Already now one can observe examples of how artificial intelligence technologies are able to exhibit some seemingly at first sight and peculiar only to a person's traits. We create humanoid robots, at least very similar to us, some are engaged in creating algorithms that can perform what people are usually capable of - writing music, painting, or learning.
With the development of this sphere, companies and developers are beginning to look for the opportunity to change the very basis on which the artificial intelligence algorithms are based now, and are accepted for the study of real intelligence, as well as the way how to effectively imitate it in engineering and the generation of next generation software. One such company is IBM, which has set itself the ambitious task of teaching AI to behave more correctly (like a human brain) rather than as a set of programmed algorithms.
Most of the existing machine learning systems are built around the need to use a huge set of different data. Whether it's a computer designed to look for ways to win a game of logic, or a system built to detect signs of skin cancer based on digital images-this rule always works. But such a basis for work looks very limited and concise, and of course this is what essentially distinguishes such systems from how the human brain works.
IBM wants to change this. The research team from DeepMind has created a synthetic neural network, which is based on rational decision-making when working on a particular task.
Rational machines "By giving artificial intelligence a lot of objects and a specific task, we force the network to detect existing matches," commented on the pages of Science Magazine Timothy Lillicrap, computer specialist of the DeepMind team.
In the network tests conducted in June, the system, given a variety of factors, was given various tasks related to the digital image. For example, this: "Before the blue thing on the image is an object. It has the same shape as that tiny blue thing that is to the right of the gray metal ball? "In this test, the artificial neural network was able to determine the desired object in 96 percent of the cases, while the conventional machine learning models were able to cope with the task in 42-77 percent of the cases.
Recently, artificial neutron networks continue to improve in the understanding of the human language. Researchers also want, in addition to making reasonable decisions, such systems can demonstrate and maintain attention, and also store memories.
According to Irina Rish, a researcher at IBM, the development of artificial intelligence could be significantly accelerated and expanded through the use of similar tactics.
"Improving neural networks remains a subject of engineering, usually requiring a huge amount of time to come to the right architecture that works best. In fact - this is a method of human trial and error. It would be great if these networks could create and improve themselves ".
Some, of course, can scare the idea of ??AI networks that are able to create and improve themselves, but if we find a competent way to monitor, control and manage this process, it will allow us to go beyond the currently existing restrictions. Despite the growing fear of the revolution of robots that will enslave us all, the development of the AI ??sphere is predicted by thousands of saved lives in medicine, the opening for us of the opportunity to visit and even settle on Mars and much more.