On the use of memory

The difference in memory in computers and organic memory is a strange thing. The memory in computers is linearly aligned and stores elements very differently than organic memory. Where the storage area is not large enough in computers, we tend to use databases to make the memory "searchable" and mallable.

Computers generally need to trawl through the entire space in order to find something, albeit with clever algorithms rather than an exhaustive search. We're modeling the world around us into a specific, definition to work with and then compare new input to those things we know.

Organic memory is very different. Through the input of elements and situations, the recall of other elements in memory seems activated automatically without the need for a search. As if very small biological elements recognize features in the stream of information and respond. This may cause other cells to become activated too, thereby leading to the recognition of one set of features with previous experiences. It's as if the recognition drifts up automatically, rather than that a general CPU searches for some meaning based on a description or broken down representation.

The problems of computers then is our limitation / lack of imagination to be able to represent memory in a non-linear form. The way how computers are modeled require the programmer to define concepts, elements and situations explicitly or less explicitly using rules and then search, within a certain space, for similar situations and reason further from there. It's totally lost if the situation cannot be mapped to anything.

I was thinking lately that it would be a ground-breaking discovery if memory could be modeled in similar ways to organic memory. That is, the access to memory being non-linear and a type of network, rather than developing a linear access algorithm. If you consider a certain memory space where a network may position its recognition elements (neurons?), then the connection algorithm of a current computer should map the linear memory differently into a more diffuse space of an inter-connected network. Basically, I'm not saying anything different than "create a neural network" at this time, but I'm considering other possibilities to use the mechanical properties of memory access in a clever way and as such to reduce the required memory for connection storage and to figure out a possibility to indicate "similarity" by the proximity of memory address locations.

Or, alternatively to that, use a neural network to determine a set of index vectors that will map to a large linear space. The index vectors can be compared to a semantic signature of any element. This signature should be developed in such a way that it is categorizing the element from various perspectives. Basically, considering the ability for semantic indexing of text, the technique is used to find texts that are semantically similar.

The larger the neural network, the finer its ability to recognize features. But our minds do not allocate 100 billion neurons to the (same) ability of pattern recognition. Thus, you could talk of specialized sub-networks of analysis that together define a certain result (binding problem).

But perhaps we're thinking too much again in the terms of input-processing-output as I've indicated before. We like things to be explicitly defined, since it provides a method of understanding. What if the networks don't work together in a hierarchy (input->network1->network2->output->reasoning), but work together in a network themselves?

Then this would mean that such a network could aggregate information from different sub-networks together to form a complicated mesh itself. The activation of certain elements in one part could induce the activation of neurons in another, leading to a new sequence of activation by reasoning over very complicated inputs of other neuronal networks. For example, what if thinking about a bear causes our vision analysis network to fire up / become induced and then produce a picture of such a bear?

Imagine a core of a couple of neural networks that have complicated information available to them from "processing" neural networks before them. If those core networks are interconnected in intricate ways and influence one another, then it's likely that a smell causes the memory of a vision or hearing in another network, albeit slightly weaker than normal.

Leaving this thought alone for now...

Semantic Intelligence

I'm reading up as much as I can about semantic search. What I find on the Internet so far are quite a number of marketing materials, which shows that the concept of semantics is still very new. The direction taken in these materials is generally the analysis of language, linguistics, attempting to re-create common sense in a computer, as if it were possible to allow it to reason.

I'm very skeptical about these approaches at the moment, but don't totally discard it. The problem with a computer is that it is a fairly linear device. Most programs today run by means of a stack, which is used to push information about current execution context. Basically, it's used to store contexts of previous actions temporarily, so that the CPU can perform other tasks either deeper or revert to previous contexts and continue from there.

I'm not sure whether in the future we're looking to change this computing concept significantly. A program is basically something that starts up and then, in general, proceeds deeper to process more specific actions, winds back, then process more specific actions of a different nature.

This concept also more or less holds for distributed computing, for many ways this is implemented today. If you look at Google's MapReduce for example, it reads input, processes that input and converts it to another representation, then stores the output of the process towards a more persistent medium, for example GFS.

I imagine a certain model in the next paragraphs, which is not an exact representation of the brain or how it works, but it serves to purpose to understand things better. Perhaps analogies can be made to specific parts of the brain later to explain this model.

I imagine that the brain and different kinds of processing work by signalling many nodes of a network at the same time, rather than choosing one path of execution. There are exceptionally complex rules for event routing and management and not necessarily will all events arrive, but each event may induce another node, which may become part of the storm of events until the brain reaches more or less a steady-state.

In this model, the events fire at the same time and very quickly resolve to a certain state that induce a certain thought (or memory?). Even though this sounds very random, there is one thing that gives these states meaning (in this model). It is the process of learning. The process where we remember what a certain state means, because we pull that particular similar state from memory and that state in another time or context induced a certain meaning. In this case, analogy is then pulling a more or less similar state from memory, analyzing the meaning again and comparing that with the actual context we are in at the moment. The final conclusion may be wrong, but in that case we have one more experience (or state) to store that allows us to better define the differences in the future.

So, in this model, I see that rather than processing a many linear functions for a result, it's as if networks of different purposes interact together to give us the context or semantics of a certain situation. I am not entirely sure yet whether this means thought or whether this is the combination of thought and feeling. Let's see if I can analyze the different components of this model:

• Analysis
• Interpretation
• Memory
• Instinct, feeling, emotion, fear, etc.
  

That is interesting.

Well, the difference that this model shows is that semantic analysis talks about generally accepted meaning rather than individual meaning. The generally accepted meaning can be resolved by voting or allowing people to indicate their association when a word is on screen. This seems totally wrong. If for example a recent event, like 9/11 occurs, and the screen shows "plane", most would type "airplane" and the meaning of that word will very quickly distort other possible meanings: a surface, an "astral" plane, geometric plane, compass plane, etc. Meaning by itself doesn't seem to bear any relationship with frequency.

If this holds true, then it means that as soon as any model that shapes semantic analysis in computers has any relationship with frequency, it means the model or implementation is flawed.