Technology has continued to evolve in ways that few would have been able to imagine. This has allowed electronics to become smarter, more connected and far more useful.

With the Internet of Things (IoT), they're allowing more than just computers to become connected to the Internet. This aims to make the life of the average person easier, better and more care-free.

Let's examine why the Internet of Things has become such a powerful idea that an estimated one out of every five developers currently works on an IoT project.


What is the Internet of Things?

The Internet of Things hinges on one seemingly simple concept: electronics can be embedded in machines, clothing, animals and even people to provide a networked world where the whole is more than just the sum of its parts.

For example, consider how the Internet of Things can influence things like refrigerators. They can be networked directly to the manufacturer for readings that can warn if the refrigerator is about to malfunction. They can even be connected to a grocery shopping service to allow someone to restock them automatically or to notify the owner that the refrigerator is almost out of an item.

The most interesting notion about the Internet of Things is that it's not just a situation where one “thing” connects with a party. They typically communicate with other things, which in turn allows for a network of automated processes to occur.

These processes can simplify and expedite tedious tasks to make everyday life for everyone easier, which is why projects involving the Internet of Things are so popular.


How Prevalent is IoT Development?

An estimated one in five developers are currently developing projects for the Internet of Things. Their chosen languages vary widely because of the flexibility that IoT enjoys.

For example, IoT projects that hinge on interacting with mobile phones tend to have apps written in JavaScript or Java. The back-end code that runs the IoT functionality for machines tends to be written in Assembly, C++,Java,Perl,Pythonor another compiled language for efficiency.

To put the growth of IoT work into perspective, Evans Data Corp. performed research to create predictions about IoT projects in 2014. They stated that 17% of companies would be developing IoT projects.

In this year, that figure's risen to a solid 19%. Given the fact that 44% of developers have stated that they will enter into the IoT scene this year or next, this means that development will only grow in the coming future.


The Future Involving the Internet of Things

Development of IoT-related projects will likely explode in the next few years. The advantages it brings, such as more efficient work in manufacturing environments and the projected 15% savings to the restaurant industry over the next five years, will make it one of the most valuable technological changes in the near future.

Without a comprehensive understanding of the Internet of Things and the skills to lead IoT projects, businesses and developers may find themselves falling behind. Don't let the Internet of Things pass you by.

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.

There are normally two sides to the story when it comes to employment. On one hand, employers hold the view that the right candidate is a hard find; while on the other, job hunters think that it’s a tasking affair to land a decent job out there.

Regardless of which side of the divide you lay, landing good work or workers is a tedious endeavor. For those looking to hire, a single job opening could attract hundreds or thousands of applicants. Sifting through the lot in hope of finding the right fit is no doubt time consuming. Conversely, a job seeker may hold the opinion that he or she is submitting resumes into the big black hole of the Internet, never really anticipating a response, but nevertheless sending them out rather than sit back doing nothing.

A recruitment agency normally keeps an internal database of applicants and resumes for current and future opportunities. They first do a database search to try and identify qualified and screened candidates from their existing crop of talent. Most often the case, they’ll also post open positions online through industry websites and job boards so as to net other possible applicants.

When it comes to IT staffing needs, HR managers even find a more challenging process in their hands. This is because the IT department is one of the most sensitive in any given organization where a single slip-up could be disastrous for the company (think data security, think finances when the IT guys are working in tandem with accounts). You get the picture, right?

Smart Project Management –Best Practices of Good Managers

Project management could be one of the easiest jobs on the planet, and could also be the worst nightmare. The difference between the two extremes depends on smart management of a project. According to the project management institute, there are five phases in project management - Initiating, Planning, Executing, Monitoring & Controlling, and Closing.

Every manager has his own style of project management. But there are a lot of contributing factors that result in a successfully managed project. These factors vary from project to project, but they all contain some common elements.

1. Setting SMART Goals