Data Science Central recently published an article on “Can Big Data Algorithms Tell Better Stories than Humans.” We were intrigued to read the article as the general ideas that it presents are right in line with what he have worked on in the past, and continue to look towards in the future. In essence, the article presents the idea that it may be possible to teach a computer the process of structuring data into a narrative just like humans can do. Skeptics exist because they are fearful that technology will replace their job description. Algorithms generated by computers have the ability to change many things in our daily lives, not only the way we search.

We have already begun to see a shift by the major players in the computer and search industry in recognizing that artificial intelligence is on its way and employing a variety of measures to stay at the forefront of this emerging technology.

The mere mention of AI, or artificial intelligence brings up mixed emotions. On one side of the coin, it sounds cool and high-tech, but it also seems like it has had much hype over the last few years without many publicized breakthroughs. Sure we have IBM’s Watson, Siri and dare we say self-driving cars. But have any of those cutting edge artificial intelligence technologies changed life as we know it?

Machine learning and machine intelligence can be a frightening prospect to think about for some. For nerds like us we hold high hopes of the possibilities, but for others they picture machine-like robots creating chaos in the streets.

Is the real reason that AI hasn’t taken off in the past simply because as a society we weren’t ready for it? Or is that we had to build the systems that can now accommodate it. Processing speeds have greatly improved as well as the computational capacity necessary to support artificial intelligence.

Algorithms have the potential to transform nearly any type of business operation. Take for example a nuclear power plant. In this scenario the plant supplies power to a large number of customers, runs 24 hours a day seven days a week and has a high price tag associated with each hour that the plant is down for maintenance. If an algorithm can detect a problem before it actually happens then, it can save a great deal of money and manpower.

The news publishing industry has become fearful of algorithms and AI taking over and writing their stories for them. Maybe they have little cause for concern due to the recent announcement that Penn State Technology BBookX that can build textbooks by utilizing artificial intelligence and open source.

The resurgent interest that we have seen in artificial intelligence is here to stay in our humble opinion. AI-powered applications have unlimited possibilities and will more than likely become the newest disruptive technology.

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Much has been written about artificial intelligence and machine learning, yet there are still far too many who neither understand the difference nor comprehend the applications of these growing technologies. Some of this is to be expected, as the two fields are changing rapidly to meet the demands of application developers, system engineers, and business in general. Still, the initial academic inquiries into these two subjects established a body of knowledge that has formed the foundation for all the study that has taken place since.

Artificial Intelligence

Programming a computer to make decisions based on an arbitrary set of data is not artificial intelligence. Computers make “decisions” billions of times a second. The transistor is essentially a decision engine, it can be configured or controlled in a manner that simulates decision making.

Artificial Intelligence, or AI, on the other hand, is a system that poses questions. When a computer correctly recognizes the necessity of a question, that is the first step towards intelligence. The answer to the question is by definition academic at the point where the machine correctly recognizes the conditions that must give rise to it.

Ultimately, AI is far more an academic concept than it is a practical application of computer science. It exists when an arbitrary set of conditions are met, and those conditions can change based on the application at hand.

Machine Learning

When a machine is said to be “learning,” more often than not, it is refining either the set of data being fed to a standardized algorithm or it is refining an algorithm to derive better efficiency or more accurate results from a set of standardized data.

Machine Learning is a process that produces greater efficiency, greater speed or more accurate data. It is AI’s counterpart in most any construct or system designed to investigate a source of information. Artificial Intelligence and Machine Learning can be designed to work together depending on the kinds of problems they are being asked to solve. AI asks the questions, and machine learning produces the best possible answers. Properly utilized, the two processes can form a positive feedback loop, which would be considered an emergent property of an artificially intelligent machine.

Computer science, by and large, is far more concerned with the theoretical applications of microprocessor-based electronics than it is with the practical limits of the same technology. What is clear from the research, however, is that AI and Machine Learning are most likely to produce progress if they are properly understood and implemented.

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Technology is imperative in any society. However, when it is traveling at breakneck speed, people can easily break their necks when they try to keep up. The major downside to rapid technological progress is the rate at which technology becomes obsolete. Every company is trying to implement the next technology instead of improving its present technology. This concept is what IT experts call the ‘next practices.’

Taking system monitoring a notch higher

Most technologies have to adopt new assets and capabilities over time proactively. Sometimes, these new assets and other existing assets require active monitoring. In the case of overloads, internal and external attacks, or even false alerts can overwhelm any team.

Thanks to artificial intelligence and machine learning, one can overcome possible fatigue. Within the existent technological framework, one can teach the systems to analyze the threats further.

Such a system can sieve through the many alerts and identify the most critical ones. It can also monitor signals that lead to preventive measures. This capability is the next practice as it focuses on the problems that are already there.

