n-ary state computers that represent n-ary numbers constitute a lossless compression of any unary number (sticks or marks using the 'natural' representation of numbers) within the range of the extent of the numeration (which means I can't casually figure the range out in a few minutes.) Jim Bromer
On Sat, Oct 6, 2018 at 9:24 PM Matt Mahoney via AGI <agi@agi.topicbox.com> wrote: > > I understand the desire to understand what an AGI knows. But that makes you > smarter than the AGI. I don't think you want that. > > A neural network learner compresses its training data lossily. It is lossy > because the training data information content can exceed the neural network's > memory capacity (as all learners should). Then it compresses the remainder > effectively by storing as prediction errors. Learning simply means making > whatever adjustments reduce the error. > > On Fri, Oct 5, 2018, 10:29 PM Ben Goertzel <b...@goertzel.org> wrote: >> >> Jim, >> >> If you look at how lossless compression works, e.g. lossless text >> compression, it is mostly based on predictive probability models ... >> >> If you have an opaque predictive model of a body of text, e.g. a deep >> NN, then it's hard to manipulate the internals of the model ... >> >> OTOH if you have a predictive model that is explicitly represented as >> (say) a probabilistic logic program, then it's easier to manipulate >> the internals of the model... >> >> So I think actually "operating on compressed versions of data" is >> roughly equivalent to "producing highly accurate probabilistic models >> that have transparent internal semantics" >> >> Which is important for AGI for a lot of reasons >> >> -- Ben >> On Sat, Oct 6, 2018 at 5:05 AM Jim Bromer via AGI <agi@agi.topicbox.com> >> wrote: >> > >> > A good goal for a next generation compression system is to allow >> > functional transformations to operate on some compressed data without >> > needing to decompress it first. (I forgot what this is called but >> > there is a Wikipedia entry on something s8milar in cryptography.) >> > This is how multiplication works by the way. >> > >> > If a 'dynamic compression' was preformed in stages using 'components' >> > which had certain abstract attributes that could be used in >> > computations that were done in multiple passes, then it might be >> > possible to postpone a complete analysis or computation until the data >> > was presented in a more abstract format (relative to the given >> > problem). The goal is to find a way to make each pass effective but >> > seriously less complicated. The idea is that the data 'components' >> > (the data produced by a previous pass) might have certain abstract >> > properties that were general, and subsequent passes might then operate >> > on narrower classes. (This is how many algorithms work now that I >> > think about it, but they are not described and defined using the >> > concept of compression abstractions as a fundamental principle.) >> > Jim Bromer >> >> >> -- >> Ben Goertzel, PhD >> http://goertzel.org >> >> "The dewdrop world / Is the dewdrop world / And yet, and yet …" -- >> Kobayashi Issa > > Artificial General Intelligence List / AGI / see discussions + participants + > delivery options Permalink ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/T55454c75265cabe2-M2c2d3be0d28fe18619931795 Delivery options: https://agi.topicbox.com/groups/agi/subscription
