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Brain Cells For Sale ~ The Need For Standardization of Artificial Neural Nets

When it comes to Artificial Neural Networks, the world is awash with roll-your-own. Everyone has their own brand and implementation.  Although the theory and practice is well thought out, tested and put into use, the implementation in almost every case is different. In our company, we have a partner university training artificial neural nets for our field of endeavor as a research project for graduate students.

Very few roll-your-own ANN's or Artificial Neural Networks are object-oriented in terms of the way they are programmed. This is because it is easier to have a monolithic program where each layer resides in an array, and the neurons can input and output to each other easily.  All ANNs are coded in everything from Java, to C, to C++, to C# to kiddie scripting.  I am here to preach today, that there should be a standard Artificial Neuron.  To be more explicit, the standardization should be in the recipe for layers, inputs, weights, biases and outputs.  Let me explain.

While the roll-your-own is efficient for each application, it has several major drawbacks.  Let me go through some of them.

The first one is portability. We have a multitude of platforms on everything from Windows to Linux, to Objective C in the iOS native format, to QNX to folks putting Artificial Neural Networks on silicon, and programming right down to the bare metal, or the semi-metals that dope the silicon matrix in the transistor junctions of the chips. We need to be able to run a particular set of specifically trained neural nets on a variety of platforms.

The multiplicity of platforms was seen early on and as a result, we had strange things developed like CORBA or Common Object Request Broker Architecture being formulated ( ). CORBA came about in the early 1990's in its initial incarnations however it is bulky and adds a code-heavy layer of abstraction to each platform when you want to transport silicon brainiacs like a multilayer perceptron machine. The idea of distributed computing is an enticing one, but due to a large variety of factors, including security and the continued exponential multiplication of integrated transistors on a chip according to Moore's Law, it is a concept that has been obviated for the present time.

My contention, is that if you had a standard for a Neural Net, then you wouldn't have to call some foreign memory or code object from a foreign computer. You would just use a very simple light-weight data protocol to transfer post-learning layers, weights and biases (like JSON)  and bingo -- you can replicate smartness on a new machine in minutes without access to training data, or the time spent training the artificial neural net. It would be like unpacking a thinker in a box. You could be dumber than a second coat of paint, but no one would notice, because your mobile phone did your thinking for you.

There is another aspect to this, and it is the commercial aspect.  If I came across a unique data set, and trained a bunch of neural networks to predict stuff in the realm of that data set, I potentially could have a bunch of very valuable neural nets that I could sell to you.  All that you would have is pay me the money, download my neural net recipe with its standardized notation, and be in business generating your own revenue stream. It wouldn't matter what platform, operating system or chip set that your computer or device used -- the notation for the recipe of the artificial neural network would be agnostic to the binaries.

We are in a very strange time, with the underpinnings of our society changing at a very fast pace.  My contention is that the very nature of employment may change for many people.  We will not longer need to import cheap goods from China that fill the dollar stores. You will order the recipe for a 3D printer and make whatever you need.  This paradigm alone will kill many manufacturing jobs. As a result, the nature of work will change.  People will find a niche, and supply the knowledge in that niche that can be utilized or even materialize that knowledge into what they need.   We will transcend the present paradigm of people supporting themselves by making crafts and selling them on Etsy or writing books and selling them on Amazon.  People will make and sell knowledge products, and one could sell trained neural nets for any field of endeavor.

Just as rooms full of Third World country young men game all day and sell the rewards online to impatient first world gamers, you will have people spending days and weeks training neural nets and sell them on an online marketplace.

That day is coming shortly, and the sooner that we have a standard for Artificial Neural Net recipes, the sooner that we will see intelligence embedded in devices and trained neural nets for sale. You can count on it.

These thoughts were spawned on my daily walk, and you can bet that I have already started to create a schema for a neural net transference, as well as a Java Interface for one version of a standardized neural net.  Stay tuned.

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