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Google.ai plans to make artificial intelligence accessible to as many people as possible

On the advent of Google I/O, the CEO, Sundar Pichai announced a new beginning in the field of artificial intelligence through Google.ai. He said that it will enable to democratize the benefits of the best and latest in the space of machine learning research. Google.ai will serve as the centre of the Google’s efforts which, includes research, applied AI and tools.

 

The brain team and Google will host their research on the new site. In addition, it will also let the users to select fun experiments that demonstrate the company’s progress in this particular field. One of it is AutoDraw, which enables the unskilled artists to put their best ideas onto paper. Duet, as the name itself, suggests helps the piano players to play quick and in an efficient manner. Quick Draw is a game which tries to guess your drawings with the help of the algorithms. This site also hosts a collection of videos and posts which display the Google’s AI first efforts.

 

Tensor Flow at Google has played a vital role in making sure that machine learning is accessible to a larger number of developers. However, Google targets to bring out day to day researchers from various Universities and private research labs from across the world into the hands of those who are interested to learn about the artificial intelligence.

Sundar Pichai agreed that at this point of time, building machine learning models is expensive and takes a lot of time. This he said is due to the lack of Engineers with required skills in this field. However, Google Cloud and Tensor Flow, will make it easier for the engineers to do more with less as this technology become more ubiquitous.

Sundar Pichai in his conversations also mentioned the role that AutoML can play in the future. He also envisioned a future where neural nets will be able to create newer neural nets by their own. This would be an eventual result where the researchers will have a greater control of the Generative Adversarial Networks (GANs). Moreover, the reinforced learning will get applied in a more challenging context and in new endeavors.

 

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