AI algorithms can be divided on two classes: supervised and unsupervised algorithms. Some of the most common examples of artificial intelligence algorithms include: Decision Tree Algorithms, Deep learning, Linear Regression algorithms. Artificial intelligence is a very complex field.
I built an evolutionary algorithm for retail inventory replenishment in a product targeted at huge plant nurseries (and there are some really big, smart ones -- $200m companies).
It was probably the coolest thing I've ever worked on. Using three years of historical data, it crunched and evolved for a week straight while I was on vacation.
The end results were both positive and bizarre. Actually, I was pretty sure it was broken at first.
The algorithm was ignoring sales from the previous few weeks, giving them a weight of 0 for all indicators (which is at odds with how these guys currently work -- right now they consider the same week in the previous year and also factor in recent trends).
Eventually I realized what was going on. With the indicators the organism had to work with, over time it was more efficient to look at the same part of the previous month and ignore recent trends.
So instead of looking at the last several days, it looked at the same week in the previous month because there were some subtle but steady trends that repeat every 30 days. And they were more reliable than the more volatile day-to-day trends.
And the result was a significant and reproducable improvement in efficiency.
Unfortunately, I was so excited by this that I told the customer about it and they cancelled the project. That first run was extremely promising, but it was hard to sell as proof even though you could crunch almost any data from the last three years and see that the algorithm magically improved efficiency. EA's are not hard, but people find them convoluted at first, and the idea of doing something so arcane was just a little bit too much to swallow.
The big takeaway for me was that if I ever create something that appears a bit too magical, I should hold off on talking about it until I can put together a good presentation. :)
Some times ago, I've found this series of articles: Designing Emergent AI.
The author of these articles has created the game "AI War: Fleet command" that features an emergent AI. Maybe you'll find this interesting.
Slightly outside of the traditional AI realm, are HTMs (Hierachical Temporal Memory) as developed at Numenta. This technology is still in its early stages but shows promises in the targeted "WOW factor" areas.
So far the most impressive aspect of AI has been the ratio of promises to deliveries. In my opinion, the only truly viable approach to computer-based intelligence is simulated neural networks, because all of the things in the real world that we consider to be "intelligent" (humans, chimpanzees, dogs, cockroaches etc.) all possess variants of the same basic control system: a big mess of neurons hooked up to input and output devices.
Amazingly, despite this apparent truth, the Computer Science field that calls itself "neural networks" has pretty much abandoned the attempt to simulate actual biological neurons and neuronal structures. I couldn't begin to tell you why this is the case, although I suspect it's because programmers in general do not like going outside their comfort zones and learning about topics outside of Computer Science.
The only upside to this is that Terminator is still just a movie.
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