Data Science Investment Counter —Funding raised by UK data science companies in 2018.
£ 5.640 Million

Google AI has Built its own “Child”

Are we getting closer to the much-fabled intelligence explosion? Google researchers have created an artificial intelligence able to design and build other AIs; the news now is that this AI’s first “child” is better at recognising images than any smart system humans have ever made.

Cue the shrieks of terror from journalists, commenters and sundry AI-doomsayers, warning that super-smart “AI children” could be the beginning of humankind’s annihilation at the hands of evil machines . Still, to be  honest, the breakthrough is more interesting than worrisome.  And the extreme specificity of the ability at which Google’s AI-built AI excels makes it rather harmless.

To pull this off, the tech giant used an AI system called AutoML  — first introduced in May 2017 on the company’s research blog. AutoML is a neural net relying on a technique called reinforcement learning — an automated machine learning method— to  build new AIs.

In the latest experiment using the method— carried out in November, but dug up and reported by Alphr days ago—  AutoML efforts begot NASNet, a “child AI” trained in real-time image recognition in videos. AutoML devised the algorithm and trained it, by constantly assessing its accuracy and tweaking it accordingly.


NASNet is the most accurate image recognition system ever created— and it was built by another AI.

At the end of the process, NASNet had become a real champ: the “child AI” had attained 82.7 percent of accuracy in recognising images of the ImageNet database. That is 1.2 percent higher than any other computer vision system; NASNet is also 4 percent more efficient than any man-made AI system. The technology was also deployed on COCO— a database for object detection tasks— returning similarly high levels of accuracy.

“We suspect that the image features learned by NASNet on ImageNet and COCO may be reused for many computer vision applications,” Google Research wrote in a blog post.“We hope that the larger machine learning community will be able to build on these models to address multitudes of computer vision problems we have not yet imagined.”

Image via Google Research


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