Passbook is one of the newest features of the iPhone. What many people were offput by standards today is that we rely too much on disposable tickets, and the like, such as when you go to the airport for a boarding pass, or a movie ticket. Apple strove to solve that with a new application that is bundled with the newest update: Passbook.

Passbook offers everything you need, especially when a ticket, coupon, or gift card is needed. When you first open the application you are greeted by a sampling of applications that support Passbook, and its features. To begin, install the particular application you want to use. For example, we will be going with the Starbucks application to satiate our coffee needs every morning, as we have a gift card to spend.

Open up the freshly downloaded application, and get ready to be blown away. Like most applications, especially when it has to do with airlines, or coffee, you are almost always required to sign up for a respective account. Don’t worry, it is easy. All you will be asked is to fill out your email, a password, and other miscellaneous information to identify you when you lose your password or others.

Once you have finished signing up, you’re ready to roll. It will prompt you to enter a gift card that you already own, or begin a digital gift card. If you don’t have a gift card, you can start a digital gift card (or, as many may call it: a prepaid card), and pay for your coffee in a fast, and efficient way. Just tap on “Tap to Pay” when you are ready to pay, and tap again after you have paid. It’s really that easy.

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.

Recently, the new iOS update had added Reminders to the iPhone. If you ever found yourself setting notes on your iPhone to remember to do things, such as buying milk while at the grocery store, this process has become leagues upon leagues simpler, and faster. On your iPhone is an application named “Reminders”. Tap on this application and experience the new world of To-Do lists.

Right away, you are greeted by a screen that looks similar to a notepad, where you would be scribbling down reminders for this, and for that. To start off, tap on the plus button, and you are able to input the reminder you want. Say you want to be reminded to “Buy Milk.” Just type that into the application and you’re good to go.

But wait, there’s more. What this new application brings to the table that is extremely useful is the fact that your iPhone can remind you to do that task at a certain location, which, in this case, is buying milk. If you had saved your regular grocery store in your Maps application as a favorite location, you are able to do so. (To save a favorite location, go into your Maps application, search for your nearest grocery store that you regularly shop at, tap on the pin, tap on the blue arrow to get more information, and “Add to Bookmarks.”) In order to remind you to buy milk at your favorite grocery store, slide the “Off” to “On” and you are now able to set where you would like to be reminded at, and at what point in time. Now, you will never leave the grocery store without buying milk!

It is rather unfortunate that in the ever changing and rapidly improving world of technology, we hardly remember the geniuses who through their inventions laid the foundation for many of the conveniences and features we now enjoy in our favorite communication devices.

This article is a tribute to the ten people who made these discoveries and an attempt to bring their achievements into the limelight.

1.      Marty Cooper

Did you know that Cooper was the first to file the patent in 1973, when he was already working for Motorola for the “radio telephone system”. The Cooper’s Law is his brainchild and to think that he himself was inspired to come out with the patent was Star Trek and its Captain Kirk is indeed revealing.

2. Mike Lazardidis