Thank you for the correction. AlphaStar is not completely stateless (even ignoring fog-of-war-related issues).
I think the issue here is more about a lack of ‘reasoning’ skills than time-scales: the network can’t think conceptually...
This is exactly what I mean. The problem I’m trying to elucidate is that today’s ML techniques can’t create good conceptual bridges from short time-scale data to long time-scale data (and vice-versa). In other words, that they cannot generalize concepts from one time scale to another. If we want to take ML to the next level then we’ll have to build a system that can. We may disagree about how to best phrase this but I think we’re on the same page concerning the capabilities of today’s ML systems.
As for connectome-specific harmonic waves, yes, my suggestion is to store slow-changing data in the largest eigenvectors of the Laplacian. The problem with LSTM (and similar RNN systems) is that there’s a combinatorial explosion[1] when you try to backpropagate their state cells. This is the computational cliff I mentioned in the article.
The human brain has no known mechanism for conventional backpropagation in the style of artificial neural networks. I believe no such mechanism exists. I hypothesize instead that the human brain doesn’t run into the aforementioned computational cliff because there’s no physical mechanism to hit that cliff.
So if the human brain doesn’t use backpropagation then what does it use? I think a combination of Laplacian eigenvectors and predictive modeling. If everything so far is true then this sidesteps the RNN computational cliff. I think it uses something involving resonance[2] between state networks instead, but we can reach this conclusion without knowing how the human brain works.
This is promising for a two related reasons: one involving power and the other involving trainability.
Concerning power: I think resonance could provide a conceptual bridge between shorter time-scales to longer time-scales. This solves the problem of fractal organization in the time domain and provides a computational mechanism for forming logic/concepts and then integrating them with larger/smaller parts of the internal conceptual architecture.
Concerning trainability: You don’t have to backpropagate when training the human brain (because you can’t). If CSHW and predictive modeling is how the human brain gradient ascends then this could completely sidestep the aforementioned computational cliff involved in training RNNs. Such a machine would require a hyperlinearly smaller quantity of training data to solve complex problems.
I think these two ideas work together; the human brain sidesteps the computational cliff because it uses concepts (eigenvectors) in place of raw low-level associations.
I mean that the necessary quantity of training data explodes, not that it’s hard to calculate the backpropagated connection weights for a single training datum.
Thank you for the correction. AlphaStar is not completely stateless (even ignoring fog-of-war-related issues).
This is exactly what I mean. The problem I’m trying to elucidate is that today’s ML techniques can’t create good conceptual bridges from short time-scale data to long time-scale data (and vice-versa). In other words, that they cannot generalize concepts from one time scale to another. If we want to take ML to the next level then we’ll have to build a system that can. We may disagree about how to best phrase this but I think we’re on the same page concerning the capabilities of today’s ML systems.
As for connectome-specific harmonic waves, yes, my suggestion is to store slow-changing data in the largest eigenvectors of the Laplacian. The problem with LSTM (and similar RNN systems) is that there’s a combinatorial explosion[1] when you try to backpropagate their state cells. This is the computational cliff I mentioned in the article.
The human brain has no known mechanism for conventional backpropagation in the style of artificial neural networks. I believe no such mechanism exists. I hypothesize instead that the human brain doesn’t run into the aforementioned computational cliff because there’s no physical mechanism to hit that cliff.
So if the human brain doesn’t use backpropagation then what does it use? I think a combination of Laplacian eigenvectors and predictive modeling. If everything so far is true then this sidesteps the RNN computational cliff. I think it uses something involving resonance[2] between state networks instead, but we can reach this conclusion without knowing how the human brain works.
This is promising for a two related reasons: one involving power and the other involving trainability.
Concerning power: I think resonance could provide a conceptual bridge between shorter time-scales to longer time-scales. This solves the problem of fractal organization in the time domain and provides a computational mechanism for forming logic/concepts and then integrating them with larger/smaller parts of the internal conceptual architecture.
Concerning trainability: You don’t have to backpropagate when training the human brain (because you can’t). If CSHW and predictive modeling is how the human brain gradient ascends then this could completely sidestep the aforementioned computational cliff involved in training RNNs. Such a machine would require a hyperlinearly smaller quantity of training data to solve complex problems.
I think these two ideas work together; the human brain sidesteps the computational cliff because it uses concepts (eigenvectors) in place of raw low-level associations.
I mean that the necessary quantity of training data explodes, not that it’s hard to calculate the backpropagated connection weights for a single training datum.
Two state networks in resonance automatically exchange information and vice-versa.