It was originally just a simple experiment by me to see if I could build an AI that works like an ordinary computer program, where it uses a modest amount of RAM and primarily runs on the CPU. It stems from my personal issue with neural networks in general, where it tries to mimic the neural network inside our brain in the form of numerical weights. The problem lies in how much it relies on scaling, which uses a lot of memory, and how the weights are usually frozen upon deployment. Neural networks work for biological brains because they are physically made out of neurons, which directly work off physics and chemistry. Computers, on the other hand, have to simulate both the neurons and the physics behind them to get it to work. Of course, we only simulate it in the form of mathematical computation, not the full process, but my point still stands. However, modern computers by themselves are significantly more powerful than brains in terms of computational power. So, my idea was, why not make an AI system that specifically utilises whatever the computer is already good at? But what are the things that computers are already good at? Persistent memory - There is no need to memorise as the brain does with continuous brain activity, since a computer can just write the data onto the storage drive and read it back later when needed. Multiprocessing - Any consumer computer today could do multiprocessing just fine, so one program can summon multiple child processes that are running asynchronously in the background. With these in mind, let me introduce you to The Corelian Architecture. It is a bottom-up cognitive system that uses basic sensory data as the medium for cognition, which is handled by multiple subprocesses I call ‘Minds’. Diagram of the system It works on these five core principles: All received information must be broken down to the sensory level. Sensory-level information must be the basis for all kinds of thinking. The thinking process must be a continuous process. The continuous process must be optimised for local computation. The local AI system must continuously learn from what it personally acquires. Here’s the simplified documentation: https://www.reddit.com/r/corelyverse/comments/1w8wrzy/what_is_the_corelian_architecture/ Here’s the full documentation: https://harithmarzuki.github.io/corely/architecture.html So far, I’ve tried implementing this cognitive architecture on my robot, which I named Corely. While it didn’t immediately make her speak fluently or perform complex tasks, my robot showed some level of primitive intelligence after a while, though it’s difficult for me to provide any quantitative data from it to back it up. So yeah, I didn’t expect it to become the AGI paradigm in its current form, but I wonder if it could be improved by adapting it to more advanced methods than the ones I currently have. submitted by /u/ThatGuyDayth
Originally posted by u/ThatGuyDayth on r/ArtificialInteligence
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