Cool Machine Learning Examples in Real Life

Algorithms | 0 comments | by Thomas De Vos

Practical Machine Learning Examples in Real Li

Machine learning is fun, challenging, puzzling, and even a bit scary if you’re one of those people that believe robots will someday steal our jobs and rule the world. This article will focus on Real Life and Practical Machine Learning Examples. Whether we like it or not, we are surrounded by adaptive smart things that can fix some of our most common daily queries in a split of a second. Because “machine deciphering an equation to find a way to fix a particular problem based on exemplified data” is not exactly easy to digest, machine learning seems like a more suitable name.

Once an esoteric practice that only the savviest data scientists could unravel, machine learning has gone mainstream, and it is now more accessible to the masses than ever before. We’re living in the golden age of machine learning, thanks to omnipresent big data, easier frameworks and convenient tools. The following examples might help you understand machine learning a bit better.

Speech Recognition – Siri & Cortana

Machine Learning Examples - Siri and Cortana

Thanks to advanced technology, ML (machine learning) techniques are becoming increasingly more widespread in applications. We use apps based on machine learning every day, and one typical example is Apple’s Siri. Aren’t you just a bit curious to know how Siri works? How is it possible for Siri to answer your questions or decode speech and convert it to text?

  • Siri

To begin with, Siri uses various technologies, including question analysis, natural language processing, machine learning, and data mashups. Speech recognition, in particular, involves spoken word conversion to a sequence of words, also known as speech to text (STT) or automatic speech recognition (ASR). Speech recognition apps are available in many forms and can be found in home appliances, cell phones, data entry, medical transcriptions, air traffic controllers, and more.

The training process of a speech recognition systems demands several techniques, and the level of complexity varies depending on the nature of the training. Siri doesn’t understand the English language, but because it is based on a set of specific templates that trigger specific actions, it adapts making you believe it’s “human”. It doesn’t learn new things, but it can be expanded and extended. It can only perform programmed tasks.

  • Cortana

Microsoft’s Cortana is smart enough to crack a joke and tell whether you should leave early for work because there’s a lot of traffic. You cannot trust it completely, but it’s a start. Cortana redefines the term “virtual assistant” because it was built following a model called “dynamic learning”. Cortana is an advanced business intelligence (BI) analytics suite; a data storage package that includes information management system, business intelligence software, machine learning, all wrapped up in a monthly service subscription.

Available on Microsoft’s Azure Cloud, Cortana enables startups and businesses to completely transform their data into intelligent actions. It allows users to process data from a many different data sources; change that data, apply machine learning and data mining to advanced analytics techniques, and then extract actionable information that enables businesses to take timely, intelligent actions.

Let’s have a look at another Machine Learning Example.

Face Recognition

Facebook Helps You “Remember” the Name of Your Friends

Machine Learning Examples - Siri and Cortana

Another very cool machine learning example is Facebook’s newest face recognition software. We know, it’s pretty scary! How is it possible for Facebook to know the name of your friends when you upload a group picture if you haven’t even had the time to tag them?

Back in the day, Facebook “recommended” users to tag their friends. All they had to do was click on their face on the picture and type their name to tag them. Facebook didn’t know who those people were. Now, when a photo is uploaded onto a user’s profile, Facebook automatically recommends that user tag their friends. And it knows exactly who is who. Abracadabra? Or just creepy?

The new technology is better known as face recognition, and it uses machine learning (Facebook’s algorithms) to recognise familiar faces in your friends’ list. It’s pretty amazing, and it can do it because it is a form of adaptive technology. You probably tagged those friends before in the past, right? Facebook’s machine learning mojo memorised those patterns, and voila! There’s your answer.

How Much Machine Learning is There in Search Engines?

