All of Guillaume Corlouer's Comments + Replies
An interesting analogy, closer to ML, would be to look at neuroscience. It's an older field than ML, and it seems that the physics perspective has been fairly productive, even though not successful at providing a grand unified theory of cognition yet. Some examples:
- Using methods from electric circuits to explain neurons (Hodgkin-Huxley model, cable theory)
- Dynamical systems to explain phenomena like synchronization in neuronal oscillations (ex: Kuramoto model)
- Ising models to model some collective behaviour of neurons
- Information theory is commonly used
1Interesting! Perhaps one way to not be fooled by such situations could be to use a non-parametric statistical test. For example, we could apply permutation testing: by resampling the data to break its correlation structure and performing PCA on each permuted dataset, we can form a null distribution of eigenvalues. Then, by comparing the eigenvalues from the original data to this null distribution, we could assess whether the observed structure is unlikely under randomness. Specifically, we’d look at how extreme each original eigenvalue is relative to those...
Right, I got confused because I thought your problem was about trying to define a measure of optimisation power - for ex analogous to the Yudkowsky measure - that was also referring to a utility function, while being invariant from scaling and translation but this is different from asking
"what fraction of the default expected utility comes from outcomes at least as good as this one?’"
What about optimisation power of as a measure of outcome that have utility greater than the utility of ?
Let be the set of outcome with utility greater than according to utility function :
The set is invariant under translation and non-zero rescaling of the utility function and we define the optimisation power of the outcome ' according to utility function as:
This does not suffer from compar...
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 pe...
Thanks for all the useful links! I'm also always happy to receive more feedback.
I agree that the sense in which I use metaethics in this post is different from what academic philosophers usually call metaethics. I have the impression that metaethics, in academic sense, and metaphilosophy are somehow related. Studying what morality itself is, how to select ethical theories and what is the process behind ethical reasoning seems not independent. For example if moral nihilism is more plausible then it seems to be less likely that there is some meaningful...
I am not sure about what computer aided analysis mean but one possibility could be to have formal ethical theories and prove some theorem inside their formal framework. But this raises questions about the sort of formal framework that one could use to 'prove theorems' under ethics in a meaningful way.
Another perspective would be too look at the activations of an autoregressive deep learning model, e.g. a transformer, during inference as a stochastic process: the collection of activation (Xt) at some layer as random variables indexed by time t, where t is token position.
One could for example look at mutual information between the history X−t=(Xt,Xt−1,...) and the future of the activations Xt+1, or look at (conditional) mutual information between the past and future of subprocesses of Xt (note: transfer entropy can be a... (read more)