The definition of artificial intelligence (AI) is complex and varies among software developers and AI researchers. There are two main camps in AI: the Neats and the Scruffies, who have different approaches to AI development. All AI projects aim to mimic some aspect of intelligence, and goals are assigned based on utility. The dynamics of achieving these goals differ widely between AI.
There are various forms of artificial intelligence (AI) out there today. What to call an artificial intelligence and what to simply call a software program is also a difficult question. There’s a trend in software, where when something that used to be called “AI” matures and integrates into the technology landscape, it’s no longer called AI. The programmers of the 1950s might call a lot of software embedded in our world “artificial intelligence,” for example, the microchip in your car that regulates fuel injection, or the supermarket database that stores records of all sales, or the google search.
But the field that calls itself “Artificial Intelligence” tends to be slightly different from the much larger group of “software developers in general”. AI researchers tend to look at forms of software that are more complex, adaptive, capable, or even vaguely human-like. Workers in AI also tend to be interdisciplinary and well versed in areas of science and mathematics foreign to the typical programmer, including but not limited to: formal statistics, neuroscience, evolutionary psychology, machine learning, and decision theory.
In the field of AI, there are two main camps: the Neats and the Scruffies. The division has held pretty much since AI was founded as a field in 1956. Nets are proponents of formal methods like applied statistics. They like their programs to be well organized, demonstrably sound, operate on the basis of concrete and freely modifiable theories. Scruffy people love messy approaches, like adaptive neural networks, and consider themselves hackers, putting anything together until it seems to work. Both approaches have had impressive successes in the past, and there are hybrids of the two themes as well.
All AI projects are at least superficially inspired by the human brain, because by definition AI is about mimicking some aspect of intelligence. AIs have to construct concepts of the things they manipulate or work with, and store those concepts as blocks of data. Sometimes these blocks are dynamic and frequently updated, sometimes they are static. Generally an artificial intelligence is concerned with exploiting the relationships between data to achieve a goal.
Goals are often assigned based on utility. When a goal is presented, an AI system can generate sub-goals and assign these sub-goals utility values based on their expected contribution to the main goal. The AI keeps pursuing secondary goals until the main goal is achieved. Then he’s free to move on to a new (but often similar) primary goal. What differs widely between AI is how all these dynamics are implemented.
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