As someone who works in many facets of the music industry, I used to seethe with a mixture of anger and jealousy when I would hear people in more “traditional” goods-based industries argue in favor of music content-based piracy. They made all the classic talking points, like “I wouldn’t spend money on this artist normally, and maybe if I like it I’ll spend money on them when they come to town” (which never happened), or “artists are rich and I’m poor, they don’t need my money” (rarely the case), or the worst, “if it were fairly priced and worth paying for, I’d buy it” (not true).  I always wondered if they’d have the same attitude if 63% of the things acquired by customers in their industries weren’t actually paid for, as was conservatively estimated as the case for the music industry in 2009 (other estimations put the figure of pirated music at 95%). Well, we may soon see the answer to curiosities like that. Though one can say with tentative confidence that music piracy is on the decline thanks to services like Spotify and Rdio, it could be looming on the horizon for the entire global, physical supply chain. Yes, I’m talking about 3d printers.

Before I get into the heart of this article, let me take a moment to make one thing clear: I think these machines are incredible. It’s damn near inspiring to think of even a few of their potentially world-changing applications: affordable, perfectly fit prosthetic limbs for wounded servicemen and women; the ability to create a piece of machinery on the spot instead of having to wait for a spare to arrive in the mail, or en route if your car or ship breaks down in a far away place; a company based out of Austin, TX even made a fully functioning firearm from a 3d printer a few months ago.

If these machines become as consumer-friendly and idiot-proof as possible (like computers), it’s possible that in a matter of decades (maybe less), a majority of U.S. households will have their own 3d printer. There’s also the possibility they could take the tech-hobbyist path, one that is much less appealing to the masses. Dale Dougherty of Makezine.com estimates there are currently around 100,000 “personal” 3d printers, or those not owned for business or educational purposes. I don’t think they’ll ever be as ubiquitous as computers, but there are plenty of mechanically inclined, crafty hobbyists out there who would love to play around with a 3d printer if it was affordable enough.

That being said, is there reason to worry about the economic implications of consumers making what they want, essentially for free, instead of paying someone else to produce it? Or will the printers instead be used for unique items more so than replicating and ripping off other companies’ merchandise in mass amounts? The number of people working in industries that would be affected by a development like this is far greater than the number of people who work in content-based industries, so any downturn would probably have a much larger economic implications. Certainly, those times are a ways off, but a little foresightedness never hurt anyone!

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.

IT Outsourcing came to foray as a means for corporations to focus on critical business operations while having a specialized IT company take over the responsibility of managing the IT infrastructure and application development. For corporations especially in the developed countries, IT outsourcing provided two fold advantages, one was access to a highly talented pool of engineers and that too at a lower cost since countries like India were quickly growing their stature as an IT outsourcing hub.

IT Outsourcing is now as mainstream as ever and almost every leading organization outsources some or all parts of its IT infrastructure to a specialized company. It makes pure business sense and with tightening budget controls, IT outsourcing has become one of the strategic cost reduction driver for most organizations. Moreover, IT outsourcing is no more restricted to companies in USA & Europe outsourcing their IT projects to countries like India. Domestic companies within India itself actively use IT outsourcing including the Indian government like the India Post project given to TCS.

Is it then a no brainer that IT Outsourcing is critical to your business? Well, if it is cheaper and does not seem to have any inherent disadvantages why not!! Not really, IT Outsourcing despite proven benefits has its limitations and you should be cognizant be of the same before considering outsourcing your IT operations.

·         Limitations in estimating the actual cost of IT outsourcing:Let’s tackle the biggest driver of IT Outsourcing-Cost Savings. For anyone to estimate the cost savings from IT Outsourcing one needs to be able to predict the cost of outsourcing which then helps understand the cost savings from the same. Yes, at a higher level it is a matter of a simple $ per man-hour costs and IT outsourcing will appear to be cheaper in almost all cases. However, “hidden costs” are commonplace with IT outsourcing and it can be immensely difficult to accurately predict these hidden costs. For example, you need to be able to identify the costs of transitioning your in-house IT to an outsourcer, management overhead needed to manage the outsourcing relationship etc. In addition, IT outsourcing contracts are fixed at the start of the contract and as a result any additional requirement/change tends to be charged additionally. It is no surprise that IT requirements can change frequently and if your outsourcing contract doesn’t account for flexibility, you might be limited in the actual cost savings you might make. There is no surprise that there have been so many instances of IT outsourcing projects overshooting their budgets by a huge margin such as the one government shared services project going 500 million pounds over budget

JAVA SCRIPT TUTORIAL – THE ESSENTIAL ELEMENTS

If you are looking to increase your proficiency in programming, it can make a lot of sense to invest some time into learning how to use JavaScript, or taking a Java Script tutorial.  It is one of the most popular and powerful options available today for people to use in programming different parts of their websites.  It often finds use in headers, or in interactive features displayed on pages.  It allows you to execute many different functions, such as calculation, pulling data from forms, special graphical effects, customized selections, custom security protocol and password systems, and much more.  Here are some essential points to keep in mind:

·         Java vs. JavaScript – These two languages are not the same.  Java uses completely separate files for their headers and classes, and they need compilation prior to execution.  Java is used in the creation of applets for pages.  JavaScript is much easier and simpler to learn than regular Java, and Java Script tutorials are often significantly more accessible for the average user.

·         OOP – OOP, or object oriented programming, is a specific programming technique that simplifies complicated computer programming conceptual issues.  Essentially, it lets a programmer treat whole chunks of data (defined either by users, or by the system itself), and modify or access them in specific ways.  It does this by classifying different parts of the programming into Objects, Methods, and Properties, which will be discussed more in depth in the future, in other Java Script Tutorials.