Google AI to Create Simliar AI

  Weird but true… Google researchers are teaching their Artificial Intelligence(AI) to create more of its type. Google…


Weird but true… Google researchers are teaching their Artificial Intelligence(AI) to create more of its type.

Google has announced another big push into artificial intelligence, unveiling a new approach to machine learning where neural networks are used to build better neural networks – essentially teaching AI to teach itself.

These artificial neural networks are designed to mimic the way the brain learns, and Google says its new technology, called AutoML, can develop networks that are more powerful, efficient, and easy to use.

The News

Pichai at I/O

Google CEO Pichai showed off AutoML on stage at Google I/O 2017 this week – the annual developer conference that Google throws for app coders and hardware makers to reveal where its products are heading next.

“The way it works is we take a set of candidate neural nets, think of these as little baby neural nets, and we actually use a neural net to iterate through them until we arrive at the best neural net,” explained CEO Pichai

That process is called reinforcement learning, where computers can link trial and error with some kind of reward, just like teaching a dog new tricks.

It takes a massive amount of computational power to do, but Google’s hardware is now getting to the stage where one neural net can analyse another.

What is Neural Networking?

Inspired by the human brain and nervous system, neural networks are mathematical systems designed to analyze enormous amounts of data. Like a brain, neural networks rely on large numbers of interconnected “neurons” work at the same time to solve problems. But instead of following instructions like a traditional computer, they follow examples and “learn” to improve the rules and methods they use.

Machine Learning(ML)-


Machine learning is getting computers to make their own decisions based on sample data – is one approach to developing artificial intelligence, and involves two major steps: training and inference. The inference part is where the system then takes what it’s learned to make educated guesses of its own. It’s an intensive process for scientists and engineers, and that has limited the use of neural networks and machine learning to small pools of computer experts and academics. That’s where AutoML comes in. Its machine-learning software that can help create machine-learning software.

The idea of Google to teach AI to create copy of itself can reduce the workload of the engineers at google will have a reduced working on the engineers which are already in a handful quantity.

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