Due to the advancements in technology, teens and adults alike can now partake in virtual worlds thanks to video games. Video games are enjoyed as a hobby all over the globe, but some gamers have made it their career with help from the ever-growing e-sport community. This is an inside look at the professional level of gaming from an ex-MLG participant, and what I remember going through when starting to play video games at an elite level.

One of the premiere and most popular leagues within the United States happens to be Major League Gaming or MLG for short. This is a league that usually involves more of the most recent games out, and they create circuits for each major title and its subsequent releases. Two of the most major game circuits within the MLG league were the Halo series and the Call of Duty series, both which happened to be first person shooters (FPS). There were a potential hundred or so teams within each circuit, but much like other competitions, the circuits were ran with winner’s brackets and losers brackets. This means that out of all the teams that would show up to MLG events, about the top eight of each bracket would really be known as the "elite" players. I personally played in the Gears of War circuit at venues like MLG Raleigh and MLG Toronto, and we had very few teams compared to Call of Duty and Halo. The amount of participants at each event usually varies in each circuit based on the popularity of the game being played.

When you win tournaments, the payouts are split between the team members. This means that looking at playing in the MLG for a life career is an ill-advised move. The cost to get to events and buy team passes usually negates the prizes you win most of the time, considering by the time that the prize money is split you are left with about $800 in a popular circuit (Like Call of Duty). The payouts are usually only high in special and certain occasions, one for example being the million dollar showdown that Infinity Ward hosted for Call of Duty: Modern Warfare 3 a couple years back. The way that players that make professional gaming their career get the big money now is by being sponsored by the big companies that back the league like Red Bull and Hot Pockets. MLG players like "Walshy" and "FeaR Moho" were sponsored early on in the league and were able to make a living off of the games they played. I would imagine them getting around $60K in a good year off of sponsors alone. I would go even as far as to say that if you do not have a sponsor in e-sports, you will not be financially successful in the career.

Being an MLG gamer requires passion and understanding for the games. If you just want to make money, then you are better off working at McDonalds.

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Machine learning systems are equipped with artificial intelligence engines that provide these systems with the capability of learning by themselves without having to write programs to do so. They adjust and change programs as a result of being exposed to big data sets. The process of doing so is similar to the data mining concept where the data set is searched for patterns. The difference is in how those patterns are used. Data mining's purpose is to enhance human comprehension and understanding. Machine learning's algorithms purpose is to adjust some program's action without human supervision, learning from past searches and also continuously forward as it's exposed to new data.

The News Feed service in Facebook is an example, automatically personalizing a user's feed from his interaction with his or her friend's posts. The "machine" uses statistical and predictive analysis that identify interaction patterns (skipped, like, read, comment) and uses the results to adjust the News Feed output continuously without human intervention. 

Impact on Existing and Emerging Markets

The NBA is using machine analytics created by a California-based startup to create predictive models that allow coaches to better discern a player's ability. Fed with many seasons of data, the machine can make predictions of a player's abilities. Players can have good days and bad days, get sick or lose motivation, but over time a good player will be good and a bad player can be spotted. By examining big data sets of individual performance over many seasons, the machine develops predictive models that feed into the coach’s decision-making process when faced with certain teams or particular situations. 

General Electric, who has been around for 119 years is spending millions of dollars in artificial intelligence learning systems. Its many years of data from oil exploration and jet engine research is being fed to an IBM-developed system to reduce maintenance costs, optimize performance and anticipate breakdowns.

Over a dozen banks in Europe replaced their human-based statistical modeling processes with machines. The new engines create recommendations for low-profit customers such as retail clients, small and medium-sized companies. The lower-cost, faster results approach allows the bank to create micro-target models for forecasting service cancellations and loan defaults and then how to act under those potential situations. As a result of these new models and inputs into decision making some banks have experienced new product sales increases of 10 percent, lower capital expenses and increased collections by 20 percent. 

Emerging markets and industries

By now we have seen how cell phones and emerging and developing economies go together. This relationship has generated big data sets that hold information about behaviors and mobility patterns. Machine learning examines and analyzes the data to extract information in usage patterns for these new and little understood emergent economies. Both private and public policymakers can use this information to assess technology-based programs proposed by public officials and technology companies can use it to focus on developing personalized services and investment decisions.

Machine learning service providers targeting emerging economies in this example focus on evaluating demographic and socio-economic indicators and its impact on the way people use mobile technologies. The socioeconomic status of an individual or a population can be used to understand its access and expectations on education, housing, health and vital utilities such as water and electricity. Predictive models can then be created around customer's purchasing power and marketing campaigns created to offer new products. Instead of relying exclusively on phone interviews, focus groups or other kinds of person-to-person interactions, auto-learning algorithms can also be applied to the huge amounts of data collected by other entities such as Google and Facebook.

