Have you heard the term “machine learning” and wondered exactly what that entails? Machine learning essentially gives computers the ability to “learn.” Arthur Samuel coined the term in 1959 and it has been growing and changing ever since. Let’s explore what exactly machine learning encompasses.

Our ability to learn and get better at tasks through experience is part of being human. When we were born we knew almost nothing and could do almost nothing for ourselves. But soon we are learning and becoming more capable by the day. Did you know that machines can do the same?

Machine learning brings together computer science and statistics to enable computers to do a given task without being told to do so. Say you need a computer that can tell the difference between a dog and a cat. You can begin by giving it pictures of both animals, and telling it which is which. A computer programed to learn will seek statistical patterning within the data that will enable it to recognize a cat or a dog in the future.

It may figure out that dogs tend to be larger, or that cats have small noses. It will then represent that numerically, organizing it in space. Crucially, it is the computer and not the programmer identifying and deciding those patterns and establishing the algorithm by which future data will be sorted. The more data the computer receives, the more finely tuned the algorithm becomes and the more accurate it becomes.

Machine learning is already widely applied. It’s the technology behind facial recognition, credit card fraud detection, text and speech recognition, spam filters on your inbox, online shopping recommendations, and so much more. At the University of Oxford, machine learning researchers are combining statistics and computer science to build algorithms that can solve complex problems more efficiently while using less computing power. From medical diagnosis to social media, the potential of machine learning to transform our world is truly mind blowing.

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Machine learning can seem like an abstract concept that is too difficult to wrap our human brains around. Part of that feeling is based on misconceptions surrounding the concept of machine learning. What are some common misconceptions about machine learning?

The models computers learn are incomprehensible to humans

One of the most common misconceptions surrounding machine learning is that humans cannot comprehend what the computer is learning. In reality, while some models are indeed complex and difficult for humans to understand, most are not. Don’t immediately assume that you cannot understand the same exact way that the computer or machine can.

It’s all About the Right Algorithm

Many people believe that machine learning is simply coming up with the correct algorithm in order to solve a problem or identify a pattern. This could not be further from the truth. Machine learning is much more based in the data than an algorithm. As the CTO of Sift Science Fred Sadaghiani states, “data is orders of magnitude more important than the algorithm you use or any technique that you’re applying.” When we refer to data, that means both the amount and the quality of data. The more quality information that the system receives, the better the results will ultimately be.

Machine learning is absent of human bias

It is almost impossible to completely eliminate human bias from machine learning.

Quality data is crucial to machine learning; data filled with human bias can greatly impact machine learning applications. One of the best examples of machine learning being absent of human bias can be found in Microsoft’s bot named Tay, released in early 2016. The goal of creating Tay was to determine if the bot would be able to learn from interactions with social media users on certain platforms like Twitter. Within 24 hours, users had taught Tay to be both offensive and racist. Microsoft immediately pulled Tay from the market.

While machine learning can seem abstract and complicated, it is easier to understand once you debunk some of the common misconceptions surrounding it. For example, the models are not incomprehensible to humans. Machine learning it is not only about the algorithm and lastly, machine learning is can be biased by humans, as evidenced by Tay the bot.

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With over 75 percent of business investing in Big Data, machine learning and artificial intelligence are set to take off in the coming years. But, is this true or all just hype?

More and more companies are investing their IT budgets towards machine learning and artificial intelligence capabilities and it’s clear why, as these technologies are taking off in massive proportions. Below are just a few of the many samples that we have seen of recent:

The Hype of the Self-Driving Car

The self-driving car seems to be the most heavily hyped application of machine learning and artificial intelligence, but is it giving the industries a bad name? These critical technologies may just be the way of the future, but they also have a lot of hype surrounding them.

Online Recommendations

Think Netflix or Amazon, these machine learning applications appear through online recommendations in our daily lives.

Fraud Detection

Due to the expansion of payment channels fraud is on the rise, and fraud detection services are in high demand within the banking and commerce industry. The use of machine learning allows automated fraud screening and detection as machines can process large data sets much faster than humans.

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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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In business, it is critical to have a system of measuring key figures that drive your company’s growth. There is a myriad of ways to measure insights, but there are three main types of analytics: descriptive, predictive, and prescriptive. Let’s address the key differences and similarities between descriptive, predictive and prescriptive analytics.

Descriptive Analytics

Descriptive analytics is becoming more and more prevalent in today’s society. Most people are familiar with platforms like Google Analytics and Facebook. These are all types of descriptive analytics. This type of data shows what has happened in the past. For example, when using Google Analytics, you will set a date range such as the past ninety days. From there you can see data and analytics on everything from how many views your website has received, how many people have engaged with you and even the length of time spent on the site.

Predictive Analytics

One of the most common examples of predictive analytics in practice is your credit score. The score you receive is formed using predictive analytics. It takes your history of credit and uses and predicts how you will behave financially in the upcoming years. For instance, if you have been late on your car payment ten out of twelve months in a year, predictive analytics will predict that the same will happen next year, and your credit score will be reflective of that.

