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I think this is the way that Neural Networks achieve some modicum of generality - chaining them together. Let's say you have a robot that you want to grab a ca

I think this is the way that Neural Networks achieve some modicum of generality - chaining them together. Let's say you have a robot that you want to grab a can of beer off the counter. You say "grab that beer" and point to it. The first neural network interprets the speech and visual input. A second neural network chooses the proper neural nets to continue the task based on the information interpreted from the first net - it picks one for walking and one for grabbing.

randcraw 10y ago
Of course compound task federation like this is not exactly new. Blackboard systems did the very same thing using expert systems back in the 1970s-80s. The problems then remain today: complex and compound tasks don't decompose neatly into well-defined constituent subtasks. Sub-task recombination rapidly devolves into a rat's nest of selecting from competing subtask components that don't have clearly distinct semantics, that are not free of contextual dependencies, nor do they plug-in-and-play independently.
- ced 10y ago
  Do you have any interesting material on this? Where's a good in-depth analysis of blackboard systems' pros and cons?
  - randcraw 10y ago
    I'd start here: https://en.wikipedia.org/wiki/Blackboard_system https://en.wikipedia.org/wiki/Blackboard_system I'm not up-to-date, but I haven't heard of new work on blackboards or federated rule-based systems for maybe 20 years now, after expert systems grew increasingly probabilistic (and procrustean), and BBSes based on binary rules showed little sign of escaping the classic brittleness of RBSes. The wiki article mentions 'Bayesian Blackboard' systems. Maybe they had greater success?
  - joshmarlow 10y ago
    BBS systems also interest me quite a bit. Check out https://en.wikipedia.org/wiki/LIDA_(cognitive_architecture) https://en.wikipedia.org/wiki/LIDA_(cognitive_architecture). It's based on a Global Workspace model (conceptually similar to a Black Board System). You can find papers on LIDA online (including one or two from the references on the wiki page). If you find anything particularly interesting, feel free to share!
westoncb 10y ago
So maybe now we need an NN that figures out the best way of wiring up various pre-existing NNs?
- halflings 10y ago
  Isn't that exactly what the paper is about?
  - westoncb 10y ago
    It's sort of related. In the paper they are still learning parameters for a single new network, as opposed to training a new network to be good at linking up other networks.

randcraw 10y ago

Of course compound task federation like this is not exactly new. Blackboard systems did the very same thing using expert systems back in the 1970s-80s. The problems then remain today: complex and compound tasks don't decompose neatly into well-defined constituent subtasks. Sub-task recombination rapidly devolves into a rat's nest of selecting from competing subtask components that don't have clearly distinct semantics, that are not free of contextual dependencies, nor do they plug-in-and-play independently.

ced 10y ago
Do you have any interesting material on this? Where's a good in-depth analysis of blackboard systems' pros and cons?
- randcraw 10y ago
  I'd start here: https://en.wikipedia.org/wiki/Blackboard_system https://en.wikipedia.org/wiki/Blackboard_system I'm not up-to-date, but I haven't heard of new work on blackboards or federated rule-based systems for maybe 20 years now, after expert systems grew increasingly probabilistic (and procrustean), and BBSes based on binary rules showed little sign of escaping the classic brittleness of RBSes. The wiki article mentions 'Bayesian Blackboard' systems. Maybe they had greater success?
- joshmarlow 10y ago
  BBS systems also interest me quite a bit. Check out https://en.wikipedia.org/wiki/LIDA_(cognitive_architecture) https://en.wikipedia.org/wiki/LIDA_(cognitive_architecture). It's based on a Global Workspace model (conceptually similar to a Black Board System). You can find papers on LIDA online (including one or two from the references on the wiki page). If you find anything particularly interesting, feel free to share!

westoncb 10y ago

So maybe now we need an NN that figures out the best way of wiring up various pre-existing NNs?

halflings 10y ago
Isn't that exactly what the paper is about?
- westoncb 10y ago
  It's sort of related. In the paper they are still learning parameters for a single new network, as opposed to training a new network to be good at linking up other networks.