It is very likely that by the time you read this, you will have used the wonders of machine learning algorithms several times today. Your favorite social network may have offered you new friends. The search engine might have found pages relevant to your searches.

You would have dictated a message on your mobile phone, used optical character recognition software, read an article that was offered to you specifically according to your preferences, which also might have later automatically translated into a language understood by you.

Isn't it amazing how machines learn to do all this stuff and make our life easy? Yes, it is. But the idea of allowing machines to do these tasks is not a new one.

The concept of ​​teaching the machine to give it additional resources is almost as old as computer science. It was Alan Turing himself who, in 1936, laid the conceptual foundations of computation on a machine, and therefore of the computer. Since then, machines and their intelligence has narrowed down to two main fields: machine learning (ML) and deep learning (DL).

Siri, the Apple assistant can answer all our questions. And you've certainly heard of Tesla and Google's self-driving car. All of these innovations are the result of new technologies; namely machine learning and deep learning.

To summarize everything we mentioned above: Machine learning is everywhere in companies and Deep learning is everywhere in research.

So, what is the link between these two disciplines, and above all, what makes them different?

Let's take a look.

Machine Learning - an important part of Artificial Intelligence

Machine learning is a phenomenon demonstrating how algorithms can "learn" by studying examples. The idea is to collect a maximum of data, to analyze it, and to find a link allowing to create a rule and therefore to make predictions.

By applying it to health, for example, we can find correlations between certain data. We will be able to show that people who consume more sugar are generally more prone to obesity, for example. This is a simple analysis to perform since there are only two entry points: the consumption of sugar and the BMI of consumers.

The limits that demand a new technology

Machine learning remains limited in the number of input data that the information has. For example, to analyze an image of a conventional size, several thousand pixels will be sent to the machine. It will, therefore, be necessary to create a system of reception and grouping of information to select those which interest the algorithm. And this is where deep learning comes in.

How Deep Learning Helps?

One of the most telling examples is that of Siri, the voice recognition system developed by Apple. By studying the intonations and accents of thousands of people, voice recognition systems manage to translate requests with great precision and provide an adequate response.

Deep Learning is useful because it saves the programmer from having to take on the tasks of function specification (defining characteristics to be analyzed from the data) and optimization (how to weigh the data to provide an accurate forecast).

The algorithm does both!

Here's how we get there

The breakthrough in ML is to model the brain, not the world. Our brains learn to do difficult things. Take the case of speech understanding and object recognition. The brain does not process news with exhaustive rules, but with practice and feedback - just like a child learning about the world around him. 

For example, seeing a picture of a car and making conclusions that it is indeed a car. Therefore the child has learned without having a comprehensive set of rules. He has learned through training.

How does Deep Learning use this

Deep Learning uses the same approach. Small artificial calculators based on software are linked together. They function practically similar to the function of neurons in our brain. Therefore, they form a "neural network" which receives an input; the analysis happens; makes a decision about it, and is informed if its determination is correct. On the assumption that the answer is false, the connections between the neurons are adjusted by the algorithm. This results in a change in its future forecasts. Initially, the network will be wrong several times. But as we feed millions of examples into it, the connections between neurons will be tuned so that the neural network makes correct decisions on every occasion.

Deep Learning as a blessing

Thanks to this beautiful process and increasing efficiency, we can now:

• recognize the elements in pictures

• translate languages ​​in real-time

• use speech to control devices

• predict how genetic variation will affect DNA transcription

• analyze sentiment in customer reviews

• analyze bad behavior in the car

• detect tumors in medical images, and more.

Deep Learning at the service of artificial intelligence

Going further, the whole future of artificial intelligence is called into question. Because with these algorithms becoming capable of learning on their own, new perspectives are opening up.

It is rumored, for example, that algorithms will soon be able to give medical diagnoses with a probability of error significantly lower than that of a doctor.

Lawyers are not left out. The first artificial intelligence legal assistant has emerged. This new service is called ROSS. He analyzes the legal data and draws from it the relevant elements to answer the legal problems of the file. It has proven to be a great time saver for the lawyers in the U.S.! Like all machine learning technologies, Ross learns from his mistakes, and the more he is called upon, the more he will become a true partner capable of providing quality work.

Let's not stop the progress! Let's carry on the process.

Businesses are using artificial intelligence to empower themselves and achieve their organizational aims.

61% of marketers say artificial intelligence is the most important aspect of their data strategy.

Apart from this, an Accenture study also shows that current AI technology can boost business productivity by up to 40%.

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