Free Form Thoughts

A Classic Movie Voice Over

And so did the cutter of stone from the sky release the priest of his knowledge of lack of contact such that a stone cold comparison could be seen, and such that it meant that he still would still not know a hug.

And it became decided that the balance between overtaking the lessers versus timed up greaters as an order for the taking sensing a taked in the mistook, all because analytic in speed of absorption, such that little to as much was done.

How to tell the apprentice from beyond thu execution and what of the touchy humours?

And as the unity lowered with the cut words “different cutter” as they appeared. From this a division of opinion ended in more than a happen-seat. And so it was and might is a mighty word.

The multi-cutural (noel) was seen perhaps ower to the hives of man and fortuatous gods or sub-gods. Then what could be done? Why would they prey upon an idol god for it was upon the nature of being that action did perform some or a difference upon tribes and detribulates. If the payment is freedom then what is it to be holden to a duty?

Bode, bode and thrice bode that minus one is a bitch. Obvious dick in womb joke and all. All bar one off course. Yes, an extra-oneous F. Rise again dear cheapo.

And as he placed ring finger of his fishy right hand upon the pre-chopped and processed tree stump, declaring “take it and fuck off”, all was a bit more cagey and costing of those that never get told of the prices of alternate labour avoidance for profit.

Nice story so far dear observer. I think you’d like a little titillation for your money now. Bring forth babe percents and vital statistics.

What a placement of mind in such a being of knowledge. What could become? What it for removals of of thing never cast, never worried, never done.

In the be ginning. A shrrod ploy to an ends. As all became seated and thrust needed no explanation.

All the Too Messy for Sci-Fi Complaints

Assuming GPT-3 is really good at story completion how can anyone say that errors in word sequencing are irrelevant for the provocation phrase issued to an AI when the purpose is completion from the source through sense and not the generation of a more precise bore?

Although the mathematics of a form of complexity may be essential, the actual origin of the mathematics might not be as essential as a way of introducing the definitive emergents as one would assume. Multiple originations of emergence isomorphism in the completeness of behaviour might and likely are possible.

The latest AI joke is about the Silly can’ts versus the car bonned. Oh, dear. 

Gradients and Descents

Consider a backpropagation which has just applied to a network under learning. It is obvious that various weights changed by various amounts. If a weight changes little it can be considered good. If a weight changes a lot it can be considered an essential definer weight. Consider the maximal definer weight (the one with the greatest change) and change it a further per cent in its defined direction. Feedforward the network and backpropagate again. Many of the good weights will go back to closer to where they were before definer pass and can be considered excellent. Others will deviate further and be considered ok.

The signed tally of definer(3)/excellent(0)/good(1)/ok(2) can be placed as a variable of programming in each neuron. The per cent weight to apply to a definer, or more explicitly the definer history deviation product as a weight to per cent for the definer’s direction makes a training map which is not necessary for using the net after training is finished. It does however even further processing such as “excellent definer” detection. What does it mean? 

In a continual learning system, it indicates a new rationale requirement for the problem as it has developed an unexpected change to an excellent performing neuron. The tally itself could also be considered an auxiliary output of any neuron, but what would be a suitable backpropagation for it? Why would it even need one? Is it not just another round of input to the network (perhaps not applied to the first layer, but then inputs don’t always have to be so).

Defining the concept of definer epilepsy where the definer oscillates due to weight gradient magnification implies the need for the tally to be a signed quantity and also implies that weight normalization to zero should also be present. This requires but has not been proven as the only sufficient condition that per cent growth from zero should be weighted slightly less than per cent reduction toward zero. This can be factored into an asymmetry stability meta.

A net of this form can have memory. The oscillation of definer neurons can represent state information. They can also define the modality of the net knowledge in application readiness while keeping the excellent all-purpose neurons stable. The next step is physical and affine coder estimators.

Limit Sums

The convergence sequence on a weighting can be considered isomorphic to a limit sum series acceleration. The net can be “thrown” into an estimate of an infinity of cycles programming on the examples. Effectiveness can be evaluated, and data estimated on the “window” over the sum as an inner product on weightings with bounds control mechanisms yet TBC. PID control systems indicate in the first estimate that differentials and integrals to reduce error and increase convergence speed are appropriate factors to measure.

Dynamics on the per cent definers so to speak. And it came to pass the adaptivity increased and performance metrics were good but then irrelevant as newer, better, more relevant ones took hold from the duties of the net. Gundup and Ciders incorporated had a little hindsight problem to solve.