Search Engines and Machine Learning

Why do you think Google is so powerful? It’s no secret that machine learning is gaining popularity across a wide variety of industries. Not all programs are capable, though. It all depends on how much data these programs have to train their models. That’s the main reason companies like Facebook and Google need as much data as they can get. For instance, Google made TensorFlow open-source. It is an excellent software toolkit that helps build large-scale machine learning apps. Giving that away, for free, was a big deal. If you don’t know what TensorFlow is, imagine that it’s the same thing that powers the almighty Google Translate.

However, without Google’s substantial mountains of data (in every possible language) there’s no way someone can compete with Google Translate. What makes Google so powerful? It’s DATA. Google has a lot of data it can leverage, and all that data can be adapted and taught to perform specific tasks.

Machine Learning Examples & Machine Translation

Machine Learning Translation

Everyone knows Google Translate, and many people depend on it. But how can the website translate between over 100 human languages so seamlessly? Sure, there are sentences, grammar and structure problems to most translations; but nevertheless, it’s still accurate. At the core of Google Translate there’s a technology called “machine translation”, which has managed to change completely the way people communicate.

In the last two years, the approach to basic machine translation has been completely rewritten by deep learning. There’s a pretty fascinating piece of technology hidden behind this breakthrough, known as “sequence to sequence learning”. The technique is pretty powerful and it can be used to fix many problems. We’ve seen what it can do for Google Translate. But did you know that the same algorithms can be applied to describe pictures and write AI chat bots?

PayPal Uses Machine Learning to Detect Fraud

PayPal and Machine Learning as a Great Example

Detecting Fraud within banks is another Machine Learning Example, let’s explorer in depth how PayPal uses Machine Learning.

Fraud detection matters a lot because it addresses a problem that’s urgent, and machine learning can help combat fraud. PayPal for example – an international online payments system that serves as an alternative to conventional payment modes and deals with money transfers online – has dealt with its fair share of fraudulent transactions over the years. So it’s only natural to think that the company would do just about anything to combat fraud.

  • Risk management algorithms

PayPal currently uses several main types of algorithms to manage risk. All of them pertain to machine learning: long, deep learning and neural network. The company has learned over the years that the most efficient approach to combat fraud is to use them all at once. Linear algorithms are the norm, and it is a well-established practice at PayPal. Separating good customers from bad customers with a single straight line is something they’re used to do. But the world is not linear, and the approach is not exactly the most accurate.

The company soon realised that rather than use a single line to separate good customers from bad customers, a more productive approach would be to use multiple lines, or bend the straight ones. A lot of their algorithms are based on a hierarchy. Thanks to today’s advanced computing infrastructure  – together with a huge quantity of data that can be used to fed those algorithms – PayPal has managed to boost neural net efficiency and assess risk at a minimal scale.

  • Deep learning 

There’s a very complicated process that helps PayPal combat fraud, and one of their best trump cards is deep learning; a technology that powers speech recognition, computer vision, and numerous other applications. In layman’s terms, the company gathers huge quantities of data about its customers, including financial data, network information, and machine information. It uses the data to feed the deep learning beast.

Nevertheless, even though PayPal relies on advanced technology and machine learning to spot patterns, fix glitches, and separate the good from the bad thus combating fraud, a machine cannot find the data on its own. There’s just too much, and not all of it is useful. Human insight is fundamental because it helps feed a machine the right amount of data.

Machine learning depends on big data. But it doesn’t need ALL the data; just the right one. Machine learning is not magic, and once you get a feel of the techniques that can be used to fix a problem that seems difficult at first, you begin to understand that machine learning can be used to solve anything. However, keep this in mind: machine learning works only if a problem can be solved using the big data that you fed to the machine (software). The only difference between humans and machine learning tools is that the latter can fix things much quicker.

Can you think about more Machine Learning Examples which you’d like to share with us? Tell us more about your experience with this ground-breaking technology. The perks of machine learning are numerous, and IT Enterprise is here to help you understand more about its great potential. Let us convince you that whatever you want to achieve in business, you can. You just need a little nudge to get started.

Leave us a note if you know a number of practical Machine Learning examples.

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