When it comes to running a start up, leaders need to make sure that their key players are motivated. This has been seen with many companies. Back in the 1970's it was found with the inspiration and diligence of the late Daniel Nigro when he formed Kleer-Fax. More recently it was seen in David Khasidy, the founder and recently retired president of SunRay Power Management, the most dynamic green energy leader in the US today.

The question is, what is it that great leaders like David Khasidy and Daniel Nigro do that make the difference? How do the most vulnerable companies (start ups) break the mold and become a part of our everyday lives?

It starts with their mission and vision.

Create a Strong Mission and Vision

There are many reasons why start ups fail. For one, they usually lack the capital to last through the lean times. Secondly, they often don't have the tolerance for setbacks that occur. Lastly, they do not have a long-term plan, also called a mission.

When a business has a strong mission, the team knows it and their focus toward their work and service to others within and without the company reflects that. To complement that, the shorter term vision of the company needs to be present as well.

This can even be seen in sole proprietorships with no employees, such as when Brian Pascale started his law practice. His vision was to find justice for his clients while his mission was to build upon a career that had already set precedents in the area of tort law.

As his practice has grown, new staff members can sense the vision and mission he exudes.

Encourage Ownership of Projects and Processes

Start ups need to inspire and motivate their employees because they need to know that they are not only a part of something important, but that their contributions mean something.

What won't happen if they are not there? What contribution do they make, and what are the consequences of them not fulfilling their part of the work?

By encouraging ownership in projects, team members can find that the work they are doing is not only important for the organization, but that they are going to be a big part of what makes it happen. The alternative is that they feel replaceable.

Offer Incentives That Keep the Company Competitive

When team members embrace the mission and vision of the company, and then take ownership for the company's success, they are going to need to be justly rewarded.

This could include flexible schedules (for those who don't need a stringent one), use of an account at a nearby takeout place, or even the potential for ownership as a result of a vesting program.

The incentive everyone is looking for more immediately, though, is cash. When the company takes in more revenue as a result of the efforts of those on the team, rewarding them can go a long way not only in making them feel appreciated, but in encouraging them to bring in more business.

Members of a start up team are usually very talented, and commonly underpaid. However, if they believe they are going somewhere, it will make a big difference.

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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.

Python and Ruby, each with roots going back into the 1990s, are two of the most popular interpreted programming languages today. Ruby is most widely known as the language in which the ubiquitous Ruby on Rails web application framework is written, but it also has legions of fans that use it for things that have nothing to do with the web. Python is a big hit in the numerical and scientific computing communities at the present time, rapidly displacing such longtime stalwarts as R when it comes to these applications. It too, however, is also put to a myriad of other uses, and the two languages probably vie for the title when it comes to how flexible their users find them.

A Matter of Personality...

That isn't to say that there aren't some major, immediately noticeable, differences between the two programming tongues. Ruby is famous for its flexibility and eagerness to please; it is seen by many as a cleaned-up continuation of Perl's "Do What I Mean" philosophy, whereby the interpreter does its best to figure out the meaning of evening non-canonical syntactic constructs. In fact, the language's creator, Yukihiro Matsumoto, chose his brainchild's name in homage to that earlier language's gemstone-inspired moniker.

Python, on the other hand, takes a very different tact. In a famous Python Enhancement Proposal called "The Zen of Python," longtime Pythonista Tim Peters declared it to be preferable that there should only be a single obvious way to do anything. Python enthusiasts and programmers, then, generally prize unanimity of style over syntactic flexibility compared to those who choose Ruby, and this shows in the code they create. Even Python's whitespace-sensitive parsing has a feel of lending clarity through syntactical enforcement that is very much at odds with the much fuzzier style of typical Ruby code.

For example, Python's much-admired list comprehension feature serves as the most obvious way to build up certain kinds of lists according to initial conditions:

a = [x**3 for x in range(10,20)]
b = [y for y in a if y % 2 == 0]

first builds up a list of the cubes of all of the numbers between 10 and 19 (yes, 19), assigning the result to 'a'. A second list of those elements in 'a' which are even is then stored in 'b'. One natural way to do this in Ruby is probably:

a = (10..19).map {|x| x ** 3}
b = a.select {|y| y.even?}

but there are a number of obvious alternatives, such as:

a = (10..19).collect do |x|
x ** 3
end

b = a.find_all do |y|
y % 2 == 0
end

It tends to be a little easier to come up with equally viable, but syntactically distinct, solutions in Ruby compared to Python, even for relatively simple tasks like the above. That is not to say that Ruby is a messy language, either; it is merely that it is somewhat freer and more forgiving than Python is, and many consider Python's relative purity in this regard a real advantage when it comes to writing clear, easily understandable code.

And Somewhat One of Performance

Getting involved with the IT, or Internet Technology industry, is a way for you to break into a variety of potential coveted career paths and job openings. Whether you prefer working with the back-end of programming or if you enjoy improve user experience while browsing online, there are many different in-demand IT skills that are useful to obtain today if you are seeking a career in the tech industry yourself.

Cloud Computing

Working with cloud computing, otherwise known as "the cloud", requires you to work within various types of servers that store and access data globally from any location. With the increase in mobile usage, cloud computing is becoming even more prevalent in today's society. When you want to work with cloud computing, understanding the basics of programming and network security is a must. Working in cloud computing is a way to help with building new applications, expanding companies online as well as allowing anyone internationally to locate and access a specific blog, website or mobile app.

UX Design

UX Design is also known as user experience design. A user experience designer specializes in understanding the usability and overall experience a web visitor has when browsing on a site or blog. UX design is essential to ensure that all visitors on a website are capable of navigating the blog properly and accessing the site's content with ease, regardless of the browser they are using or the type of device that is being used to access the site itself. Cross-browser compatibility and ensuring that all websites you are working with are accessible via mobile platforms is another responsibility of many UX designers today. Working in UX design is highly recommended if you believe you have an eye for "good" web design and if you have an interest in improving the overall experience web users for a specific audience have when visiting the blog or website you represent or that you are building for yourself.

IT Security

IT security is one of the fastest-growing positions throughout the entire IT industry and field. IT security requires you to understand network infrastructures as well as how to properly manage each server individually to provide security and protection from potential hackers and online thieves looking to steal sensitive data and information. Maintaining the security of a network and all servers for a company is only becoming more popular with the expansion of mobile phone usage along with the growth of the Internet altogether.

Understanding the variety of IT skills that are in demand today can help you to better decide on a path that is right for you. The more you understand about various IT skills, the easier it is to find a position or career in your future that is most suitable for the type of work you enjoy. Whether you are looking to develop new apps or if you are interested in managing the security of company servers, there are hundreds of positions and skills that are in demand in the IT industry today.

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