AI Safety Researcher @AEStudio
Currently developing a novel AI Alignment agenda that focuses on scalably inducing self-other overlap in state-of-the-art machine learning models in order to facilitate the learning of empathetic and non-deceptive representations and elicit prosocial behaviors.
Any n-bit hash function will produce collisions when the number of elements in the hash table gets large enough (after the number of possible hashes stored in n bits has been reached) so adding new elements will require rehashing to avoid collisions making GLUT have a logarithmic time complexity in the limit. Meta-learning can also have a constant time complexity for an arbitrarily large number of tasks, but not in the limit, assuming a finite neural network.