The relationship between rationality and intelligence

Every day, we make decisions on a continuous basis. We've come across many situations before, thus can reliably estimate a path of best resolution for experienced situations. In other cases, we haven't seen too much of a similar situation, but still develop an opinion, gut feeling and most likely undertake on a path for resolution.

We can call our thought and actions rational or not rational. The word rational refers to an ability to fully explain an action and most likely we'll agree that assumptions are not taken as an acceptable means of forming it, unless we have data/information to back up those claims. Thus, rationality involves an act or decision that is developed from a calculation and estimate of existing experiences. Irrational thoughts and actions are the products of assumptions, incomplete data or little experience. You could closely couple rationality with logic, although rationality may be a little larger than logic. Logic requires predicates and through logic and knowledge represented in the rules of logic, one can "reason" about the validity of claims, thoughts and actions. However, since logic follows those rules only, whenever knowledge is not embodied within the rules, the system cannot appropriately confirm or deny a specific claim, thought or action.

Intelligence could be seen therefore as the ability to act outside the realms of logic and rationality, based on the premise of uncertainty, and intelligent reasoning is the ability to infer new relationships through trial and error or 'logical reasoning' with analogous material and developing gut-feel probabilities that another situation will behave in similar ways or slightly different with expectations on how it will differ (although we could be really wrong there).

Induction is the ability to estimate the outcome of a situation based on a set of assumptions, initial states, goals and effects. Deduction is the ability to find out under which conditions a situation came to be. Both are intelligent actions.

A computer is a pure rational machine. It acts within the knowledge it was given and we haven't so far agreed that computers are really intelligent. Although some systems exist that can perform very specific tasks in a very efficient way, those systems are entirely rational and cannot deduce/induce new knowledge from their surroundings (enrich themselves with new programming).

Rational is also defined sometimes as "void of emotion and bias". This bias is caused by how easy it is for you to recall memory from similar situations. Stronger emotional situations generally are easier to remember (and this is generally for the good). Many times, we're over-compensating risks related to explosions, accidents or attacks, more than what is needed to appropriately reduce the risk. Some academic research is highly biased, because the author wanted to find the evidence that his claims are true, rather than remain open to find contradictory results. Rational reasoning thus requires us to eliminate the bias, not be guided by opinion, but rely on facts and computation to come to a conclusion.

The following text is related to power and rationality:

http://flyvbjerg.plan.aau.dk/whatispower.php

The interesting question that you can derive from the text is: "How can people in important governmental positions correctly apply the power that is given to them and make rational decisions in the interest of the people they serve?".

In order to make rational decisions, we may not be biased by irrational opinion. That is... the thoughts and arguments that we come up with must be fully explainable and not be tainted by personal expectations from the leader. We can choose to trust the leader on those claims, but without any explanation given, there is little reason to provide that trust.

Artificial Intelligence in this sense can be applied to some of these problems, although it should probably not be considered leading? There are some AI programs for example in research that can be used by the justice system to analyze historical cases. A current case can then be evaluated against the historical punishments, such that the judge has an extra tool to ensure the punishment given is fair and enough, considering the situation and previous cases. Certainly, each case by itself is one to be considered individually, but the programs give an indication of the similarity. It's thus a tool for the judge to verify his own bias, if any exists.

VU start

I visited the university today because of the start of colleges. Picked up most books except a few and then... start studying in the evenings and weekends.

The recent post about frozen realities is a nice one to extend further. The meaning and definition of "intelligence" is also one to think about before one calls a system "intelligent". It's used as a buzzword. As wikipedia states it:

"Intelligence (also called intellect) is an umbrella term used to describe a property of the mind that encompasses many related abilities, such as the capacities to reason, to plan, to solve problems, to think abstractly, to comprehend ideas, to use language, and to learn. There are several ways to define intelligence. In some cases, intelligence may include traits such as creativity, personality, character, knowledge, or wisdom. However, most psychologists prefer not to include these traits in the definition of intelligence." (source: wikipedia).

As with "emergence", we should not consider intelligence to be subject to one entity within a system, we should remain open enough to allow definitions where relating entities together, as part of one system. demonstrate intelligent activities. Just as with ants, it's possible with computers that interacting components are smarter than the sum of their individual parts.

The following is a current imagination and I may change my mind on it. Think of the human brain as a set of components that control motor functions and analysis / pattern recognition functions. The ability and function of each component is pre-determined through DNA, but the way how they interact with other components is to be learned. A supervisor in the brain controls through feedback mechanisms of the input sensors whether a certain desired result was achieved. The desired result is also dynamic and a pattern.

Actually, everything is a pattern of some kind. The patterns are stored in huge containers where each container has patterns of the same type. Pattern recognition and indexing sounds like a very complicated affair. For example, the smell of hot pizza is not just "pizza". There is no such smell. It's the smell of cheese, dough, hot cheese, tomato and everything else what's on there. The individual smells make up the rest. Depending how trained you are, we could still wonder whether someone can guess what's in the oven? The more information we use to construct our environment, the more senses we need to make sense of it.

We tend to automatically direct other senses for the confirmation of certain impulses. Such as looking into the oven, listening intently to some events and so forth.

The idea here is then that it sounds difficult for a single sense to function properly by itself. It's missing a lot of information to "get around". So the conjunction of patterns from different senses can be used to provide a deeper definition of the environment than a single sense can.

A very difficult thing is visual recognition and deconstruction. I don't think we're able to actually store every pixel of everything we see. Rather, I believe we store some kind of gist, a simple three-dimensional deconstruction of the image and some color features for each. This could differ from person to person of course and explains why certain people are artists and others are not :). The ability to see perspective is a very difficult one. We're able to see that, because we know that some things are larger than just what we see, thus we know something is in front of something else. Also, we strongly use shadows and tint differences to further analyze a scene.

In OpenGL, we speak of a pipeline to construct a scene. But when you look at an image, we also need to think of a pipeline for de-rasterization. Possibly, as soon as a computer is able to construct simple wireframes from simple images, we're steps closer to creating a computer that can recognize objects easier.

Whereas many people consider the definition of things equal to "naming it", this is also wrong I think. A definition of something can also be considered: "a formal recognition and method of communcation through symbolic means, such that it calls up the same pattern and recognition with another intelligent being". Thus, we need not restrict ourselves to using words. If we were telepathically endowed and could transfer our thoughts, we'd probably call that "definition" instead.

So, calling something "car" then is just a specific name for a specific generic pattern. Moreover, the specific car that one has in mind is likely different from somebody else. So the patterns we really evoke are possibly different from person to person, yet we all catch the gist of the message.

If you consider that a pattern can be associated with a term, why not consider the possibility that certain indexes can be given a certain name or list of names?

Then, intelligence becomes the ability to reason with those patterns that are similar to some extent in order to make something out of it that re-defines your reality. A certain kind of juggling and analysis on similarity, possibilities, abilities and relationships (plus defining new ones, aka learning) where certain rules exist that should not be broken. For example, most cars can't drive on water. When your car leaves, so do its tires. A car should not drive with its door open. A car should have wheels to drive.

The problem of reasoning then is how rules are embedded within this reasoning system. The patterns *are* the symbols and the names it has been given. Semi-patterns that are lightly activated form new possible paths. Is it that the path of one pattern to another forms the embodiment of a rule?