Peer-to-Peer Payment Apps: A Brief Overview

The growing influence of the web has reached to an extent where we can roam around cashless. No, I am not talking about going cashless with your ATM card in the wallet, which would take care of the needs. But, this is one of the scenarios where you can go cashless and cardless at the same time. Just with a swipe on your touch screen you can make payments or transfer funds or initiate a payment to the merchant or to anyone as you wish.

Yes, it is happening. Moreover, there are people who are worried about the safety and security of their payments, which prevents them from using this payment mechanism, where you can go cashless and cardless without any further worries of carrying money with you all the time.

One of the examples of this payment wallet could be best illustrated by the Paytm wallet that is one of the coolest payment gate way. Let us think about a situation where you are planning to hire a UBER cab for your next journey. It is late in the night and you need to get home from office. You are not carrying cash with you and it is a little bit difficult to find ATM’s nearby. But, you need to get home and you are going to hire an UBER. What do you do?

Your UBER app shows the payment options as: “CASH” and “Paytm.” Which one do you select? As we have already discussed you are in short of cash. Now the only option you are left with is the Paytm. You check on the Paytm option and within minutes your UBER is here. You enjoy one of the coolest ride home and your Paytm takes care of your payment. Here, there are quite a few advantages of not having to carry cash around.

You have been traveling alone at night with a stranger. We cannot prevent disasters such as theft which happens at the most unexpected time. But, with cashless and cardless payments we are ensuring that there is more security to our lives and assets than ever and also, we are saving ourselves the effort of always keeping money with us.

The payment happens instantly as you are dropped at your destination. You can go home and have a sound sleep. Also, think about your situation where you went out for a team dinner. And, all of you planned to split the bill amongst the team. Now you have the easiest way of payment with you. You can transfer it via the payment wallet and burp merrily after a delicious dinner at one of your favorite restaurants.

So, what exactly is this peer-to-peer payment app?

Let me not give you any complicated definitions, if you haven’t heard of it before, I wish to make it easier for you. This system acts or takes up the responsibility of a messenger or carrier, which would provide you with a bridge to transfer or initiate payments from your bank account to another individual or merchant’s account or card via a software app. Ain’t it easy?

These days we can observe that a number of youngsters are in awe of these apps that makes it easier for them to go around without the typical difficulty of carrying cash or card with them every time they wish to make a purchase or transfer back the money someone had lend them. But, there are still those who are worried about the safety, which you can take a chill pill, because it is safe and secure.

2016 Mobile Game App Statistics – Past Trends and Future Projections

Mobile gaming apps have been around since the late 1990s and have evolved into a robust revenue-generating powerhouse. They accounted for 37 percent of revenue for the games market globally in 2016. China generated the most revenue – accounting for 29.8 percent of mobile game installation worldwide. The North American region also made billions – producing approximately $6.9 billion USD in revenue in 2016. It was the second-largest mobile gaming region, just behind Asia, and much of that revenue can be attributed to the U.S.

Where is the money coming from?

Mobile apps for iOS devices offer high earning potential. The middle of 2016 saw some interesting figures when it came down to in-app purchases based on device. Most in-app purchases worldwide generated $7 USD per buyer monthly. However, users that made in-app purchases from iOS devices paid almost $11 USD on average.

Freemium purchases also generated revenue in 2016. Approximately 64 percent of mobile gamers using freemium apps made at least one mobile in-app purchases during February 2016.

Mobile games took over revenue share in 2016 for app store purchases. According to Statista, 90 percent of Google Play Store revenues were attributed to mobile gaming apps. Apple’s App Store also saw a similar large attribution of revenue thanks to mobile gaming as it accounted for 80 percent of its revenue.

How many people are playing and where are they playing?

There were 2.8 billion mobile gamers who were actively using games on their mobile devices every month. This figure increased by 100 million from 2015. The bathroom and workplace are mobile gaming playgrounds.Yes. It’s true. People play mobile games while “taking care of business.” Statista data revealed that 30 percent of U.S. mobile game leisure time occurred while gamers were on the toilet, while another 18 percent played at work.

The smartphone is the device of choice for many mobile gamers worldwide. In the Asia Pacific market alone, 71 percent of Internet users played a mobile game via a smartphone in 2016, according to Statista. This was closely followed by Latin America, Middle East and Asia markets at 68 percent. The North American and European markets saw smartphone mobile gaming activity of 52 percent and 50 percent respectively.