Data-backed decision making

Business decisions are becoming sensitive by the day. An internal business decision can easily escalate within a short time if it falls in the wrong hands. In such an environment, businesses cannot afford to make the wrong step. Unfortunately, the number of interlinked variables continues to increase every day compounding the process.

In such an environment, companies must continually rely on data to draw insight. Allowing data to stay in silos is no longer possible if a company is to progress. Companies can use the Internet of Things (IoT) to interlink data collection, storage, and analysis. This capability will give the company control over its information.

Spot-on remediation

Many people are frowning upon the trial and error decision-making process. The room for it is thinning as customers, and business partners gain prominence in the market. If there is a problem, the option is to remediate immediately.

Thanks to machine learning, businesses can test and retest their processes before they can launch them. Businesses can mimic various settings until they arrive at the most beneficial situation.

Artificial intelligence and machine learning are no longer cliches for the future; they are the keys to the next practices in the IT industry.

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We may not have sentient robots roaming the streets, but AI technology has advanced well beyond what was believed possible. Artificial intelligence is now able to assist in the business place, analyzing data and forming effective business plans based on numbers and facts. As a matter of fact, it is being used in everyday life, and no one seems to take notice. It is even being utilized in the medical industry and in warfare.

Some companies are belittling AI, making it seem less important to modern tech than it is. Oral-B, the toothbrush company, is currently promoting its Genius X device. They are praising it for its AI abilities, but it isn’t real AI. It simply gives you feedback on brush time and variation. It is a clever use of sensors and tech; however, calling it artificial intelligence is quite the stretch.

Entertainment is also confusing people about what artificial intelligence actually is. People tend to think of film and television when they think of artificial intelligence. Some modern AI isn’t quite to the level of what we see in movies; however, its usefulness should not be under-appreciated.

Machine learning and deep learning are what is fueling the artificial intelligence movement at the moment. These terms deal with teaching machines to learn on their own. To break it down into simplest terms, previous forms of tech must be programmed to recognize an object. You tell a machine what certain objects are, and it will only know those objects for its entire up-time. Machine learning is when a machine is able to figure out what objects are without prior programming to specifically tell them.

Some naysayers believe that artificial intelligence has reached its peak. But machines are able to analyze data much faster than humans and relay that information to us in understandable ways. We may never get to see truly sentient machines, but the technology we have created will certainly change the world for the better.

The research isn’t going to stop anytime soon. There are still plenty of approaches to take. Many are still hopeful that artificial intelligence breakthroughs could revolutionize the way life is for humans. Benedict Evans, a VC strategist, believes that machine learning will be present in almost everything in the near future; however, no one may know and no one may care.

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The study of artificial intelligence is advancing far more quickly than most other areas of computing. AI is already much more common in business than most people suspect, and it is only going to get more common as the programs improve. AI even has the potential to take over customer service within a few years! That means that it is vital for everyone with interest in business to understand where the field is going so they can plan for the future.

Basic Customer Service

Many of the businesses that are making use of AI are using it in a customer service role. It is relatively easy to produce an AI that can recognize common questions and provide a programmed answer to them. Artificial Intelligence also excels at basic clerical work, such as making appointments, since it only needs to collect information from an individual and put it into a form. AI can even be programmed to contact a human employee if it can’t recognize an input from the user. That makes the power of AI an ideal choice for a basic customer service role. It is often expensive to hire enough human personnel to handle those jobs at a large business, which makes an AI solution very appealing to decision makers.

Predictive Techniques

Businesses already use AI to process data. AI tends to be better at it than humans because the technology can process a vast number of data points far more quickly than a human can read through them. Humans also struggle with the complexity of dealing with that many reports, but computers just need a little more processing power to get the job done.

It is likely that programmers will adapt those techniques and turn them into powerful predictive tools more and more. After all, computers are already fairly good at looking at data to figure out trends. Predicting the future is a matter of extending those trends into the future. It will not be a perfect system, but humans also have trouble getting things perfectly accurate too.

New Roles

Nearly every industry is investigating the potential of AI, and most of them are making progress. It is likely that AI systems will start to spread out more in the next few years. They will start in a supporting role for human workers, but they will take on more and more tasks as the programmers have a chance to observe them and tweak the programs.

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Unless you live under a rock, you have probably heard a great deal by now about virtual reality, augmented reality, artificial intelligence, and machine learning. Hearing about them is one thing, but understanding what they are, how they work, and what benefit they hold is an entirely different issue. Here is what you need to know about machine learning.


Machine learning is the ability of artificial intelligence to observe patterns and take correlative action. Machine learning is essentially the same as human learning but on a much more limited scale. The human brain can make quantum leaps, while machine learning cannot. For instance, if you have a smart thermostat and turn your thermostat down every evening at 9:00, eventually your learning thermostat will begin turning the heat down on its own at 9:00.

