Hello,
I thought I would share this informaton with the group and start a new topic. I came across this information the other day regarding neural networks and the applications in electronic design. Take a look below.
A long time goal of artificial intelligence (AI) is the development of methods that can learn from examples to recognize events and make decisions. For example, automatic recognition of speech is already in widespread use, although this technology is far from usable in ordinary conversations. Another example is the recognition of handwriting; here the technology is still primitive. The eventual wide-spread consumer use of decision making machines and robotics will depend on the development of powerful, inexpensive, trainable devices which will allow the evolution towards thinking machines with capabilities far in excess of today's speech processors and robots.
NSC was founded to exploit a trainable high-speed technology called trainable digital logic (TDL) that is inexpensive to implement for recognition and decision making. TDL could form a basis for widespread use of broader AI technology.
Consider a digital device that performs recognition and outputs a yes or a no when the input is a digital word. For example, the input might be numbers that are parameters characterizing an electrocardiogram (ECG) beat, and the device must decide if the beat is a dangerous arrhythmia.
When a sequence of bits is input to a logic circuit, which then outputs a 1 or a 0, (e.g., answers yes or no), the circuit is called a switching function. All digitally implemented pattern recognizers (such as neural networks) that have a binary output are, in fact, switching functions.
The word "trainable" means that examples of known categories (e.g., both dangerous and normal ECG beats) and a training algorithm can be used to organize the device. The trained device will give the correct answer (classification) when presented with examples where the category is not known.
A key to NSC's technology is the development of efficient training algorithms that can organize binary logic into complex switching functions by using only binary inputs from known categories. The number of categories need not be restricted to two; NSC has constructed logic for up to sixteen categories.
I didn't want to put the full article here, so to read the full technology - you can find it here: