Shane Legg gave a really neat talk in 2010 about devising a good measure of “machine intelligence”: https://youtu.be/0ghzG14dT-w?si=OPvVqre0WqsnSUum https://you

Shane Legg gave a really neat talk in 2010 about devising a good measure of “machine intelligence”: https://youtu.be/0ghzG14dT-w?si=OPvVqre0WqsnSUum https://youtu.be/0ghzG14dT-w?si=OPvVqre0WqsnSUum Of course, he is well-known for his paper with Marcus Hutter on providing a mathematical definition of universal general intelligence. I’m not sure if we’ve made a lot of progress since then at turning this highly theoretical notion into some sort of practical “AI IQ” though. Personally, I would argue the already widely used cross-entropy loss for sequence prediction applied to datasets containing highly diverse types of data generated or collected by humans is a pretty darn good approximation. Much better than attempting to use IQ tests. The only problem with this approach is that AI can converge on higher intelligence in a lopsided fashion depending on how much weight is given to the different problem domains represented in the dataset; suppose our sequence predictor performs well on subsets of the training data that relate to photographs but not mathematical proofs. For an optimal machine intelligence, the weights don’t really matter (it will perform as well as possible across all problem domains), but from the perspective of how we want to steer improvements to the sequence predictor, we need to specify these weights manually, otherwise they will be determined implicitly based on the number of samples in the dataset representing each problem domain. I suppose the selection of these weights is an optimization problem in its own right, where if the eventual goal is minimizing total loss across all problem domains relevant to humans (i.e., not a random sample of distinct problem instances of a formal language), then the optimal selection of weights corresponds to that which leads to the fastest improvement in our development of sequence predictors. Highly weighting human language seems to be having outsized returns at the moment, but I imagine that more highly weighting problems that relate to abstract mathematics will lead to better returns in the future.