A warning

Traditional industries trying to profit from emerging markets will see a slowdown unless they adapt to new competitive forces unleashed in part by new technologies such as artificial intelligence that offer unprecedented capabilities at a lower entry and support cost than before. But small high-tech based companies are introducing new flexible, adaptable business models more suitable to new high-risk markets. Digital platforms rely on algorithms to host at a low cost and with quality services thousands of small and mid-size enterprises in countries such as China, India, Central America and Asia. These collaborations based on new technologies and tools gives the emerging market enterprises the reach and resources needed to challenge traditional business model companies.

Python is an incredibly powerful and useful computer programming language that many of the biggest websites in the world rely on for their foundation. Python provides reliable results that are functional and involve a variety of dynamic scripted and non-scripted contexts. And because it is free and open source, it has remained a popular choice for a variety of different developers who are looking to build new sites on one of the most reliable languages available. Here is a look at 10 of the most famous software programs that are written in Python and what they do.

YouTube
If you love watching hours of homemade and professional quality video clips on YouTube, you can thank Python for giving you that option. The foundation for Python helped YouTube integrate streaming videos into their pages, as well as the ability to like videos and embed certain information. YouTube is one of the most popular sites on the Internet, and it runs off of one of the most powerful languages in Python.

DropBox
What started as a powerful app, DropBox is now used by a variety of individuals, businesses, companies, organizations and more. This program lets you save files to a cloud-based service, that you can then access from anywhere in the world. With Python at the root of DropBox, there is no longer a need for USB sticks or blank CDs, since you can now save and share everything with your cloud-based account.

Google
It takes a lot of power to be able to handle the most popular search engine in the entire world. That is why Google uses Python for its mainframe foundation, as well as in addition to various apps that it runs in conjunction with the main site. The ease that Google provides for finding certain information, would be impossible without Python at the core.

Quora
Got a question? Ask it on Quora. This site compiles a list of questions and answers that come from a community of individuals. Those questions are then organized by various members of the community, which puts the most relevant information at the top. The creators of Quora, who happened to be former Facebook employees, decided to use Python to help them create the world’s best Magic 8 ball in Quora.

Instagram
If you love taking photos of your food or a new outfit and posting it online for all of your friends to see, you can thank Python for that ability. Granted, Instagram has both a very powerful app and a website, but the latter runs on Python language. The system allows for users to browse, find and post pictures that they like on the site.

BitTorrent
BitTorrent has evolved quite a bit in recent years, but its foundation and earlier years were built on Python. When it comes to one of the largest databases of knowledge, media and content, BitTorrent is the way to go. But you wouldn’t be able to get any of those lectures or other legal stuff that you are downloading from BitTorrent, if it wasn’t for Python.

Spotify
Spotify changed the music game when it allowed you to listen to ad-free music of your choice. This wasn’t a program where you got to select a playlist, but rather full songs that you love, on repeat as many times as you can imagine, if you so desire. But whether you are rocking out to the latest K-Pop song from Psy or a classic jazz tune, you are doing so because Spotify was built on Python.

Reddit
Reddit is one of the biggest open communities on the web. You have a question, want to talk about something in specific, or find tons of information regarding a particular topic, you can just look on Reddit. The site relies on Python to help them store user names, categorize subreddits, upload links to GIFs and, of course, award gold to valued posters.

Yahoo Maps
Much like Google, Yahoo also uses Python for a variety of different resources. Most valued may be Yahoo Maps. The API and programming behind the maps program, which is built with Python, allows for users to find locations, get directions and even find reviews about local places.

Hipmunk
If you love to travel, you have likely come across Hipmunk. And while the site lets you save money on booking your itinerary through Hipmunk, it is Python that keeps everything organized. Python also helps sort the best discounts and rates, so you can get the best packages available.

Python is an incredibly powerful tool for web development. More and more sites rely on it, including 10 of the most powerful sites in the world that are listed here.

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With the skyrocketing popularity of Android and iOS operating systems, software developers got a whole new arena opened up. Many of the programmers have progressed to concentrate solely onto Mobile Technology Development. This is mainly due to the high demand as well as numerous lucrative ideas left to explore in the Mobile App world.

Exponential growth of smartphone users

As per the survey by eMarketer, the number of smartphone users across the globe crossed 1 billion almost two years ago. The expected number of smartphone users by 2014 end is 1.75 billion.

With smartphones, iPads and Tablets getting more accessible and less expensive day by day, the development potential for mobile apps is truly vast. The under-penetration in emerging markets like India and China in Asia shows that there seems to still a lot of steam left in the mobile app development industry.