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Why do we need Machine Learning?

 

Every time, it is seen that whenever you opens a browser, you will find someone written about machine learning. About applications to self-driving cars, everything is covered in a articles and blogs. So many companies are focusing towards "Machine Learning as the Future" but what does that really mean? Machine learning is the idea that there are generic algorithms that can tell you something interesting about a set of data without you having to write any custom code specific to the problem. Instead of writing code, you feed data to the generic algorithm and it builds its own logic based on the data.
Think of machine learning like this. As a human, and as a user of technology, you complete certain tasks that require you to make a decision or classify something. For instance, when you read your inbox in the morning, you decide to mark that ‘Win a Free Cruise if you click here’ email as spam. How would a computer know to do the same thing? Machine learning is comprised of algorithms that teach computers to perform tasks that human beings do naturally on a daily basis.
The first attempts at artificial intelligence involved teaching a computer by writing a rule. If we wanted to teach a computer to make recommendations based on the weather, then we might write a rule that said: IF the weather is cloudy AND the chance of rainfall is greater than 50%, THEN suggest taking an umbrella. The problem with this approach used in traditional expert systems, however, is that we don’t know how much confidence to place on the rule. Is it right 50% of the time? More? Less?


Everyone talking about Machine learning……
There are basic algorithms for teaching a machine to complete tasks and classify like a human date back several decades. The difference between now and when the models were first invented is that the more information is fed into the algorithms, the more accurate they become. The past few decades have seen massive scalability of data and information, allowing for much more accurate predictions than were ever possible in the long history of machine learning.

Why do we need ML?
That’s the first question I asked myself. We, humans, are already capable to read, understand and extract conclusions of almost everything in the world. And we also hope to give helpful advice if we are expert enough.
Then, why? Because we are just that, humans. Sometimes there is something we hadn’t seen, there are challenges we are not prepared to success in. Because there are a million things that could be wrong. And because, face it, we are expensive enough to think of something to at least help us do the same but faster, cheaper, more accurately.
On the other hand, I never said that machines would replace us and win a war against us. We are still as far from it as reaching Pluto and living there. But they can be of great help today.
Because of new computing technologies, machine learning today is not like machine learning of the past. It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks; researchers interested in artificial intelligence wanted to see if computers could learn from data. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new – but one that has gained fresh momentum.
Machine learning has several very practical applications that drive the kind of real business results – such as time and money savings – that have the potential to dramatically impact the future of your organization. At Interactions in particular, we see tremendous impact occurring within the customer care industry, whereby machine learning is allowing people to get things done more quickly and efficiently. Through Virtual Assistant solutions, machine learning automates tasks that would otherwise need to be performed by a live agent – such as changing a password or checking an account balance. This frees up valuable agent time that can be used to focus on the kind of customer care that humans perform best: high touch, complicated decision-making that is not as easily handled by a machine. At Interactions, we further improve the process by eliminating the decision of whether a request should be sent to a human or a machine: unique Adaptive Understanding technology, the machine learns to be aware of its limitations, and bail out to humans when it has a low confidence in providing the correct solution.
While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. Here are a few widely publicized examples of machine learning applications you may be familiar with:
  • The heavily hyped, self-driving Google car? The essence of machine learning.
  • Online recommendation offers such as those from Amazon and Netflix? Machine learning applications for everyday life.
  • Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation.
  • Fraud detection? One of the more obvious, important uses in our world today.

Machine learning is needed for tasks that are too complex for humans to code directly. Some tasks are so complex that it is impractical, if not impossible, for humans to work out all of the nuances and code for them explicitly. So instead, we provide a large amount of data to a machine learning algorithm and let the algorithm work it out by exploring that data and searching for a model that will achieve what the programmers have set it out to achieve.

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