Programmers often tend to be sedentary people. Sitting in a chair and pressing keys, testing code, and planning out one logical step-wise strategy after another to get the computer to process data the way you want it to is just what life as a programmer is all about. But, is being too sedentary hindering a programmers max potential? In other words, will getting up, moving around, and getting the blood pumping make us better programmers? To answer this question more efficiently, we will need to consider the impact of exercise on various aspects of programming.

Alertness And Focus

It is no surprise that working up a sweat makes the mind wake up and become more alert. As the blood starts pumping, the body physically reacts in ways that helps the mind to better focus. And improving our focus might make us better programmers in the sense that we are more able to wrap our mind around a problem and deal with it more efficiently than if we feel sluggish and not so alert. However, improving one's focus with exercise can be augmented by taking such vitamins as B6, Coleen, and eating more saturated fats rather than so many sugars. Exercise alone may be a good start, but it is important to realize that the impact of exercise on overall focus can be enhanced when combined with other dietary practices. However, it never hurts to begin a day of programming with fifteen minutes of rigorous workout to give the mind a little extra push.

Increase In Intellect

Does exercise cause a programmer to become a smarter programmer? This is perhaps a trickier question. In some sense, it might seem as if exercise makes us more intelligent. But, this may be more because our focus is sharper than because of any increase in actual knowledge. For example, if you don't know how to program in Python, it is highly doubtful that exercising harder will all of a sudden transfer such insights directly to your brain. However, exercise might have another indirect impact on a programmer’s intellect that will help them to become a better programmer. The more a person exercises, the more stamina and energy they will tend to have, as compared to programmers who never exercise all that much. That additional energy and stamina might help a programmer to be able to push themselves to learn things more efficiently, simply because they aren't getting tired as much as they study new languages or coding techniques. If you have more energy and stamina throughout the day, you will likely be more productive as a programmer as well. Greater productivity can often make one program better simply because they actually push themselves to finish projects. Other programmers who do not exercise on a regular basis may simply lack the energy, stamina, and motivation to follow through and bring their programming projects to completion.

Memory

The ability to remember things and recall them quickly is key to being an efficient programmer. Getting up and getting real exercise may be central to making sure that one does not lose control of these cognitive abilities. According to the New York Times, article, Getting a Brain Boost Through Exercise, recent research studies on mice and humans have shown that, in both cases, exercise does in fact appear to promote better memory function as well as other cognitive factors like spacial sense. (1) Consequently, if a person intends to be a programmer for a long time and wants their mind to be able to remember things and recall them more easily, then exercise may need to become an essential part of such a programmer's daily routine.

As much as one might want to resist the need for exercise and be sedentary programmers, the simple fact is that exercise very well could improve our ability to program in numerous ways. More importantly, exercise is critical to improving and maintaining good health overall. Even if a person does not have much time to get up and move around during the day, there are exercises that one can do while sitting, which would be better to do than no exercise at all.

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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.

Sage wisdom states that there are two sides to every coin. This timeless wisdom will be borne out in spades with Windows 8/RT. Let's get into the dark side first.

If your users are veterans of Windows it is safe bet they are going to take one look at Windows 8 and scream blasphemy. Users whose brains are geared towards visual learning will undoubtedly yell the loudest and longest.

There's a good reason for this. Mick Jagger brought his band to the Redmond campus, performing live "Start Me Up" in the summer of 1995 (it was a great show). This heralded in the abandonment of program icons sitting on the desktop and introduced the now legacy Start button.

Ending the life of the 17-year-old start button is not going to go well with some users.

Controversy was recently courted as Southern California Edison (SCE) prepares to cut their own staff while looking to meet their staffing needs with offshore employees skilled in the field of “IT” or Informational Technology. This has been the second major utility company in the United States to take this path towards providing services to its consumers while holding current rates at consistent levels. SCE does not disclose the exact numbers of expected lay-offs, but the LA Times reports that it is in the hundreds.  Utility companies tell their consumers that these moves are necessary as a hedge against inflation and to keep their services at rates that their customers can easily afford. Critics claim that the use of foreign workers is the first step to using an entirely foreign workforce and promoting large scale unemployment amongst American citizens. Often this has been seen as a conflict between national and international workers for the same jobs, salaries and careers.

It has been noted that this State of California utility company, much like other corporations that hire foreign workers does so primarily when there is a shortage of national citizens that can perform these jobs well. IT workers that are brought in with H-1B Visa work permits usually are college educated and hold expertise in technical areas and studies that local employees may not be especially trained in. Once again, critics decry the fact that these employees are not hired directly. On shore contracting companies operating in the continental United States are directly hired by the utility companies. These contracted companies then serve as “middle-men” and hire a wide range of foreign workers with H-1B paperwork so that they can move to the United States. The workers then perform a variety of jobs instead of American workers who were either born in the country or have achieved American citizenship on their own.

Needless to say, the amount of visas issued in a given year is a concern for U.S workers in various fields but particularly in Information Technology. As large corporations stack the employment deck with foreign workers who put in the hours for a fraction of the pay-rate for local employees, local IT professionals are finding it more difficult to find work nationally.  They encounter rejections, endless interview processes or low –ball offers from companies and recruiting agencies looking to fill positions at a bare minimum cost for coveted skill-sets.  

Meanwhile, an H-1B worker is a worker brought in on a temporary basis with a visa allowing them to work freely in the United States. Much like a student or travel visa, it is issued for on a calendar oriented basis.  Applicants who successfully renew the visa for an extended period of time can expect to work in the United States for up to ten years.  Although U.S companies hiring these employees may pay them less than their local employees, the salaries earned by H-1B Visa workers are almost always higher than these workers would earn in their own country of origin.

Both sides can agree on several issues. When it comes to these H-1B Visa workers, their assignments are generally of a contractual nature and require them to reside in this country for a period of months to years. However it is also an accepted fact that while they are in this country, they are responsible for paying rent, utilities and all other living expenses. As residents of the United States on a permanent basis, they are also liable for taxes on any salary they have earned while living here.

Dr. Norman Matloff, a professor at the University of California, Davis and writer on political matters believes the shortage to be fiction. In his writing for the University of Michigan Journal of Law Reform, he claims that “there has been no shortage of qualified American citizens to fill American computer-related jobs, and that the data offered as evidence of American corporations needing H-1B visas to address labor shortages was erroneous. The American Immigration Lawyers Association (AILA) agrees with him and describes the situation as a crisis. Likewise, other studies from Duke, Alfred P. Sloan Foundation and Georgetown University have disputed that in some years, the number of foreign programmers and engineers imported outnumbered the number of jobs created by the industry