Prescriptive Analytics

Prescriptive analytics takes predictive analytics one step further. Prescriptive analytics uses past behaviors from customers to predict their future actions. IBM coined the phrase “prescriptive analytics” in 2010 and since then it has been used by a myriad of other companies as artificial intelligence continues to grow.

What is the difference between predictive and prescriptive analytics? While predictive analytics makes an educated guess at an end result, prescriptive analytics actually processes the descriptive numbers and presents a recommended set of options.

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Machine learning is one of the most impactful technologies to ever grace mankind. An increasing amount of funding goes into research and development spending. The goal is to unlock the deep learning aspects of artificial intelligence. One of the primary challenges faced by scientists is trying to manage an inordinate amount of disorganized data in a timely manner.

Data scientists are hired by corporations to find insights within industry data. However, their analysis is hindered by the inefficient way in which data is organized. Rather than spending most of their time extrapolating useful information to guide a company’s agenda, data scientists spend about 80% of their time “cleaning” data sets.

It is this inefficiency that many in the business world overlook. Yes, the companies hiring data scientists know about data inefficiencies, but they have continually failed to account for it appropriately. Instead of focusing on the novelty of machine learning and hiring data skill sets, businesses who wish to maximize this area need to reassess their perspective. They need to recognize machine learning as a service and not just a hire-able skill.

Machine learning as a service means stabilizing infrastructures. It means realizing that extracting information from a data set will need to be uniquely applied. And finally, it means maximizing the insight time of data scientists. This last aspect is undoubtedly the most important. After all, what is good data without the right interpretation? Ultimately, the type of insights that data scientists need to make must follow stable infrastructures and organizational perspective.

In the current business environment, data scientists are overwhelmed by inefficient processes. Machine learning solutions need to recognize that data scientist training comprises more than algorithms and coding skills. For a company to improve its efficiency and scalability, it must support the many other components that enable data scientists to produce their end result insights. Unfortunately, there’s no streamlined solution for this process.

Every business scenario is unique. A corporation cannot expect to employ a cookie-cutter scenario when it comes to discovering insights using machine learning. Once the right qualified data scientist is hired, a company will need to support their efforts with the appropriate tools. Turning to a suitable technology partner for machine learning tools is often the missing ingredient of inefficient machine learning enterprises.

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To date, advancements in Artificial Intelligence (AI) have enabled AI-based programs to achieve super-human performance in a variety of games. For instance, IBM’s Watson trounced human competitors on Jeopardy!, and in the areas of chess, go, and pong, experts now consider it impossible for any human ever to beat any of these expert AI players. While these advancements are impressive, the AI robot uprising has not occurred—yet. However, it is coming. From cancer diagnostics to driverless automobiles, AI is advancing across multiple industries. Following are some trends to watch for 2019.

GPU Technology

New graphics technology is not imbued with artificial intelligence; instead, the new generation of graphic processing units (GPUs) are optimized to better handle the heavy loads of data crunching required by AI. Due to the heavy demands on central processing units (CPUs) and the inability for most CPU-powered computers to adequately meet the graphics needs of most games, independent graphics cards were developed. It soon became clear that graphics card technology could be applied to other data-intensive applications, and to date, GPUs have allowed unprecedented advancements for gaming, animation, and CAD applications. Such companies as Nvidia and Intel are now designing GPUs made specifically to power artificial intelligence. These GPU chips will power AI in the healthcare, automobile, and natural-language processing industries.

ONNX Framework Standardization

Currently, AI trained and developed on one type of platform cannot easily be ported, used, and developed on another platform. Consequently, AI applications are isolated in their own specialties. This limitation, however, is about to change.

Facebook and Microsoft, for instance, have teamed to develop a standardized framework called Open Neural Network Exchange that allows AI systems to be developed then used on various, different systems. This network exchange will enable developers to have a much more effective pipeline to develop and distribute AI applications.

Dueling AI

The most significant advancement in AI technology does not involve applications that make use of it. Instead, the advancement to watch for 2019 is the technology that increases an AI’s intelligence. The newest technology responsible for making AI smart is called dueling AI.

Dueling AI, otherwise known as general adversarial networks (GANs), involves one AI solving a problem and another AI attempting to find flaws in it. The initial AI then creates another improved version, and this version is then instantly examined for flaws. This process continues until neither AI can find flaws in the solution.

In general, dueling AI technology has proven successful in real-world generation applications. Specifically, it is used in image generation. For instance, dueling it out with another AI, one AI has created a painting that the other AI cannot distinguish from a human-made painting. Additional AI applications dueling their way to perfection include AI poets and musicians.

Final food for thought might be this–current AI technology has already created super-human game masters. Considering new GPU hardware technology along with broader network accessibility combined with AI-driven technology that makes AI more intelligent—the moment AI becomes more intelligent than humans might not happen in 2019, but the 2019 trends are contributing to that moment, which might not actually be that far away.

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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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