I can't dream of predicting. (eg. How soon will or understanding of the use of organics in computing advance to such a level)
- bra-ket 9y ago
  If the objective is to simulate general intelligence then you want to copy the natural one as closely as possible. Simulating is easier than inventing.
  - kobeya 9y ago
    False dichotomy. The alternative is not blind trial and error.
- burkaman 9y ago
  It's not silly to try and mimic the only known model that has ever worked. Lots of modern technology is inspired by "randomly evolved" mechanics, it's a perfectly reasonable place to start.
- toss1 9y ago
  Biological plausibility is only silly if you assume that biology has NOT in ~4 billion years of evolution managed to explore the design space and arrive at good general maxima/peak design for intelligent processes and are not merely stuck on the first local peak design encountered. It assumes that outside of this biological design space lies both lower peaks and much higher unexplored peaks. While it is likely that somewhere in the universe are higher peaks of design schemes for intelligence, it is also likely that those peaks are difficult to reach from here (4B+ years of evolution hasn't done it yet). It's also possible that the Fermi Paradox is showing is that there is no substantially better general design schema. So, the more successful approach would probably be to seek designs for the first Automated General Intelligence in a design space that has already demonstrably succeeded, then perhaps use that AGI to search for other better design spaces.
  - corey_moncure 9y ago
    1) One trouble with imitating biology is that stochastically driven processes tend to achieve maxima by exploiting environmental circumstances that are not intended or considered to be part of the design constraint. See for example the genetic-algorithm designed tone differentiator circuit on FPGA, which achieved extreme efficiency but the experimentors could not explain the circuit's behavior based on known theory, moreover, the circuit ceased to function correctly when tested outside of the lab it was 'grown' in. 2) Biology has to satisfy many constraints which are not hard limitations on artificial brains, such as: * Thermodynamics concerns, surface-area to volume ratio * A tightly controlled operating temperature of 97-99 F * Weight (wetware brains must float on CSF because they belong to moving animals) * Size
    - jxy 9y ago
      Point 1, we know. Just look at all kinds of the insects. Point 2, this reads as some advantages compared to our current computers.
selimthegrim 9y ago
http://boulderschool.yale.edu/sites/default/files/files/17_07_Boulder.pdf http://boulderschool.yale.edu/sites/default/files/files/17_0...

tw1010 9y ago

Biological plausibility is just silly. What it mostly does is just put artificial constraints on the problem that doesn't need to be there. It's all mathematics. However you want to interpret or metaphorize the mathematics is up to each math-phobic field, but constraining yourself to what randomly evolved serves little good.

52-6F-62 9y ago
While it's likely true that there are other ways to approach the problem, we're yet unsure of how to get there. The existing biological model has as least proven itself to work to some level, at least insofar as Descartes has lead us to believe.
- tw1010 9y ago
  I would bet a lot of money that engineers testing model after model in a state of flow, without any concern for biological validity, will yield a better result than any biologically inspired thing will. Time efficiency is also an important factor here. The model that will win is the one that is first to "market" (academic paper).
  - 52-6F-62 9y ago
    Can't say I'm disagreeing with you. In fact, I'm not. To illustrate, I think it is far more likely we'll have "Jarvis" before we are building our own analogues of the human brain (or better). I do want it to happen, though. And I think it will, in spite of whatever model wins. It's just too interesting a problem. How that takes shape, I can't dream of predicting. (eg. How soon will or understanding of the use of organics in computing advance to such a level)
bra-ket 9y ago
If the objective is to simulate general intelligence then you want to copy the natural one as closely as possible. Simulating is easier than inventing.
- kobeya 9y ago
  False dichotomy. The alternative is not blind trial and error.
burkaman 9y ago
It's not silly to try and mimic the only known model that has ever worked. Lots of modern technology is inspired by "randomly evolved" mechanics, it's a perfectly reasonable place to start.
toss1 9y ago
Biological plausibility is only silly if you assume that biology has NOT in ~4 billion years of evolution managed to explore the design space and arrive at good general maxima/peak design for intelligent processes and are not merely stuck on the first local peak design encountered. It assumes that outside of this biological design space lies both lower peaks and much higher unexplored peaks. While it is likely that somewhere in the universe are higher peaks of design schemes for intelligence, it is also likely that those peaks are difficult to reach from here (4B+ years of evolution hasn't done it yet). It's also possible that the Fermi Paradox is showing is that there is no substantially better general design schema. So, the more successful approach would probably be to seek designs for the first Automated General Intelligence in a design space that has already demonstrably succeeded, then perhaps use that AGI to search for other better design spaces.
- corey_moncure 9y ago
  1) One trouble with imitating biology is that stochastically driven processes tend to achieve maxima by exploiting environmental circumstances that are not intended or considered to be part of the design constraint. See for example the genetic-algorithm designed tone differentiator circuit on FPGA, which achieved extreme efficiency but the experimentors could not explain the circuit's behavior based on known theory, moreover, the circuit ceased to function correctly when tested outside of the lab it was 'grown' in. 2) Biology has to satisfy many constraints which are not hard limitations on artificial brains, such as: * Thermodynamics concerns, surface-area to volume ratio * A tightly controlled operating temperature of 97-99 F * Weight (wetware brains must float on CSF because they belong to moving animals) * Size
  - jxy 9y ago
    Point 1, we know. Just look at all kinds of the insects. Point 2, this reads as some advantages compared to our current computers.

selimthegrim 9y ago

http://boulderschool.yale.edu/sites/default/files/files/17_07_Boulder.pdf http://boulderschool.yale.edu/sites/default/files/files/17_0...