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.

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.

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.

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.

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.

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.

WHAT IS IT?

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.

HOW CAN IT BE USED?

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.

Machine learning is gradually creeping into the marketing sector where it is influencing businesses to gain an edge in marketing in a multitude of ways. The following are the four largest areas in marketing that have been transformed by machine learning:

1. Enhanced Targeted Marketing

The use of machine learning greatly enhances digital marketers’ bid to increase precision in marketing. Computer programs can accurately analyze consumer behavior in various ways, thereby making it possible for marketers to know which content or product to recommend to customers. Intelligent machine learning programs can analyze a user’s browsing history and accurately come up with advertisements of interest to the potential customer. Machine learning, in this regard, takes into consideration users’ location, age, and gender among other customer-specific attributes.

2. Enhancing Communication in Marketing

Business-to-customer communication is facilitated in various ways in the electronic age, including the use of emails, push notifications, and phone-based text messages among other channels. To enhance precision, businesses must know the precise channel of communication that will enhance successful conversion rates of customers. Machine learning and machine-aided marketing can determine which customers often use mobile phones to access business web pages, therefore recommending the use of push notifications or text-based marketing. The system can also recommend email marketing for customers who often access the web through laptops and desktop computers.

3. Market Segmentation

Machine learning is capable of segmenting customers into different groups with a view to making it easy for marketers to reach out to them. To achieve this, computing programs analyze customers’ purchasing behaviors such as the kind of products the customer viewed and bought and among other specific attributes. This enables the program to segment customers into different groups following different patterns that are not-so-obvious to humans. This helps inform marketers of the specific approaches to use to win customers.

4. Diagnosing Problems in Marketing

As businesses automate their marketing initiatives, machine learning comes in handy in helping to automatically diagnose potential problems and thereby recommend solutions. For example, email marketing enables businesses to send automated emails to specific customers for a potential increase in conversion rates. Machine learning automatically observes how customers respond to such emails. The automated programs may, for example, detect that the click-through and conversion rates are not coherent. This may lead to the possible diagnosis of an error in the link embedded within the email.

Marketing is significantly evolving in line with the various technological advancements made today. One of the cutting-edge tech tools and concepts at our disposal is machine learning where computers can make informed decisions through pattern recognition and predictions.