This is just me rambling but the thought of using natural physics to solve complex computation as described here [1] got me thinking about using humans to do complex computation as part of a neural net. Is it possible to create a neural net where the neurons or activation functions are humans and only the communication between the humans (i.e. the weights/synapses) need to be optimized. Does anyone know if there's experiments done for this? I know of one thought experiment which is similar but for another purpose called the China brain [2].<p>* The idea is that you can make an AGI out of a network of humans. The individual humans within the network needn't know how they are contributing to the overall intelligence of the AGI.<p>* The communication between neighboring humans in the network can happen via sending emails, over hand help radios, yelling or even dockerized containers. The optimization algorithm determines to whom these messages are sent and with what "weight".<p>* The inputs and outputs need not be trivial, they can be arbitrarily complex, such as numbers, theorems, songs or questions being posed and answered. These inputs and outputs need only be "differentiable" either numerically or even in a "fuzzy" sense in order to allow the communication to be optimized.<p>* An example could be a neural net of mathematicians who together produce a better mathematician who is able to solve a problem no one single mathematician within the network is able to solve. This neural net of mathematicians could be better than a group of mathematicians discussing amongst themselves. In a group discussion, human dynamics come into play, where one of the mathematicians becomes the leader and needs to act as a centralized consolidator and arbiter of information. Whereas in the neural net the algorithm/weights determines how the mathematicians communicate with each other.<p>[1] https://news.ycombinator.com/item?id=27738029<p>[2] https://en.wikipedia.org/wiki/China_brain
Some additional points:<p>* Another example could involve a neural net of software developers trying to build an application. Where over many iterations of building several applications, the algorithm learns that certain sub tasks are best given to certain developers (i.e. neurons) and certain sub tasks need to be given to multiple developers since it might be a difficult or an error prone problem to solve.<p>* The neural network architecture needn't be limited to just MLPs, they can be recurrent, convolutional or anything else.<p>* Obviously the problem I am unable get around is how the inputs and outputs are to be defined for given problem. Given that the neurons are humans who can solve quite general problems themselves, the inputs and outputs needn't be limited to just numbers.<p>* The optimization algorithm determines to whom these messages are sent and with what "weight".<p>To state some of the obvious:<p>- A neural net of just one human is AGI<p>- A company or any organization of humans can also be considered to be AGI. However the focus of my question is whether we can capitalize on the recent developments we've made in the neural networks architectures and optimization techniques and apply it to a neural net of humans.