"Symbolic learning" is simply "machine learning" (automatically learn function x -> y given examples of (x, y) pairs), but where the substrate is symbolic, i.e. the functions you learn are code-like, not curve-like. The term is in opposition to "parametric learning" or "curve-fitting". Symbolic learning does not mean that the system avoids numbers or probabilities. It means its learned representation are discrete, explicit, parsimonious, code-like.
most surveys find that the median AI researcher forecasts about a 5% chance of extinction or similar "doom" from AI, while the mean probability is much higher -- around 15-20% -- due to a smallish group who give very high probabilities of doom.
I put the probability of complete extinction as being so low it isn't worth discussing, but the probabilities of AI caused disasters - e.g. cyber attacks on critical infrastructure or bio-risks - as being worth debating.
there's as populations become more apart, there begins to be biological incompatibilities rapidly developing, probably as the square of the distance separation, because you need pairs of interacting genes, and therefore it's the square of the separation.
My bet is on the more effective two pizza teams. Um and it it's also some interesting, you know, feedback we're beginning to get in terms of pair programming. I mean, with pair programming, do you say pair programming is the human and the genie? Or is it two humans and and genies? Because if it's two of us, we can control the genies perhaps a little bit better.
In comparison to text or images, it is more difficult to collect large amounts of high-quality, high-dimensional video data, let along text-video pairs.
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About the English under a post
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