There was a time, people did worry that the software will take jobs such as driving trucks away from humans, as a result of the development of Artificial Intelligent.Now we can laugh at ourselves because leading researchers are finding that they can make software that can learn to do the task of designing machine-learning software, one of the trickiest parts of their own work.

So let’s meet AutoML, a project or an experiment that has been taught machine-learning software to build machine-learning software by researchers at the Google Brain artificial intelligence research group. Google has announced that AutoML has beaten the human AI engineers at their own game by building machine-learning software that’s more efficient and powerful than the best human-designed systems.

Google says the system recently scored a record 82 percent at categorizing images by their content, while the auto-generated system scored 43 percent and the best human-built system scored 39 percent. These results are meaningful enough for Google’s well-being of the next-generation AI systems. “Today these are handcrafted by machine learning scientists and literally only a few thousands of scientists around the world can do this,” WIRED reports Google CEO Sundar Pichai said. “We want to enable hundreds of thousands of developers to be able to do it.”

According to the WIRED, AutoML is still a research project. This technique attempts, to automate it to work. But outside of Google, many researchers work on this technology. When AI products AI are practiced, outside the technology industry can spread outside learning.

Have you ever heard about Pichai’s “AI first”? AutoML can stimulate that strategy. The company uses it to create more efficient and innovative products using the computer. Company’s Google Bing Research Team’s researchers or the London-based DeepMind Research Laboratory‘s researchers have cut data centers and have helped improve Google’s ability to map the new cities. AutoML can make these specialists more productive, or build engineers with less-qualified engineers by themselves.

Do you ever know Google refused to do anything to discuss AutoML? Researchers outside the company say that some of the AI’s expertise has become an experimental site for the automation of some of the AI’s specialists. It is necessary when AI systems are more complex.

Most metalworking is to try to imitate human nets and to get more data through those networks. Not this – to use an old saw-rocket science. Instead, when it comes to the training, it is very appropriate to have plastic and craftwork. The difficult part is the imitation of the first place in the brain structure and the more complex problems that are in accordance with the scales. It seems to be very complicated. But using good tricks, networking techniques, such as audio and video devices, such as a good paycheck.

Creating a creation of a neural network is far more difficult to configure than someone who already has one. Recent surveys show that this is more practical. This is Professor Mehryar Mohri of NYU who is working with Scott Young and another 3 on a system called AdaNet, in a collaboration that includes researchers at Google’s New York office. AdaNet is based on a solid theoretical analysis, including data-dependent generalization, where the labels are collected; it builds a nerve layer over the layer. Mohri argues that it is possible to detect and prevent problems by reducing manual by using neural networks. “It gives people more hands for another problem,” he says.

So the AI revolution is in progress, and its future is brighter AutoML is the next generation of machine learning tools. Tomorrow’s machines won’t just learn, they’ll self-update and be capable of creating custom programs to solve unforeseen problems. Our hope lies in a future where AI takes care of time-consuming tasks like programming, thus freeing humans to do the things machines can’t do – like enjoy tacos and beer.