Considering a remarkable abstract datatype or two is possible, and perhaps closely models the human sequential thought process I wonder today what applications this will have when a suitable execution model ISA and microarchitecture have been defined. The properties of controllable locality of storage and motion, along with read and write along with branch on stimulus and other yet to be discovered machine operations make for a container for a kind of universal Turing machine.
Today is a good day for robot conciousness, although I wonder just how applicable the implementation model is for biological life all the universe over. Here’s a free paper on a condensed few months of abstract thought.
It’s not just about IT, but thrashing through what the mind does, can be made to do, did, it all leverages information and modeling simulation growth for matched or greater ability.
Yes, it could all be made in neural nets, but given the tools available why would you choose to stick with the complexity and lack of density of such a soulution? A reasoning accelerator would be cool for my PC. How is this going to come about without much worktop workshop? If it were just the oil market I could affect, and how did it come to pass that I was introduced to the fall of oil, and for what other consequential thought sets and hence productions I could change.
One might call it wonder and design dress in “accidental” wreckless endangerment. For what should be a simple obvious benefit to the world becomes embroiled in competition to the drive for profit for the control of the “others” making of a non happening which upsets vested interests.
Who’d have thought it from this little cul-de-sac of a planetary system. Not exactly galactic mainline. And the winner is not halting for a live mind.
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.