On Sun, May 23, 2021 at 1:57 PM James Bowery <jabow...@gmail.com> wrote: > On Sun, May 23, 2021 at 12:32 PM Matt Mahoney <mattmahone...@gmail.com> wrote: >> >> ...Data compression alone doesn't lead to AGI, but it does measure >> prediction in signals with a high signal to noise ratio, like text. It's >> less useful for vision and robotics. > > Seems to me if a vision system can transform a 2D array of pixels into a 3D > array of voxels into a CAD model that the CAD model of the 3D environment, > that this would be both a highly compressed representation of the environment > and highly useful for robotics.
Image prediction is very useful. It is central to how we understand what we see. But the problem with turning the prediction algorithm into a compressor is that the input is mostly noise, which is meaningless and does not compress. So your compression ratio doesn't tell you much. If you use lossy compression, which is more appropriate, then you have to subjectively evaluate it for quality. Either way, you don't get a precise number like with text compression, which makes searching for better algorithms much slower. -- -- Matt Mahoney, mattmahone...@gmail.com ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/T95f11a183fb9b6e1-Mad3786e425780d8b763b51fa Delivery options: https://agi.topicbox.com/groups/agi/subscription
