the trend of Artificial Intelligence that copies a human brain – WAU

Deep Learning is a technology increasingly present in everyday life. It allows systems to be able to give more accurate answers and to perform even more complex tasks, all due to the analysis of data in deeper layers.

Machines and systems that behave like humans don’t scare us anymore. On the contrary, they are increasingly present in everyday life and the intention is that they really work that way.

The basis of this is Artificial Intelligence, which has in Machine Learning a strand aimed at enabling machines to learn from the data with which they have contact.

Going further, there is another category of Machine Learning that takes things to another level: Deep Learning.

The idea is to apply to machines much more than human behavior, but mainly simulate brain reasoning to generate an even more intelligent system. This technology is responsible for services that are on the rise, such as virtual assistants.

Understanding Deep Learning is not difficult, but it is necessary to delve into this idea to know its origin and how the technology was designed. That’s what we’ll talk about in the following topics:

Read on and find out more about it!

What does Deep Learning mean?

Deep Learning is a subfield of Machine Learning, focused on in-depth analysis of data in a much larger volume than usual.

The purpose of this technology is to allow systems and machines to be able to observe patterns and correlations in a large amount of information.

For this, the basis of Deep Learning are its algorithms that help identify data. The way they are designed is an attempt to reproduce the functioning of the neural networks of the human brain.

Thus, such algorithms are able to repeatedly perform analysis of this data, which generates a greater and deeper learning capacity.

In Machine Learning this already happens, but on a smaller scale. The idea is to allow systems that deal with data to be able to learn from the information they receive, as is the case with chatbots.

For each question asked, there is a collection of information for its database. So the more he is stimulated, the more he is able to answer questions.

When we speak of Deep Learning, there are far more complex applications of the technology in question. Neural networks reproduced as algorithms are able to increase this learning capacity, since can handle a lot more data.

Deep learning is the key to developing some of the main technologies that we see today, when data is generated in giant volumes at all times.

A simple web browsing is a source of ample information for systems to function, as they learn and perform functions.

Below are some technologies based on Deep Learning.

Virtual Assistant

Virtual assistants are increasingly useful and have won over users. Siri, Alexa and Cortana are the main ones, each in their platform and operating system.

These technologies use Deep Learning as the main resource to enable the understanding of what each user requests when activating the voice command.

Speech has different details that make it distinctive, such as intonation, language and accents, which varies from region to region.

When a voice command is made, the virtual assistant needs to deeply analyze this material you receive, making crosses with the data, to then gain understanding. It is Deep Learning that allows this result!

Facial recognition

Another technology already widely inserted in daily life is facial recognition, widely accessible mainly on smartphones, which use it as a security factor.

The proposal is only to release access to the device, or its functionalities, given the confirmation that the face facing the camera is really that of the owner.

Deep Learning uses the same basis as explained in the voice commands: analyzes, at the time of registration, all the detailed features of the face of the device owner. Once registered, these characteristics are consulted whenever facial recognition is activated.

Deep Learning enables analysis even in dark places, with the use of glasses or with changes, such as a new haircut, for example.

Autonomous vehicles

Autonomous vehicles, despite being widely seen in tests, already work in smaller structures, to make deliveries in some countries.

They need to have an accurate and detailed navigation system, as they do not depend on a command to function.

As a basis, they have something similar to car navigation systems, but even more effective. It turns out that there is a need to have a technology capable of dealing with the dynamism that the streets offer to these autonomous vehicles.

For this reason, Deep Learning was seen as the ideal technology, since all the time it allows to evaluate the navigation conditions and, in view of that, rethink routes and identify specific points, even if, for example, it is snowing and signs are covered.

Offer customization

The personalization of offers sometimes still surprises the most unsuspecting, with suggestions that are totally appropriate to their profile. Well, these are just algorithms working around the clock to identify each of your actions.

Every time you visit a product on a website, for example, you are emitting data that is collected by Deep Learning systems.

It is precisely because of this action that Netflix manages to indicate films and series according to your personal taste.

This is not to spy on you, but because Deep Learning technology works by collecting this data and making in-depth analyzes of your profile.

The same is done, for example, by Amazon to offer products on the main page when you access it.

What are Deep Learning neural networks?

Neural networks are inspired by the functioning of the human brain. Simply put, they are a set of interconnected neuron fields that work by exchanging information.

It is these activities that allow us to identify something, reason, assimilate something we read or hear, in addition to a series of other simple activities.

Deep Learning’s neural networks are not much different when we think about machine learning.

However, instead of neurons we are talking about algorithms dedicated to deeply analyzing the vast amount of data to which they are exposed.

In operation, neural networks use these data to study them in depth, performing tests and crossing information to generate this artificial reasoning. It is from this that Deep Learning makes interpretations and makes the right decisions to offer answers to the commands that have been requested.

In this work, neural networks work in a constant trial and error process, since the volume of data is large.

In practice, this works as a kind of information refining, until it is possible to find patterns that offer the right answers. The data, when identified, is cataloged and stored to generate a bank that is useful for the system.

How important is Deep Learning in intelligent systems?

Deep Learning can be understood as a fundamental technology for intelligent systems that propose learning and, consequently, the execution of advanced tasks.

Today, this feature is expanding and can go far beyond the examples you saw throughout this post. The future perspective helps to understand the relevance of this technology and, mainly, where it can arrive in some time.

Understand in more detail how Deep Learning impacts Artificial Intelligence and what possibilities are realized by the operation of this science.

Structure exploration

Intelligent systems need to have their processing capacity elevated to the maximum, which completely involves data analysis.

Deep Learning, thanks to its structure of neural networks formed by algorithms, makes all technological processing structure is used. This is what creates the concept of “deep”, that is, depth at work.

Resource learning

Machine learning was a really relevant advance for modern technology, allowing systems to act like people on tasks.

More than that, the learning of Deep Learning resources brings a deepening that allows functionalities that are still seen as surprising. The main point is: much more will be developed from that!

Scalability of neural networks

Neural networks are not static and predefined structures to function in a certain way and with limited work complexity.

The scalability of these algorithms is a concrete feature, which practically removes any limits to the data analysis and understanding process. This generates a perspective of constant improvement for any system.

In the era of digital transformation, Deep Learning is an example of how we live in a period when technology is at the center of everything, from simple everyday tasks to managing companies.

Systems are increasingly able to act without human intervention, reproducing even a person’s reasoning, inspired by the functioning of the brain.

Did you know that Artificial Intelligence can be implemented productively in different types of applications? Understand more about this in our post “How to implement artificial intelligence in my software?”