Nand Kishor Contributor

Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... ...

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Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...

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New Breakthrough Could Put Artificial Intelligence Inside Your Kitchen

By Nand Kishor |Email | Feb 15, 2018 | 4740 Views

The time is not so far when the coffee maker could possess enough artificial intelligence (AI) to learn a customer's preferred drink for a certain time of day. AI can soon make appliances do these wonders offline, without relying on processing input and outputs via the cloud.

Researchers at MIT have developed a special chip that makes it possible to run a powerful neural network while drastically reducing the power consumption of all data processing. That means smartphones and even appliances and smaller Internet of Things (IoT) devices could run neural networks locally.

The secret of the new chips comes from taking the normal process of moving data back and forth between memory and processors in a chip, whilst also, reducing it down to a single operation called the ??dot product'.

"Our approach was, can we implement this dot-product functionality inside the memory so that you don't need to transfer this data back and forth?" explained Avishek Biswas, MIT graduate, who led to the new chip's development. The new chip is explained in a paper written by Biswas which he is presenting at the International Solid State Circuits Conference, San Francisco. 

The researchers say it can increase the speed of neural net computations by up to 700 percent while reducing power consumption by 94 to 95 percent.

"The results show impressive specifications for the energy-efficient implementation of convolution operations with memory arrays," said Dario Gil, VP of AI at IBM.

"It certainly will open the possibility to employ more complex convolutional neural networks for image and video classifications in IoT in the future", he added

Due to A.I., it is no sooner when door knobs and locks will identify who is at door and decide whether or not to unlock themselves or not. It all raises one most important question of whether A.I. can ever truly grasp the concept of a "frenemy" and how to handle it.

Source: HOB