Quoted in a Thesis

I feel a bit like I've arrived when other researchers quote me in their work !

Bitter Electromagnet As Foundation Principle for Synthetic Muscles

The nylon twist coil heat activated synthetic muscle at first seemed promising. But ultimatly we could not get a reliable solution. They tended to break and fatigue, adding heat without melting the nylon was difficult. Braided carbon nanotubles was another solution but decades away in mass production as well as being too small.

So our synthetic muscle development is working on a principle which is used in Bitter electromagnets,which is a distributed helical coil field. This provides much of what we wish, a strong stable and efficient response to low voltage pulses.


The key aspect is how to manage contraction and retraction such that these can be durable for millions of pulses. But the unique Bitter design which is used in the worlds most powerful magnets, works just as well on the micro scale. As above so below ....

Using Tertiary Structures in HBM2 Arrays To Accelerate Neuronal Access

HBM2 architecture still suffers from bus latencies across the interposer substrate. Wider memory buses and stacked memory architecture still does not ameliorate the bus differential distance between the GPU processing core (stream processor) and HBM retrievals. This works find for mass bulk data screen loads supporting graphics but is a terrible architecture for neural processing.

Instead an interposed fine grained tertiary structure between HBM and each stream processor can improve performance by a factor of several thousand times for retrieval and update operations.

Nvidia's GP100 uses the same slow approach relying on high wattage bus speeds



The interposer tech is designed for one processor to access large scales of memory whereas the neuronal unit requires localized memory with hyperspeed interconnects which transcend each planar layer. A few random spiking units defer to interposing bus transactions alternating bit transfers and based on non-linear excitation strategies.

Here is a brief overview of correlet architecture for synaptic processing without large bus latency



This produces an inner group latency which is nearly instantaneous while larger outer group branches simply degrade exponentially according to distance. The main complexity with neural cube architecture is transposing non-linear synthetic distances into core relationships as its nearly impossible to dis-assimilate plane relationships which are inherently larger correlet hops than planar distance would indicate.



To ameliorate this situation, a plane dragging event is created to provide sequential ordered plane processing where required. Luckily in true convolution the cyclical stimulus is often non-ordered.

current implementations fail at this accelerated locality. Larger scale designs which operate on stimulation impedus rather than constant high rate bus cycling is the answer, something IBM has achieved with very small scale architecture (256 neural units) while true neural cubes begin to take form at 64 billion neuronal units. To get there synthetic networks transposing to high powered local groups is the optimal architecture. Eventual non-synthetic processing will be ideal, most likely when organic circuits develop.

Kurzweil the "father of AI" You've Got to be Kidding me!

A recent article

https://www.dailystar.co.uk/news/latest-news/682554/robots-artificial-intelligence-singularity-google-engineer-Jurgen-Schmidhuber-Ray-Kurzweil

calls out Ray Kurzweil as the father of AI. What HOGWASH.

First, I own a K2000RS and had a K1200. I know mr. Ray quite well. He is if anything the father of the speaking reading machine for the blind and sample instrument synthesis. NOT AI.

Let's talk the real history.

The Dartmouth Conference of 1956 was organized by Marvin Minsky, John McCarthy and two senior scientists: Claude Shannon and Nathan Rochester of IBM. ... At the conference Newell and Simon debuted the "Logic Theorist" and McCarthy persuaded the attendees to accept "Artificial Intelligence" as the name of the field

Marvin Minsky

Minsky is often the one I think of first when someone asks about founders of AI, and Turing pops to mind second simply because he did so much. 

Don't forget Kunihiko Fukishima's Cognitron who was so far ahead of his time its frightening, and a giant whose shoulders I stand on. 

Dr. Fukushima

 or Edelman's Neural Darwinism. Giants both. They forged a lot of principles of Noonean Inc. 

Dr. Gerald Edelman M.D.

What about Karl Pribram? Holographic Brain theory? 

Karl Pribram

What about the first commercial AI - Expert Systems. Nope Ray wasn't there either

developed by Edward Feigenbaum and his students. Dendral, begun in 1965, identified compounds from spectrometer readings. MYCIN, developed in 1972, diagnosed infectious blood diseases. They demonstrated the feasibility of the approach

Dr. Edward A. Feigenbaum

What about early neural networks? John Hopfield gave us those. His background was in molecular biology. Ray was mysteriously absent. 

John Hopfield

What about the founder of semantic nets - Ross Quillian? And what about companies? IBM and Bolt Beranack and Newman (sp?) which was whispered quietly as I was a senior at Vassar as how brilliant they were and how hard their interviews were. I ended up starting with IBM Research. But I wished I were smart enough for BBN. 

Ross Quillian

We ended up using Ross's work for enhancing search engines in 1996 and doing things miles ahead of the dummies at Google, but success isn't always about being the smartest. 

It seems the one big thing Kurzie did was write a rather poor book full of 30 year old research on AI, and then hire out of date hacks like Geoffry Hinton at Google. Lawd help us all.  Alright, Hinton is ok as a general theorist but he is hardly a pioneer and his Ted talk showed what I was studying in 1988!!!

Daniel Dennet led much of the work in symbolic representation in Cognitive science

Daniel Dennett

Dennett is our great american cognitive philosopher and still alive and going strong. May you live another 100 years Daniel we are in debt!!!

