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> "In Pregel, programs are expressed as a sequence of iterations. In each iteration, a vertex can, independently of other vertices, receive messages sent to it
by rw 17y ago
> "In Pregel, programs are expressed as a sequence of iterations. In each iteration, a vertex can, independently of other vertices, receive messages sent to it in the previous iteration, send messages to other vertices, modify its own and its outgoing edges' states, and mutate the graph's topology [...]"
This sounds very similar to how neural networks are updated through time. Could this be used to easily simulate biological cognition? Am I missing something? And, scalability isn't a problem:
> "Currently, Pregel scales to billions of vertices and edges, but this limit will keep expanding."
The human brain only has about 100 billion neurons.
- alexgartrell 17y agoBut how long until it's fast enough?
- tricky 17y agoIt doesn't have to be... at first. Prove it works and everyone will bust ass in a space race to design fast hardware.
- ivankirigin 17y agoThere are billions of neurons but many more connections between them then are probably typical in the graphs they compute. Even complex road conditions or a set of links from a page probably rarely number more than 1K - the ballpark average number of connection per neuron. Others are directly working on simulating brains. It's a hard problem.
- tricky 17y agoI would love to implement a graph like this where each node used bayesian inference to act on messages received from connected vertices. My gut says if you could get the system's state to settle to an equilibrium, it could react to changes in external signals in a probabilistic way and learn. Instead I'm busy working on an iphone app...
- kvh 17y agohttp://en.wikipedia.org/wiki/Bayesian_network http://en.wikipedia.org/wiki/Bayesian_network
- mattj 17y agoCheck out en.wikipedia.org/wiki/Junction_tree_algorithm not a great article, but the referenced literature is ok Don't have much time to elaborate at the moment, but look up the "junction tree algorithm"- it's a way of performing inference in graph-structured statistical models. You think of edges as relationships between random variable (which are the nodes), and have the nodes communicate with each other until all the signals have propogated. Makes inference straightforward, though still exponential
- DocSavage 17y agoThe number of "iterations" in biological cognition is quite small. You don't get many synaptic transmissions in the time it takes for us to have some act of cognition.