Google’s AI Creates Its Own AI That Beats The Performance Of Other Human-made Models

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this year in May, Google unveiled a new approach to A.I.
development that was called AutoML, which stood for Auto-machine
learning. As the name suggests, this approach allowed an AI to
get involved in the development of another and eliminate the need
for any human engineer.

Google calls it a means to make machine learning models more
accessible. AutoML’s controller neural net can propose “child
models” that can be further trained and evaluated. This child
AI, called NASNet, can be used to recognize objects in

After repeating this training process thousands of time and
improving NASNet, it was tested on the ImageNet image
classification and COCO object detection sets; these are two
most respected large-scale data sets in computer vision. The
results were surprising as NASNet outperformed any other
computer vision system, according to Google, as
reported by Futurism.

As per the results on ImageNet image classification, NASNet
surpassed previous models and showed a prediction accuracy of
82.7%. Moreover, it performs 1.2% better than all previously
published results. The computational costs are also very low.

google ai performance nasnetNASNet performance

The state-of-the-art predictive performance on COCO object
detection task, Google’s largest model achieved 43.1% mAP,
which is 4% better than previously published state-of-the-art.

It’s also worth noting that Google has open-sourced NASNet for
inference on image classification and detection in TensorFlow

These types of huge advancements in the field of artificial
intelligence and machine learning are the reasons we need more
stringent regulations and better ethical standards for AI. What
are your thoughts on this advancement? Share your views and
become a part of the conversation.

Also Read: This A.I. Can Tell If You’re
Gay Or Straight From Your Pictures With 91% Accuracy

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