Fractal Affine Representation

Going back to 1991 and Micheal Barnsley developing a fractal image compression system (Iterrated Systems FIF file format). The process was considered computationally intensive in time for very good compression. Experiments with the FIASCO compression system which is an open-source derivative indicate best performance lies in low quality (about 50%) is very fast, but not exact. If the compressed image is subtracted from the input image and further compressed as a residual a number of times, performance is improved dramatically.

Dissociating secondaries and tertiaries from the primary affine set allows disjunct affine sets to be constructed for equivalent compression performance where even a zip compression can remove further information redundancy. The affine sets can be used as input to a network, and in some sense, the net can develop some sort of affine invariance in the processed fractals. The data reduction of the affine compression is also likely to lead to better utilization of the net over a convolution CNN.

The Four Colour Disjunction Theorem.

Consider an extended ensemble. The first layer could be considered a fully connected layer distributor. The last layer could be considered to unify the output by being fully connected. Intermediate layers can be either fully connected or colour limited connected, where only neurons of a colour connect to neurons of the same colour in the next layer. This provides disjunction of weights between layers and removes a completion upon the gradient between colours.

Four is really just a way of seeing the colour partition and does not really have to be four. Is an ensemble of 2 nets of half size better for the same time and space complexity of computation with a resulting lower accuracy of one colour channel, but in total higher in discriminatory performance by the disjuction of the feature detection?

The leaking of cross information can also be reduced if it is considered that feature sets are disjunct. Each feature under low to non detection would not bleed into features under medium to high activation. Is the concept of grouped quench useful?

Query Key Transformer Reduction

From a switching idea in telecommunications, an N*N array can be reduced to a mostly functional due to sparsity N*L array pair and an L*L array. Any cross-product essentially becomes  (from its routing of an in into an out) a set of 3 sequential routings with the first and last being the compression and expansion multiplex to the smaller switch. Cross talk grows to some extent, but this “bleed” of attention is a small consideration given the fact that the variance spread of having 3 routing weights to product up to the one effective weight and computation is less due to L being a smaller number than N.

The Giant Neuron Hypothesis

Considering the output stage of a neuronal model is a level sliced integrator of sorts, the construction of RNN cells would seem obvious. The hypothesis asks if it is logical to consider the layers previous to an “integration” layer effectively an input stage where the whole network is a gigantic neuron and integration is performed on various nonlinear functions. Each integration channel can be considered independent but could also have post layers for further joining integral terms. The integration time can be considered another input set for per integrator functional.  To maintain tensor shape as two inputs per integrator are supplied the first differential would be good also especially where feedback can be applied.

This leads to the idea of the silicon conectome. Then as now as it became, integration was the nonlinear of choice in time (a softmax divided by the variable as goes with [e^x-1]/x. A groovemax if you will). The extra net uninueron integration layer offering the extra time feature of future estimation at an endpoint integral of network evolved choice. The complexity of backpropagation of the limit sum through fixed constants and differentiable functions for a zero adjustable layer insert with scaled estimation of earlier weight adjustment on previous samples in the time series under integration for an ideal propergatable. Wow, that table’s gay as.

This network idea is not necessarily recursive, and may just be an applied network with a global time delta since last evaluation for continuation of the processing of time series information. The actual recursive use of networks with GRU and LSTM cells might benefit from this kind of global integration processing, but can GRU and LSTM be improved? Bistable cells say yes, for a kind of registered sequential logic on the combinationals. Consider that a Moore state machine layout might be more reductionist to efficiency, a kind of register layer pair for production and consumption to bracket the net is under consideration.

The producer layer is easily pushed to be differentiable by being a weighted sum junction between the input and the feedback from the consumer layer. The consumer layer is more complex when differentiability is considered. The consumer register really could be replaced by a zeroth differential prediction of the future sample given past samples. This has an interesting property of pseudo presentation of the output of a network as a consumptive of the input. This allows use of the output in the backpropergation as input to modify weights on learning the feedback. The consumer must be passthrough, in its input to output while storage of samples for predictive differential generation is allowed.

So it’s really some kind of propergational Mealy state machine. A MNN if you’d kindly see. State of the art art of the state. Regenerative registration is a thing of the futured.

AI and HashMap Turing Machines

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.

Computative Psychoanalysis

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.

Ideas in AI

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.

Book Published in Electronic Format. Advanced Content not Beginner Level. Second Edition may Need a Glossary.

The book is now live at £3 on Amazon in Kindle format.

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.

AI as a Service

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.

Simon Jackson, Director.

Pat. Pending: GB1905300.8, GB1905339.6

AI and the Future of Unity

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.