Revenue from mobile games is expected to increase in 2017.

2016’s revenue-generating statistics for mobile gaming demonstrates its increasing growth potential and opportunity for 2017 and beyond. As access to the Internet and smartphone devices continue to increase, mobile gaming apps will continue to scale and produce revenue-generating opportunities. Revenues are expected to grow to as much as $40.6 billion USD in 2017. This is an 18-percent increase from 2015.

The Past and Present of Machine Learning in Gaming

Machine learning represents a crucial area of artificial intelligence (AI). Over time, machine learning can teach a piece of software how to behave when it encounters certain types of environments or problems. While some scientists use machine learning to advance the future of AI, others have focused on exploring how machine learning can function within artificial worlds. In other words, these scientists introduce machine learning to video games to see if the AI can learn how to play.

Early Attempts at Machine Learning in Gaming
Computer scientists have been testing the abilities of machine learning in video games for over 60 years. In 1949, Claude Shannon published a research paper titled “Programming a Computer for Playing Chess.” Shannon’s paper estimated that chess has more than 10^120 possible positions. Even today’s supercomputers would find it impossible to solve chess problems with brute force instead of playing against their opponents.

By 1997, IBM had developed a computer called Deep Blue that managed to defeat world chess champion Garry Kasparov in a six-game match. This was the first time that AI had beaten a human in chess.

It took IBM several years to teach Deep Blue how to play chess well enough to compete against a master. Early versions of the AI couldn’t even beat basic chess-playing software run on a personal computer. Over time, though, Deep Blue learned how to use the rules of chess to its advantage. Like a human player, it discovered that it could predict and respond to its opponent’s moves. It may make some people uncomfortable to think about Deep Blue in this way, but the computer was thinking about how to beat its opponent. It wasn’t just following rules.

More recently, AI researchers introduced machine learning to “Super Smash Bros.,” a hand-to-hand fighting game that features some of Nintendo’s most popular characters. The researchers introduced four rules about the game’s goals, strategies, tactics, and chains of button presses to see how the AI would perform.

The AI excelled at beating opponents in “Super Smash Bros.” Even the world’s best players faced great difficulty competing against the AI. In fact, the AI performed so well that few human players managed to hit its game character once. (Readers can see videos of this at GitHub.)

Adding Evolutionary Genetics to AI

In 2015, Seth Bling revealed that he had created an AI called MarI/O that learned to beat a level of “Super Mario World” in just 34 tries. Bling gave the AI neural networks and algorithms so it could decide which attempts gave it the information it needed to move forward in the game. Other than that, Bling gave it nothing. MarI/O didn’t even know to press left to move left. It had to discover that on its own.

Bling’s project was revolutionary because it used aspects of evolutionary genetics. Bling wanted to see how the AI’s approach to playing would evolve. He found the “species” that worked best would dominate the AI’s progress. Eventually, the AI had taught itself everything that it needed to complete a level successfully.

Introducing AI to Games of Imperfect Information

Chess and “Super Smash Bros.” are considered games of perfect information because both players have full access to the rules and the board. Other than an opponent making a particularly inventive move, no one encounters surprises in chess. Other games of perfect information include backgammon, checkers, tic-tac-toe and Go.

Games of imperfect information operate in a more realistic way that can represent how the real world functions, or at least how the rules of a fictional world function. With games of imperfect information, players have to make quick decisions. Players also only have access to certain aspects of the game. For instance, Player A may not have the ability to see Player B’s board. This creates more uncertainty for machine learning to handle.

Current thinking suggests that exposing machine learning to games of imperfect information could lead to significant improvements in technologies like autonomous cars. Operating a car safely requires an understanding of real-world physics as well as an ability to respond to unexpected events.

Somewhat surprisingly, “Minecraft” may offer one of the best environments for AI to learn in. The “Minecraft” universe follows specific rules that are very similar to those created by Earth’s physics. Within the game, though, AI can make mistake after mistake without harming anything. If it made those same mistakes while driving a car, companies would end up spending a lot of cash on replacement cars and driving courses.

Machine learning has already shown humans how impressive artificial intelligence can become when given enough time to train itself. In the near future, researchers may use video games to teach AI how to perform all manner of tasks and recognize objects that exist in the world.

No one should expect AI to find the same solutions that humans developed, though. AI and humans do not experience environments in the same ways. This reality could cause problems for researchers who want to make AI a part of the real world.