Intellectual Property From 2008 to 2017

I started thinking about General Picture Recognition in the early 1990's and had always considered whether it was possible to create software that up until late 2000 was always concerned with recognition of a particular thing using mathematical points mostly.  For example using many different points on a face to try and identify that face.  But while all this was starting to take off I was already thinking about how every single object could be identified within a picture and the context and relationship of each object to that picture or another way of putting it how it was positioned within any one scene.

About 2008 I put my first attempt of General Picture Recognition onto the Internet with software I called General Picture Recognition Software General.   Since that time everybody including companies like Google have started to believe that what would have once been considered impossible that a computer could recognize any object within any picture may not be as impossible as first thought.

We have developed some interesting concepts in "General Picture Recognition" and they are listed below. Because we have noticed the proliferation of American Patents in Software and in particular picture recognition Patents that are not the same in the UK. We therefore have listed our intellectual property rights below.  

1. Auto Tagging using our unique concept of something similar to Neural Networking concepts but very different.  This concept does the following it compares two very similar whole pictures and allows each picture to be manually or automatically tagged by the user.  Once we have manually tagged a number of pictures we then allow our picture recognition software called GPRSG Software to take known information from a picture that is already tagged and then use that picture to identify similar pictures and tag that image.  This process is iterative the more images tagged the more images can be tagged given a selection of already tagged images.  This concept belongs to us and should not be copies or used in any other software product without our consent.

2 Colour interpretation uses our unique concept to remove all background within a photo and allows the selection of only the main object through colour selection taking any colour range and incorporating the whole colour range into a single colour.  This concept allows us to pull out an object from any picture and without the need to know every rotational shape using colour alone we can identify an object within reason of any size within any picture. We call this our "User Defined Custom Colour Object to Search Analysis" because by the user selecting colours within an object it will allow them to find the same object in any other picture even if there is a change of size and shape because of any sort of rotation.

3. Object storage uses our unique concept that allows object to be stored in their own file and tagged so that any picture can use any object within any file to build up a description of that picture so that the picture can be described in its own vocabulary or language using a simple word or phrase search with tagged to file; with in-depth descriptions and links.  Therefore a file may hold an object called helicopter and that object file is tagged internally with the description and linking mechanism and file content that allows any object in any other image to be compared with the object in the file containing the object. We use the same colours to identify the same object in other pictures even if the colour changes because of the way light hits that object when it is in different positions because of any rotation of that object vertical or horizontal or both. 

4)  Using a method that outline a change of strong colour or when one colour is changed to another colour.  We have a unique method for achieving the outline we produce.

5)  Multiple methods to identify what one picture contains.  The main concept is to identify the difference automatically between an object and what is actually background.  We identify background by working out what is an object.  Objects always being something that is between the background and each background is a layer of the object.  Using this method we can work out what is actually the object and what is the background using software alone. 

New Ideas on Neural Networks

The traditional approach to Neural Networks is creating Nodes that represent Neurons.  Each Neuron can be changed mathematically until the concept works.  My new theory is only different in the following way what if the computer "CREATES A NETWORK OF NEURONS USING DATA IMPORTANTLY NO NEURONS EXIST UNTIL THEIR CREATED USING DATA)  this is something that I am currently working on. I started this work in 2008 but came back to the idea in 20017.  If it is possible to create a computer programmed Brain using data then maybe we will be moving further towards general AI or general picture recognition.  What is important in this new concept is not to have Nodes or Neurons created by the programmer that can be acted on by working out the lowest error rate to achieve an action like the traditional method.   What if the data takes over this operation and only the data is changed when an error in the data is found.   Once found the data is updated and we then have a computer that now acts like a human Brain.  This would also eliminate one big issue with current thinking on AI and that is that all of these current Neural Networks need a lot of training and when they are wrong nodes or neuron weightings need to be changed or more neurons need to be added to the system.  This is certainly not like the human brain where new connections to Neurons and new Neurons seem to be created through life.   In my theory of general AI the program that acts on the data is equivalent to the brain structure and operates are differently depending on the structure, for example speech, hearing or seeing.  In my new theory the current thinking on Nodes or Neurons are replaced with data that importantly represent the original data. The best AI systems will be the system that are designed with a programmed structure that best implements the data as neurons.  The most important aspect of this type of data driven neural network system is that the design is general.  The big advantage of this is that the brain structure can be programmed and improved over time so it continues to get better.  But the brains patterns are not created through code but is pure data. The way the data is stored, analyzed and retrieved can use traditional programming languages but the data that makes up the neurons mean that we have true Artificial Intelligence.  Decisions made by the system will depend on how the data driven neural network nodes are organized.  This organization will be critical to the decisions the AI brain will make.   What I have not made clear is that this data can change over time to put right wrong decisions as the brain learns and new neuron or neuron connections are made.  Being able to change a data driven neural network will be critical for such systems to work well.  Therefore as the AI brain capacity increases the AI system should learn more and be able to rectify mistakes in data made when it did not have as much knowledge.  This I would think is a mirror to how a human learns.   For a simple example if I learn to spell "speach" like this although it is spelt wrong because everybody spells it like this "speech" until I realize that I have spelt it wrong I will always spell it that way. However once I use a spellchecker I realize that I have spelt it wrong my brain then swaps out speach and creates speech.  To do this it usually moves speach to a lower priority and supersedes with speech being higher priority and therefore "correct at the moment."  This is the key to learning that of "currently being right" this may change over time when new data is learnt.  This is important because the human brain like a good AI system must have a brain that can change opinion over time. This is the area of my current research that I am interested in. I believe that such data driven neural network systems may take computers far beyond current human intelligence in that they will be very much better than humans in very many areas.  These new AI system will even be able to produce or create theories like Albert Einstein's thought about but done using data driven neural networks.  What I think will be interesting is will humans be able to understand why such theories are being defined and how those computer driven theories can be implemented.  Will the human be intelligent enough to interpret such computer defined theories, I think I will leave it as an open question to be discussed by others.

All of these concepts are our intellectual property rights and these ideas should not be reproduced in any other software without our permission.

General Picture Recognition Software