Technology is wonderful. It helps us run our businesses and connects us to the world. But when computer problems get in the way of getting what you need to get done, you can go from easygoing to mad-as-a-hornet in 3 seconds flat. Before you panic or give in to the temptation to throw your computer out the window, try these easy fixes.

5 Common Computer Problems

1. Sluggish PC

A sluggish PC often means low disk space caused by an accumulation of temporary Internet files, photos, music, and downloads. One of the easiest fixes for a slow PC is to clear your cache.

The way you’ll do this will depend on the Internet browser you use:

• Chrome– On the top right-hand side of the screen, you’ll see what looks like a window blind. Click on that. Click on ‘History’ and hit ‘Clear Browsing Data’.

• Safari– On the upper left-hand side, you’ll see a tab marked ‘Safari’. Click on that. Scroll down and hit ‘Empty Cache’.

• Internet Explorer– Click on ‘Tools’ and scroll down to ‘Internet Options’. Under ‘Browsing History’ click ‘Delete’. Delete files and cookies.

• FireFox – At the top of the window click ‘Tools’ then go to ‘Options’. Select the ‘Advanced’ panel and click on the ‘Network’ tab. Go to ‘Cached Web Content’ and hit ‘Clear Now’.

Much of success is about performance. It’s about what we do and what we are able to inspire others to do. There are some simple performance principles I have learned in my life, and I want to share them with you.  They really bring success, and what it takes to be successful, into sharp focus. They are also the basis for developing and maintaining an expectation of success.

The Five Principles of Performance

1. We generally get from ourselves and others what we expect. It is a huge fact that you will either live up or down to your own expectations. If you expect to lose, you will. If you expect to be average, you will be average. If you expect to feel bad, you probably will. If you expect to feel great, nothing will slow you down. And what is true for you is true for others. Your expectations for others will become what they deliver and achieve. As Gandhi said, “Be the change you wish to see in the world.”

2. The difference between good and excellent companies is training. The only thing worse than training employees and losing them is to not train them and keep them! A football team would not be very successful if they did not train, practice, and prepare for their opponents. When you think of training as practice and preparation, it makes you wonder how businesses survive that do not make significant training investments in their people.

Actually, companies that do not train their people and invest in their ability don’t last. They operate from a competitive disadvantage and are eventually gobbled up and defeated in the marketplace. If you want to improve and move from good to excellent, a good training strategy will be the key to success.

Unlike Java, Python does not have a string contains method.  Instead, use the in operator or the find method.  The in operator finds treats the string as a word list whereas the find method looks for substrings.  In the example shown below, 'is' is a substring of this but not a word by itself.  Therefore, find recoginizes 'is' in this while the in operator does not.

s = "This be a string"
if s.find("is") == -1:
    print "No 'is' here!"
else:
    print "Found 'is' in the string."
    
if "is" in s:
    print "No 'is' here!"
else:
    print "Found 'is' in the string."

#prints out the following:
Found 'is' in the string
No 'is' here!

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.