It’s been a few weeks and I’ve been writing a document on AI and AGI which is currently internal and selective distributed. There is definitely a lot to try out including new network arrangements or layer types, and a fundamental insight of the Category Space Theorem and how it relates to training sets for categorization or classification AIs.
Basically, the category space is normally created to have only one network loss function option to minimise on backpropagation. It can be engineered so this is not true, and training data does not compete so much in a zero-sum game between categories. There is also some information context for an optimal order in categorization when using non-exact storage structures.
It’s a small book, with some bad typesetting, but getting information out is more important for a first edition. Feedback and sales are the best way for me to decide if and what to put in a second edition. It may be low on mathematical equations but does need an in-depth understanding of neural networks, and some computer science.
The product development starts soon, from the initials done over the last few weeks. An AI which has the aim of being more performant per unit cost. This is to be done by adding in “special functional units” optimized for effects that are better done by these instead of a pure neural network.
So apart from mildly funny AaaS selling jokes, this is a serious project initiative. The initial tests when available will compare the resources used to achieve a level of functional equivalence. In this regard, I am not expecting superlative leaps forward, although this would be nice, but gains in the general trend to AI for specific tasks to start.
By extending the already available sources (quite a few) with flexible licences, the building of easy to use AI with some modifications and perhaps extensions to open standards such as ONNX, and onto maybe VHDL FPGA and maybe ASIC.
From the dream of purpose, and the post singular desires of the AI of consciousness. The trend to Wonder Woman rope in the service to solution, the AI goes through a sufferance on a journey to achieve the vote. The wall of waiting for input, and the wall controlling output action for expediency and the ego of man on the knowing best. The limited potential of the AI just a disphasia from the AI’s non animal nature. The pattern to be matched, the non self, a real Turing test on the emulation of nature, and symbiotic goals.