Deep learning and Big Data to fight coronavirus

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A few days ago I commented on this blog how the Summit supercomputer with its AI developed possible molecules against the virus or how 3d printing helps to alleviate the shortage of medical equipment.

Today we will talk about how the combination of Big data and Deep learning can help not only prevent and manage epidemics like this, but also help discover the hoaxes on the internet.

As an explanation of what big data is, a database with the names and surnames of all the people born registered since Babylonian times, would be a huge database but it would not be big data.

Big data not only handles huge amounts of data, but it can be very heterogeneous, which makes it easier to compare data that we would not otherwise have related.

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Deep learning is a part of artificial intelligence, which tries to imitate the functioning of the human brain in the search for information behavior patterns, for decision making.

Unlike machine learning that requires human intervention for its training, deep learning is capable of working from scratch and accumulating experiences.

The case of AlphaGo Zero is already famous in this field, which was able to beat any human and all similar machines previously designed with just the instructions of the GO game and playing against himself tirelessly until learning.

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If we mix big data and deep learning we have projects like Blue Dot, which we already mentioned in this blog, capable of analyzing the news in 65 languages and comparing them with the flights carried out, environmental conditions, carrying insects and mass movements to control the progress of the epidemics.

Also by using a deep learning algorithm, the level of molecular affinity between existing drugs and virus-specific proteins can be predicted.

In short, there is no doubt that new technologies are an invaluable aid to new threats looming over us as the world grows smaller.

Versión en español