Pattern Recognition: A Double-Edged Sword

Nov 29, 2016 • Joe Schueller

Why is pattern recognition so great?

Why does pattern recognition suck?

There is no way around it. If humans are good at spotting patterns, we are bound to pick up some false patterns as well. Our tendency to pick up false patterns actually has a name: Apophenia.

Where do we find false patterns?

How do we deal with it?

There is also a certain level of risk that comes with false pattern recognition. False positives and false negatives in the medical setting carries a high amount of risk. Accounting for the risk involved can get complicated. It might involve attaching a percentage which represents the probablility of error. It could also involve developing a margin of error for the results.

Now we’ve already covered pattern recongnition in humans, but we have yet to discuss pattern recognition in computers. Computers are getting (creepingly) good at detecting patterns, especially faces. When uploading pictures to facebook, you’re asked to tag certain people detected in the picture. But just like humans, computers can fall prey to false pattern recognition. “Yes, that person in the background of my cover photo does look a bit like my dad, but it’s not him..stupid facebook.”

Why do computers recognize false patterns?

Reason 2. Computers also recognize false patterns because they really suck at knowing the context of a situation. A pattern might yeild good results in 95% of the situations but fail in 5% of situations. For example, a computer could find the faces of people pretty easily, but it would have trouble figuring out if a face belongs to an actual person, or just a picture of face (or a picture of a picture of a face).

Gosling and Culkin Shirt-ception

So why is this dangerous?

The combination of limited context and speed can lead to things like the Flash Crash of 2010. In the Flash Crash, the stock market dropped rapidly with no apparent reason. Computers systems that recognized a sharp decline decided to sell which only accelerated the drop. Within a matter of minutes, the stock market dropped 9%. The trillion dollar crash, which recovered soon afterwards, prompted investigations to determine the role of automated systems in the stock market. Situations like this show the speed and scale of computer systems.

In conclusion…