David Rumelhart was a brilliant psychologist and thinker into the development of mind. Sadly he is no longer with us. 

David Rumelhart

Roger Schrank a UT Austin alumn is still going strong as the CEO of Socratic Arts, a learning company. 

Roger Schrank

We also have Terry Winograd from Stanford's HCI group. He pioneered much of the work in natural language processing

Dr. Terry Winograd

Obviously an incomplete list. But I encourage to read and research these early pioneers and true fathers of AI. And hopefully as I am getting old and gray I will make the grade as well someday. These true fathers and founders, and 

As a aside, I am noted as the originator of the concept of the Neural Cube, a massive 3 dimensional self organizing structure of billions of neurons, as well as being a principal engineer in the early days of genetic programming. But I get few articles and I can't write my book without giving away too many secrets so.. that is on hold for now. But I will guarantee it will be light years past dim brain Kurzweils feeble mind. Is that harsh? Ray I love your chip wiring and early electronics, but a AI pioneer you were not. 

Book in Denmark...

I always marvel at how far and wide my books are sold... I really need to work with a translation house to get them available in native languages...

The Perils of Dependency Injection

When we worked with the Java Spring Framework (4.3 at the time) one of the things we had to adjust to was the fact that rather than have a J2E app server spring provided access to our realized objects through their dependency injection framework.

Now this is a lot like Windows versus linux. One time windows insisted on installing a security patch even though I had done everything I could possibly do to turn that off. So my computer rebooted twice, took several hours at load time with a threat "dont turn off!" and finally died altogether.

The idea of the computer taking control from you and believing "I know best" is really a new thing. One of the few pleasures we used to have was that computers unlike wives only did what we told them to do.

Now Spring is a lot like that. One of the first things that was confounding was what classes which used other classes which used other classes could actually be injected. This often wasn't as obvious as possible or predictable. Several times simple atomic classes which used other injects were not allowed. Why? This made no sense. It had to do with the internal class representation they had hacked into their injection representational model classes. It didn't have to make sense.

Another big issue which I think I mentioned before is how in the case of errors, again Spring would intercept the exception and re-wrote it to the callers callers callers class. The first time I tried to fix it with simple encapsulation. No dice it was thrown above that. Then we put in another set of classes specifically to get the throw. Nope still no dice. Finally we had a fully artifical layer calling a fascade calling into the classes. This made the code very hard to read and maintain. Why were we doing this? One developer suggested removing all this unnecessary code. Another did something similar the day of the big demo.

Now why WHY is this dependency injection stuff so high on the hog? I can always predict the classes I need from a constructor factory and have the same abilities. I've never had issues with getting hit with a new class type it just never comes up. Well a lot of it has to do with the MOCKIT or MOKITO type of testing. Testing can be difficult with the requirement to be in a fully live system. By using Mock object injection you can spoof your system to make it think its live and still test the function with the right state, say for example an object that has a handle which must be set by the system.

Again, yes useful, but real system tests always prove better. And rarely have I seen mockit object testing done well or ubiquitously it simply takes too long to setup.

Dependency injection seems like something they realized they could do, without actually looking at the pattern of use. does it have its place on some projects? Perhaps. But I advise caution.

Java Object Oriented Neural Network - Core Classes

Some early work on JOONN

InputMatrix - handles connecting data into the neural cube. typically at a front layer. Runs with its own scheduled thread

OutputMatrix - handles outputs with different paradigms - FileLogger, Alerter, CubeBridge

Now let's build up a Cube from small to large

Neuron
     This is the base type and we will have several advanced types
            uses:
                Synapse
                       hasa Weight
                       hasa Value
                       hasa ThresholdFunction, DecayFunction
                       hasa Algorithm
                       hasa SpikingFunction
                       hasa  InterconnectGrowth function (for creating new synaptic connections)
                       hasa  Chaos function  (to add gradual chaos into the system)
                       hasa  PerformanceStats (how fast is it processing)

                 NeuralCore
                      hasa  Value
                      hasa   64 value MemoryMap
                      hasa   PassingMemoryMap   for propagations
                      hasa    MemoryMapTransferClass  for constructing the outputMap
                      hasa    SpikeInput, SpikeOutput
                      hasa    ConnectionArchitecture (for what other neurons it links to)
                      hasa    ThresholdFunction, DecayFunction
                      hasa     PropagationFunction

Spike
      This is used to coordinate between layers of analysis
                    hasa   ReceiveMatrix
                    hasa    DistributeMatrix
                    hasa    SpikeThreshold
                    hasa    value
                    hasa    SpikingFunctino

Layer
       One 2d layer of the cube
                   hasa   height, width
                   hasa   InterconnectModel  (soyou dont have to hand wire up thousands of neurons!)
                   hasa   zIndex
                   hasa   neuralMatrix (x,y)
                   hasa   NeuralType  -- initially one class of neuron per layer is themaximumdiversity
                 

NeuralCube
                 hasa height, width
                 hasa  LayerList
                 hasa  SpikeArray
                 hasa  InputMatrix, OutputMatrix

HolographicValueMap
             -- This is like a complex data store that is used for advanced recognition or memory.  It unifies partial maps stored in neurons and other HVMs