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Evolution theory teaches that survival is for the fittest living organism.

What is life? What is fit? And what does it mean to survive?

Well, there are many definitions of course. A useful definition, in scientific perspective, is the one that allow us to analyze myriads things with fewest principles. So here they are.

A living organism is a class of objects that share 3 traits.

1. Reproduce. Living organism can make copies.
2. Inherit. Living organism, in the process of reproducing, will inherit traits to the offspring.
3. Mutate. Living organism, in the process of inheriting, will make small mistakes.

That seems like a reasonable definition. If we look most living things around us, like dogs, cats, pigs, birds, fido, virus, etc., they all share those traits.

However, based on those definitions, God is not alive, while computer virus, religious doctrines, ideologies, and ideas are alive.

I don’t think God will mind though. It’s just a definition.

The practical aspect is that we can use evolution theory to explain the sort of humans, cats, dogs, germs, doctrines, ideologies, and ideas that are common nowadays.

Different living organism performs those 3 traits differently. Those living organism that reproduces a lot becomes plenty. Those that reproduced a lot, become common.

That’s the basic of evolution theory.

For example, we see that peacocks tend to have long elaborated tails. From this, we can guess peacocks with long elaborate tail must have made more peachicks. Perhaps, peahen love peacocks with long elaborate tails. It’s true.

Suppose it were true. Then peacocks with longer tails will mate with more peahens. Those happy couples will then produce more peachicks.

Male peachicks will inherit long tails. Female peachicks will inherit preferences for long tail. So, peahens like peacocks with long tail.

It looks like a circular argument. It is. The truth is we don’t really know for sure, at least just from the reasoning, why peacocks have long tail.

However, we do know that traits that lead to gene pool survival through sexual selection tend to be positive feedback.
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What shape are ball bearings? They are shaped like a ball, as everyone knows, right?

The funny thing about what everyone knows is that everyone can be wrong. For instance, everybody thinks that the America’s Cup is an ocean-sailing race, and yet the Swiss managed to win the Cup. For those readers who are geographically-challenged, Switzerland is a land-locked country comprised entirely of mountains.

What does this have to do with ball bearings. Very little, I suspect, but balls have very little to do with ball bearings, either. Ball bearings look more like hula hoops. But don’t try using them for that – you will find them inconveniently heavy and painfully small.

You can view a picture of a ball bearing in the middle of this ball bearing supplier’s page.

So what are those stunted metal tube donuts called ball bearings for anyway? Are they used as a spare wheel? Do they hold in evil shop-floor spirits? No, they help things move more efficiently. In an early demonstration of bearing usage, three ladies pulled a locomotive (It was just a demonstration, not a career development).

Many bearings look very similar, whether they are ball bearings, roller bearings or other bearings. What?! Other bearings?

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Artificial Intelligence as a research field was born in the summer of 1956 during a seminal workshop at Dartmouth College in Hanover, New Hampshire. It was just a year before that when Marvin Minsky, Nathaniel Rochester, Claude Shannon and John McCarthy proposed that they should hold a workshop to put together a roadmap about how to make machines think and learn similarly to humans. The ultimate goal was to discover computational models in order to enable machines to do commonsense reasoning. Today, John McCarthy is rightly considered the father of AI. I should note that the term “Artificial Intelligence” appeared for the first time in the proposal put forth by the previously mentioned scientists. And so this new discipline that would eventually captivate everyone’s imagination was born.

Artificial Intelligence had its ups and downs in the last 50 years. Early success solving small problems in simulation ignited a flurry of predictions about super intelligent machines taking over the world before the coming of the 21st century. Hampered by a lack of a good understanding of how commonsense reasoning works in people and a lack of computational resources, computers being very slow up until the mid nineties, AI research stalled in the 80s. Many people rushed to dismiss it as nothing more than hot air.

However, science is all about proposing and testing new theories in order to find the best ones. Since the mid-90s, AI research has advanced by leaps and bounds. We now have a better understanding of how the human brain works and that has helped us to find and test better computational models for AI. These in turn have also helped us to better understand the functions of the human brain. New techniques such as statistical analysis are helping intelligent agents to copy with large amounts of information and noisy sensors. Faster computers with vast amounts of storage are allowing us to experiment in more challenging domains and solve larger problems.
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