Reading this I can't help but think about Homomorphic encryption, which provides the ability to perform computation on encrypted data. If you were to use a compression algorithm as your encryption method then you would be done. Unfortunately homomorphic encryption necessitates bitwise encryption and compression algorithms would most certainly take into account more than a single bit when they perform their compression. And so there would be quite a bit of work to do to generalize this field of mathematics to work on that class of encryption methods, but it seems to be where I would focus my efforts if I wished to provide this capability.
But you seem to have dismissed homomorphic encryption for some reason when you said the following. "I did not want to use binary arithmetic as an example because computers were designed around those principles." Homomorphic encryption most certainly uses binary arithmetic. Can you elaborate on why you wish to preclude this? Memories in the brain are reconstructive, and confabulatory. Which is to say when you ask someone to recall something, they will not recall information as it was, but rather as their brain is. And one can alter the brain of others to effectively perform CRUD operations on their memories, allowing you to alter those memories to any extent you wish. Such seems to be a rather big problem for memory systems which are brain inspired. Computers have perfect recall and such is highly desirable and a great improvement over humans. I'm not certain why you would wish to replace such a perfect system with a lossy (and confabulatory) system. On Sat, Jun 10, 2017 at 4:02 AM, Jim Bromer <[email protected]> wrote: > Rob, > I will look at the paper when I get a chance. > > Jim Bromer > > On Wed, Jun 7, 2017 at 7:16 PM, Rob Freeman <[email protected]> > wrote: > >> Jim, >> >> Have a look at this paper and see if you find it relevant. I understand >> it to be a sketch for logic using distributed representation. RNN's still >> globally optimize, so I think they will still have lossy compression >> (instead of partial compression?) But the idea of using distributed >> representation is on the right track: >> >> Semantic Compositionality through Recursive Matrix-Vector Spaces >> Richard Socher Brody Huval Christopher D. Manning Andrew Y. Ng >> https://nlp.stanford.edu/pubs/SocherHuvalManningNg_EMNLP2012.pdf >> >> -Rob >> *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> >> <https://www.listbox.com/member/archive/rss/303/24379807-653794b5> | >> Modify <https://www.listbox.com/member/?&;> Your Subscription >> <http://www.listbox.com> >> > > *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> > <https://www.listbox.com/member/archive/rss/303/26973278-698fd9ee> | > Modify > <https://www.listbox.com/member/?&;> > Your Subscription <http://www.listbox.com> > ------------------------------------------- AGI Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/21088071-f452e424 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-58d57657 Powered by Listbox: http://www.listbox.com
