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5 Ways Machine Learning Technology Will Make Your Life Easier
Staff Writer

Have you ever asked yourself?

• What is machine learning technology?

• How is the latest machine learning technology being used today?

• What is the connection between machine learning and AI?

• How will machine learning technology make life easier or better?

Machine learning is a programming methodology used to automate the construction of analytical models based on data sets that are too large and complex to be processed efficiently by humans. Technically speaking, machine learning technology is a sub-discipline of artificial intelligence programming. It relies on programs that are capable of processing large volumes of data and learning to recognize relevant patterns in the data, similar to what a human might do, only much faster and more efficient. Some machine learning programs even use their insight to make decisions on their own without human interference and achieve better results than even the most qualified person.

If you've never heard of machine learning before, you might be surprised to learn that machine learning is already playing a role in shaping the world we live in and interact with each day. With applications across industries, here are just five ways that machine learning technology will make your life easier in the next five years.

Machine Learning Will Propel Self-Driving Cars

Self-driving cars are one of the more imagination-inspiring applications of machine learning. The world's largest automobile manufacturers, technology companies and ride-sharing service providers have come together over the past decade to work towards a future where self-driving cars dominate the roadways.

Cars driving on road Image by Taras Makarenko via Pexels

Self-driving vehicles rely on a number of input sources for information about their environment. Radar sensors measure the distance between the vehicle and any environmental obstacles, a set of roof-mounted cameras uses 360-degree imaging to measure the distance to objects in the environment, detect traffic signals, and recognize moving objects, and a LIDAR unit is used to generate a 360-degree imaging of the vehicle's surroundings.

Of course, the main challenge for engineers building the vehicles of the future is not the collection of sensor data - it's creating an autonomous driving computer program that knows what to do with it. A machine learning algorithm is used in these computers to continuously render a 3-D image of the surrounding environment and to understand and predict how the environment could change over time. Self-driving cars must be trained (using machine learning) to detect common environmental objects, recognize or identify those objects, and predict their future movement.

Safety Protocols on the Road

Self-driving cars could play a huge role in making life easier for people. Advanced machine learning algorithms could lead to safer vehicles, fewer car accidents and commuters could enjoy private, automated transportation that frees up their attention to reclaim valuable productive time. At the same time, consumers should be aware of the concerns surrounding CAN bus hacking in vehicles. While machine learning can bring huge benefits, engineers must design systems that are secure and resistant to penetration by nefarious actors.

Machine Learning Drives Speech Recognition Technology

If you're trying to wrap your head around the kinds of problems that are best addressed by machine learning, speech recognition is an excellent example. Writing a computer program that can recognize human speech has proved a significant challenge for programmers using traditional methods - there is no single sound or combination of sounds that correlate specifically to human speech, and each individual person has a unique way of pronouncing words and phrases.

With machine learning technology, engineers have created speech recognition robots that can learn to better recognize the user's voice over time. Given some time to adapt to the speech patterns of a new user, speech recognition robots can convert your speech to text with near-perfect accuracy.

Speech recognition technology has positive implications for security, productivity, convenience and enhanced access in our society. It allows users to quickly dictate messages or e-mails while on-the-go, but it can also provide persons with some disabilities the ability to communicate more effectively or contribute in a work environment without needing to type.

Potential Health Benefits

Suffering from carpal tunnel, arthritis or another condition that affects your ability to type? Machine learning technology is already powering speech-to-text software that can help you get back to writing your novel or answering emails without needing to use your hands.

Machine Learning Used to Detect and Prevent Credit Card Fraud

Did you know that your credit card company is probably already using machine learning to detect and prevent credit card fraud for its millions of customers? Imagine that you are trying to write a program that will identify fraudulent purchases on a credit card - what rules will the program have for detecting a potential fraud? You might write a program that flags transactions that are larger than normal, or those that take place outside the customer's country - that seems logical, right?

In fact, it can be very difficult to write simple rules for a fraud detection program. What if it's Black Friday and the customer has been saving up all year to splurge on a new television set? What if the customer is actually on vacation in the country took place?

Financial Protection

That's where the machine learning algorithms come in - rather than using simple rules to assess whether a likelihood of fraud can be associated with a particular transaction, machine learning is used to train fraud detection bots to analyze transactions across many parameters, combining information from different sources to detect the common signals associated with fraud across far more transactions than a human being could do in the same time period. As a result, financial institutions are becoming much more efficient at proactively detecting and reversing fraudulent transactions.

Machine Learning Technology Underlies Next Generation of Virtual Personal Assistants

Machine learning technology will play an important role in the development of the next generation of personal virtual assistants. The personal assistants of today are already fairly advanced in their capabilities - they can set a timer for you on command, play your favorite music and even search the web for you. As machine learning technology continues to develop, however, the Alexa and Siri of today will be replaced by more sophisticated virtual personal assistant with advanced capabilities supported by machine learning.

round grey speaker on brown shelf Image by John Tekeridis via Pexels

Virtual Assistants Learn on the Job

The next generation of virtual personal assistants will do a better job of anticipating your needs even before you make a request. They'll have a better contextual understanding of conversations and will remember things from your previous interactions to offer improved service in the future. They will learn information about you, like your phone number, address, and e-mail, and use that information to quickly fulfill requests on your behalf - signing up for a newsletter, having food ordered to your house, and scheduling meetings or events into your personal calendar.

While today's virtual assistants depend on highly specific instructions in each communication, those of the future will use context and cues to anticipate your needs before you even communicate them, making life much easier and streamlining daily tasks for users.

Machine Learning Technology Can Detect Spam Emails and Malware

Spam email and malware detection are two areas of technology that enjoy significant benefits with machine learning technology. The difficulty in this area is nefarious programmers and cyber-criminals are continuously updating malware programs to get around the latest detection software.

Sniffing Malware

According to one source, over 325,000 new malware programs are detected each day, and each one bears 90-98% resemblance to its previous versions. This means that while a rules-based approach to malware detection falls short of detecting newer versions, a machine learning-based algorithm can easily detect that a program is 90% similar to a known malware program and flag it as dangerous.

With advances in machine learning technology, the average person will be able to more easily protect themselves from dangerous malware programs, spam e-mails and online scams.


Machine learning technology is advancing rapidly, allowing humans to analyze and control our environments in new ways. Machine learning technology is being used to train the self-driving cars of the future, detect and reverse credit card fraud, power speech recognition software, detect spam e-mails and design more robust virtual personal assistants. While machine learning may conjure scary images from Hollywood, but in reality it enables many technologies that will and already do make our lives easier every day.

Total Phase builds products for embedded systems engineers working with artificial intelligence applications and machine learning technology.

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