## Friday, January 02, 2009

### The Matrix Aggregate

Matrices are mathematical tables, which are used to record elements of data in the world around us. These are widely used for example in keeping track of rotation and translation operations like "SLERP" in 3D computations for games or simulations. Matrices are also used in the Singular Value Decomposition and have many other uses. After the recording of data in (possibly huge) matrices, one can perform various operations on the data, often resulting in a destination matrix that conveys a certain meaning.

Matrices thus are very interesting for Artificial Intelligence. It can operate on large datasets with the objective to process that information into something new, which then is used as a shortcut for making predictions for example.

A limitation of matrices is that all the information for a single timepoint or a range of timepoints needs to be available. This is often very difficult to achieve, or the resulting matrix may become so large that the general PC struggles with available memory to perform the computations.

Many academic texts written on consciousness and artificial intelligence are written from the perspective of the computational mind. But they are also written from the perspective of an algorithm. Since most (if not all?) algorithms are serial, this also suggests that the mind or the brain is serial. This is certainly not so, each neuron can fire independently in time and need not be given any CPU time for the neuron to actually fire and influence other neurons.

This suggests a parallel nature as large as the number of neurons available in the human brain. So, not only do we have more neurons in the brain than the common computer can hold by itself (not even counting the memory needed for maintaining connections), each neuron also operates as if it were a CPU by itself.

It's certainly the case that some algorithms can be parallellized, therefore allowing them to run on different devices and then have their results combined to find the answer. This is what is meant with parallel algorithms in the field of computer science.

Here though, we should also consider parallel algorithms to be algorithms that are truly parallel in nature, algorithms which run on many different processors and operate on the same data.

Just recently, I wondered what would happen if some sort of chemical concept were introduced in ANN's. Thus, an ANN would not just execute on neurons, thresholds and biases to find new values, but one could introduce chemicals that would change how neurons fire in the ANN. The applications of this aren't really clear as of yet though.