A Google project is trying to make two kinds of artificial intelligence algorithms confront each other, hoping that there may be such intelligent machines in the future.

In the Google Brain Artificial Intelligence Laboratory, researchers have developed a system known as the "Generation Against Network" (GAN). Traditional artificial intelligence uses input to "train" an algorithm to "train" a particular topic by inputting a large amount of information. The algorithm trained knowledge can be used for specific tasks such as facial recognition. GAN generates new content from these learning information and creates new pictures and video content based on the understanding of similar real-life images and videos.

Google's approach is to confront the two algorithms against each other and further improve their "imagination." An artificial intelligence robot creates new content based on what it learns about the real world, while another robot points out imperfections and inaccuracies in these creations. This allows the system to create more realistic images, sounds, and other original works that are far more realistic than a single robot.

In the future, this process may allow the robot to learn new information without human intervention—a process known as “unsupervised learning”, which will become a huge leap forward in artificial intelligence technology.

Dr. Ian Goodfellow, who works at Google Mind, told Wired: “If AI robots can imagine the details of reality, learn how to imagine realistic images and realistic sounds, which will encourage artificial intelligence to understand the structure of the real world. You can think of it as an artist and an art critic. Generating a model can deceive art critics and make art critics mistakenly believe that the images they produce are true.”

Artificial intelligence systems rely on neural networks, which try to simulate the way the brain works to learn. These networks can be used to train to identify patterns of information, including speech, textual data, or visual images, which is the basis for the development of artificial intelligence in recent years. They use input from the digital world to learn, such as Google's language translation services, Facebook's facial recognition software, and Snapchat's landscaping filters and other utilities.

However, the process of inputting these data may be very time-consuming and limited to one type of knowledge. In order to expand the limit of this kind of machine learning, Google has already started designing artificial intelligence robots against each other.

In February of this year, a Google team used a game they designed to test whether confrontation algorithms work together or whether they attack each other. These experiments show that artificial intelligence is likely to work together according to the situation. The experimental results increase our understanding and control of complex multi-agent systems such as economics, transportation systems, or ecological health—all of which depend on our continued cooperation.

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