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Yaser Khalifa and Evolutionary Algorithms

This is Yaser Khalifa's website about his experiences with evolutionary algorithms.

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Robots are Taking Over the Streets: Uber’s Driverless Cars

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pexels-photo-largeFor imagineers of the past, the 21st century was projected to be the era of the flying car. While that was how the family in the American cartoon The Jetsons got around, it is not exactly the reality of humans in 2016. To be clear, manufacturer Terrafugia is in the process of creating such a device, but it will likely be awhile for it hits the market.

 

Lucky for us, however, we have something somewhat closer to the world of the future seen in movies. Ride sharing company Uber debuted its driverless cars in Pittsburgh just last month. The technology is a combination of multiple cameras, a LiDAR system, which creates 3-D maps of the vehicle’s surroundings, and antennae for GPS. It is a robot on wheels, essentially, that signals a wave of what’s to expect in transportation over the next few years and decades.

 

For those concerned about a robot takeover that would make humans obsolete, we’re not exactly there yet either. Though Uber has made it clear that it does hope to cut down on its largest expense, its drivers, the car is not completely personless. During Business Insider’s test of this new mode of transport, there was an engineer in the passenger seat and a driver behind the wheel to take over, because robot’s don’t know everything and human interference is still necessary as the machine is tweaked to operate nearly as well (or better, depending on the situation) as a human would.

 

An example of such an issue was highlighted in Quartz, in which a self-driving Uber turned down a one-way street, unaware of the areas signs and/or rules. In another instance, a driverless car was involved in an accident, and again required human assistance. These situations show that there is still work to be done on this technology, but that is precisely why the company chose Pittsburgh as its starting point: because it’s old and at times difficult and hard to navigate.

 

As with all new technology, I imagine driverless cab will take some getting used to, but now is as good a time as any, considering the high level of interactions humans have with robots on a daily basis. With every passing generation, more technology is integrated into the natural course of our life. How we get from place to place is a natural part of that evolution. Wouldn’t you agree?

Evolutionary Algorithms Are More Common Than You Know

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pexels-photo-largeTalk to someone unfamiliar with the work of artificial intelligence about evolutionary algorithms and you may get a blank stare or, at best, an look of bewilderment and a subsequent inquiry about what exactly the phrase means. Once explained, the concept may provide a bit of insight–to be sure, evolutionary algorithms are defined as a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm, which uses mechanisms inspired by biological evolution–but not many are aware, or fail to consider, that they’ve encountered such many times, in various forms.

Take, for instance, genetic algorithms, which are a type evolutionary algorithm that is a search heuristic mimicking the process of natural selection. The purpose is to generate solutions to optimization problems using techniques inspired by natural evolution. This video is explains it in full, in under five minutes:

Below, I’ve included some of the most common things which use or include genetic algorithms:

Computer Gaming

Online gaming is highly popular–so popular that the number of individuals who participate engage in gaming around the world, account for over 44% of the total number of people online, at 700 million gamers in 2013. Offline, games are equally popular, with people simply using their devices create wholly different worlds from their own, such as The Sims, which has won a Guinness record for being the best selling PC game of all time. The game, now in its in 16th year, uses genetic algorithms instead of having users play against humans online. Instead, The Sims is programmed to learn and incorporate strategies from previous games in which users have been successful, using game theory.

Finance

Financial markets are always changing. Genetic algorithms help deal with nonlinear problems of trading. Investopedia describes it this way: “Genetic algorithms are created mathematically using vectors, which are quantities that have direction and magnitude. Parameters for each trading rule are represented with a one-dimensional vector that can be thought of as a chromosome in genetic terms. Meanwhile, the values used in each parameter can be thought of as genes, which are then modified using natural selection.”

Cars

Race cars are not necessarily designed for everyday travel. Instead, they are crafted for sport and the ability to reach high speeds. A large part of their functionality are a result of design. Airplanes as well, created to travel high altitudes at fast speeds, need to be designed well. Genetic algorithms provide combinations of materials that would work best, from an engineering perspective, which then enables designers to put them together and save time on continuous testing.

These are just a few examples of how people interact with genetic algorithms very often. As our abilities in science continue to evolve, applications and the prevalence of such will likely continue to grow. For a full list of uses, be sure to visit this blog from Brainz.org and share with friends.