Deep learning, known as deep neural networks, is an aspect of AI that emulates the learning that humans use to obtain certain knowledge.
Artificial Intelligence is becoming more and more human. We are facing a technology that already revolutionizes the world, but that still has much to evolve and change in our society. We have already spoken in BlogThinkBig how AI and robots are going to change our planet, and how they will replace human beings in millions of jobs. However, we should not hold a negative thought or fear that machines are intelligent and can learn. We have the key to controlling this complex technology that, to this day, is already implanted in many more tasks than we imagine.
Although we understand the capabilities of a Artificial Intelligenceyou know, it’s hard for us to understand how they can come to have these incredible qualities. One of the most important processes is ‘machine learning’, i.e., how an AI can learn and gain knowledge while he’s working. The basis of this technology is to ensure that a robot can enjoy the same cognitive qualities as a human (or better) and that they can help us. A clear example are virtual assistants such as Aura or Alexa.
Deep neural networks in machines
In this case, we talk about deep learning, one of the keys in the process. Also known as deep neural networks, it is an aspect of AI that emulates the learning that humans use to obtain certain knowledge. We could consider it as a way to automate the predictive analytics.
It’s important to know that deep learning is a particular branch of AI learning. In this case, while traditional machine learning algorithms are linear, deep learning algorithms are stacked in a hierarchy of increasing complexity and abstraction. If you have more questions, you can take a look at our post on the differences between machine learning and deep learning.
To understand what deep learning is, there is a great example that you can find in a TechTarget article. Imagine a child that the first word he learns is ” dog.” The child learns what is, and also what is not, a dog, pointing to objects and pronouncing the word “dog” to his father. The father says “Yeah, that’s Dog.”, or “No, that’s not a dog”. As the child continues to point at objects, he becomes more aware of the characteristics of all the dogs he points at, and his father tells whether he is wrong or not.
Little by little, the child clarifies a complex abstraction building a hierarchy in which each level of abstraction is created with the knowledge obtained in the previous hierarchical layer. I mean, the more dogs he sees, the more he knows what a dog is.
An AI needs millions of data to learn
This child procedure is the same thing that computer programs do. Each algorithm in the hierarchy applies a nonlinear transformation in its input, and it uses what it learns to create a statistical model as an output. Iterations continue until a certain level of accuracy is reached.
What is achieved with deep learning is that the system has less and less room for error. AI trains by introducing a huge amount of data which, by following that repetition process, makes a software impressively accurate. The programmers who are in charge of this work are in luck, if there is something that abounds in the digital age is information and data.
