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In recent years, the term “deep learning” has found its place in the business sector when talking about artificial intelligence, big data or analytics, and this is for a reason. Deep Learning is a promising approach in artificial intelligence to develop autonomous self-teaching methods that are revolutionizing many industries.
Nowadays, Deep Learning is being used by large multinational companies, such as Google, which uses this technology, for example, in its image and voice recognition systems; Netflix or Amazon, which use these algorithms in their recommendation and search systems, and the MIT, which has given computers the capability to predict the future with this technology.
But let’s start from the beginning…
What is Deep Learning and how is it different from AI or Machine Learning?
As nvidia explained in its blog, the easiest way to differentiate artificial intelligence, machine learning, and deep learning is to visualize them as concentric circles with AI -the first one appearing- being the biggest circle. In this circle, we would find machine learning -the next one- and then, the deep learning area, inside both of them.
The easiest way to differentiate AI, ML and DL is to visuaize them as concentric circles with AI being the biggest one.
Artificial Intelligence has been declared one the brightest keys for the future of our civilization. In the past few years, this technology has experienced an authentic revolution at all levels, especially since 2015 and the high availability of GPUs and the Big Data explosion, which have had a considerable impact on this.
Artificial Intelligence allows us to program a computer to do something; basically, we tell the machine what to do. Although the Artificial Intelligence market is still small, we can find this technology in many applications of our daily life: digital assistants, voice recognition systems, translation technologies, etc.
According to PWC, it is possible to define three basic forms of AI:
- Assisted: AI that improves what your business is already doing.
- Augmented: AI that enables your business to do things it couldn’t otherwise do.
- Autonomous: AI that acts on its own, choosing its actions on behalf of your business goals.
Let’s take a look at the main applications, i.e., current and future applications:
Coming back to our set of circles, we could find Machine Learning inside the AI sphere. Machine Learning is described as a subset of AI, whereby we provide a computer with data and the computer learns on its own, improving at tasks with experience. In other words, machines are given the information they need to do the job themselves.
This technology is showing great potential, providing indispensable tools for industry to move towards change since, on its own, the machine trains to analyze data, learns with such data and forecasts possible futures.
These algorithms have been developed over the years: decision trees, Inductive Logic Programming (ILP), clustering, reinforced learning, etc., or neural networks, which learn and behave in a remarkably similar way to human brains.
In this article, we talked about what machine learning is and its main business applications: facial, voice or object recognition, search engines, anti-spam, anti-virus, prediction and forecasts, reading comprehension, autonomous vehicles and robots, analysis of economic data, etc.
According to this, Machine learning sounds like the state of art on Artificial Intelligence, but let’s take one step -or circle- further. What about Deep Learning?
If we go back to the set of circles, the last circle corresponds to Deep Learning. It could be situated at the forefront of technology, up to the point that, with the help of this algorithm, we could reach a state of science fiction for which all of us have been dreaming for so long.
Deep Learning is a subfield of machine learning that is inspired by the human brain that teaches computers to do what comes naturally to humans. To this end, the software trains itself to perform tasks with a large set of labeled data, which can be used to take decisions on other issues. As in machine learning, this is possible thanks to a type of neural networks called deep neural networks.
This technology can be used to add sounds to silent films, classify objects in photographs, color black and white images, forecast energy market prices, detect cancer, etc.
Deep Learning vs Machine Learning in business
As we mentioned at the beginning, these terms -machine learning and deep learning- tend to be used indistinctly. In order to shed more light on how to differentiate between them, the table below shows the main applications of these models, categorized by industry:
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