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To advance, robots need artificial intelligence

Robots coupled with artificial intelligence provide the promise to reinvent industries. The reason why this has not happened to any great extent so far is because of prohibitive costs for artificial intelligence development. This, coupled with the need for more high-powered computing and a shortage of data sets needed to train artificial intelligence algorithms, has hampered advances in robotics.

In terms of delivering a breakthrough in the ‘smart robotics’ field, two artificial intelligence have deployed data scientists to accelerate development, these two companies are Neuromation and TRA Robotics. The startups have come together to remove the roadblocks and pave the way for a series of innovation in autonomous, intelligent, and affordable robots.

The aim is for these machines to assist businesses in performing a range of tasks faster and with greater accuracy. Commenting on this, Rodion Shishkov, CEO of TRA Robotics said: “To transform a robot from dumb to artificially smart, you must first train its mechanical brain, the AI algorithms in its operating software, with immense amounts of data. Data is the lifeblood of the robotics industry.”

Getting hold of sufficient data is key, since this data is necessary in order to effectively train the artificial intelligence algorithm so that the robot can perform complex tasks. Even for straightforward businesses tasks, the volume of data required by an algorithm needs to be made up of millions of images (for a robot’s optics) and equivalent number of sound files (for the audio function of the robot).

This is where Neuromation have contributed to the partnership. The company has developed a pool of synthetic (artificial) data. These data are held securely on a cloud-connected, blockchain network. By being synthetic the data mimics real data closely enough so that the artificial intelligence algorithms can continue to learn specific and complex tasks.

There have also been improvements to the way that robots learn. This has been termed a reinforcement learning platform, which is based on behavioral psychology. This approach enables the algorithms to learn from their mistakes, with the net effect of performing assigned tasks more consistently.

The startups are calling their approach to machine learning Robotics 3.0. This is due to the novel approach and to the distributed computational power that is made available.

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Written By

Dr. Tim Sandle is Digital Journal's Editor-at-Large for science news. Tim specializes in science, technology, environmental, business, and health journalism. He is additionally a practising microbiologist; and an author. He is also interested in history, politics and current affairs.

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