What it can’t do, however, is make corrections based on totally unrelated information. For instance, if you were to tell your spouse that you needed to work late that night, your spouse would most likely not turn the heat down at 9:00. If you were to say the same thing to your home hub, it would not make the same correlation. You would need to either tell it specifically to leave the heat on or override it manually.


Machine learning is a subset of artificial intelligence or one of the many functions of artificial intelligence. One of the primary uses for machine learning is to track patterns and look for irregularities. For instance, machine learning is already being used to monitor your patterns of spending to alert for potential fraud when there are any irregularities. One of the most important uses of machine learning may be in the area of digital security.

While human intelligence is in most ways vastly superior to artificial intelligence, AI does have one distinct advantage. AI is capable of absorbing a vastly larger data set in a fraction of the time it would take the human brain. For instance, if we wanted to analyze hundreds of hours of video footage to look for specific patterns, a human would have to watch it all minute by minute, one video at a time. A computer, however, can scan hundreds of hours of video in just a few minutes and detect any discernible patterns as well any irregularities to any noticeable patterns. While it is not foolproof, this technology can be a significant aid to law enforcement, banks, and other financial institution and a wide range of other businesses and governmental entities.

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In June of 1956, a small workshop of less than a dozen scientists and mathematicians met at Dartmouth College in Hanover, New Hampshire. The Dartmouth Summer Research Project on Artificial Intelligence not only created a new field of research, it founded what became a worldwide industry, today funded by governments and corporations to the tune of hundreds of billions of dollars.

The terms “artificial intelligence,” “machine learning,” and “deep learning” have become technology and fiction buzzwords, frequently appearing in science news, advertising, movies, and science fiction stories. What exactly is artificial intelligence, and how do the fields of machine learning and deep learning relate to it?

Artificial Intelligence

Artificial Intelligence or AI is a broad-based, interdisciplinary field of study pursuing machines that are capable of performing tasks which require human intelligence to complete. Machine learning and deep learning are just two of the ways to equip machines with the knowledge necessary to act as if intelligent, in the mold of human thought and behavior.

Machine Learning

More specifically, machine learning is the term for teaching a computer to perform a task, instead of programming a series of precisely ordered steps for the machine to follow. There are two main methods. Supervised learning labels types of data for the computer to use as examples. Unsupervised learning has the computer sort data into similar types, then spot detailed differences. This is how machines are taught tasks like facial recognition or predicting stock market trends.

Deep Learning

Deep learning is the term for a specific type of machine learning that allows computers to understand complex problems and provide insight, solutions, or controls for those problems. This process involves the use of neural networks, which are groups of separate computer programs that perform specific computations, then output the results to another component of the network.

The term “deep” is a reference to the way neural networks can be layered to receive and transmit results to each other; exponentially multiplying the speed and complexity of machine learning. Deep learning produces results like following speech commands or recognizing the necessary cues to drive a car. This concept is what has allowed amazingly human-like feats of deduction and strategy accomplished by some computers, such as defeating chess grandmasters or predicting weather patterns.

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Machine learning and artificial intelligence are two of the most dominating skills in industrial, manufacturing, and factory workplaces. Building a career in machine learning and artificial intelligence requires due diligence, patience, and discipline. If this is a field you’re interested in pursuing, you have to pay close attention to particular areas associated with programming, information technology, and precision mathematics.

Among the key prerequisites for someone aspiring to become an expert in artificial intelligence and machine learning includes statistics and probability, applied mathematics, proficiency in programming languages, including Python, Java, R, C++, algorithms, and coding, as well as distributed computing. Basic and advanced skills in these areas give you the necessary skills to ensure you can not only build your own artificial intelligence system but also understand the existing ones.

To enhance your competency, you may need to use a hands-on approach as far as these fields are concerned. You can approach this by exercising the vital traditional IT skills you acquire and using the existing IT software as well as mathematical and statistical software to acquire a few basic hands-on skills as to how machine learning and artificial intelligence work.

After the schooling process comes the entry to the job market stage, this is the most critical stage as the ideas and skills you gained can be utilized to give yourself a niche which is crucial in determining how successful you will be. The jobs market in the machine learning and artificial intelligence sector is very competitive and requires you to be proficient in the latest machine learning and artificial intelligence tools. This is a highly dynamic field and requires you to be in the constant lookout for new trends to help sharpen your competencies.

Most employers in the machine learning sector normally require specialists capable of meeting special organizational needs. You should, therefore, target concentrating your skills on a particular field to ensure that you have the edge over the stiff competition available. In the coming years, this dynamic field is expected to create thousands of jobs even as organizations re-adjust themselves and create a suitable portfolio of machine learning and artificial intelligence specialists that they need. There is, therefore, need for constant organizational and individual research and development while focusing on stretching as much as possible the existing technologies and applying them for problem-solving.

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