Nice recommendations! In addition to brain enthusiasts being useful for empirical work, there also are theoretical tools from system neuroscience that could be useful for AI safety. One area in particular would be for interpretability: if we want to model a network at various levels of “emergence”, recent development in information decomposition and multivariate information theory to move beyond pairwise interaction in a neural network might be very useful. Also see recent publications to model synergestic information and dynamical independance to perhaps automate macro variables discovery which could also be well worth exploring to study higher levels of large ML models. This would actually require both empirical and theoretical work as once the various measures of information decomposition are clearer one would need to empirically estimate test them and use them in actual ML systems for interpretability if they turn out to be meaningful.
Great points, thanks for the comment! :) I agree that there are potentially some very low-hanging fruits. I could even imagine that some of these methods work better in artificial networks than in biological networks (less noise, more controlled environment).
But I believe one of the major bottlenecks might be that the weights and activations of an artificial neural network are just so difficult to access? Putting the weights and activations of a large model like GPT-3 under the microscope requires impressive hardware (running forward passes, storing the activations, transforming everything into a useful form, …) and then there are so many parameters to look at.
Giving researchers structured accessto the model via a research API could solve a lot of those difficulties and appears like something that totally should exist (although there is of course the danger of accelerating progress on the capabilities side also).
Nice recommendations! In addition to brain enthusiasts being useful for empirical work, there also are theoretical tools from system neuroscience that could be useful for AI safety. One area in particular would be for interpretability: if we want to model a network at various levels of “emergence”, recent development in information decomposition and multivariate information theory to move beyond pairwise interaction in a neural network might be very useful. Also see recent publications to model synergestic information and dynamical independance to perhaps automate macro variables discovery which could also be well worth exploring to study higher levels of large ML models. This would actually require both empirical and theoretical work as once the various measures of information decomposition are clearer one would need to empirically estimate test them and use them in actual ML systems for interpretability if they turn out to be meaningful.
Great points, thanks for the comment! :) I agree that there are potentially some very low-hanging fruits. I could even imagine that some of these methods work better in artificial networks than in biological networks (less noise, more controlled environment).
But I believe one of the major bottlenecks might be that the weights and activations of an artificial neural network are just so difficult to access? Putting the weights and activations of a large model like GPT-3 under the microscope requires impressive hardware (running forward passes, storing the activations, transforming everything into a useful form, …) and then there are so many parameters to look at.
Giving researchers structured access to the model via a research API could solve a lot of those difficulties and appears like something that totally should exist (although there is of course the danger of accelerating progress on the capabilities side also).