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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How machine learning influences your productivity

By Nand Kishor |Email | May 8, 2017 | 5655 Views

If there is one word that the enterprise wants to be associated with, it's "productive."

It is the metric that influences so many others by which business is measured - success, efficiency, profit. And recently, artificial intelligence (AI) has been touted as a new way to increase productivity by replacing expensive workers with tireless machines. One recent demonstration that has garnered media attention is the first demonstration of an autonomous big rig, the use of which could replace millions of truck drivers.

But AI has been getting a lot of undeserved limelight. Because long before machines replace us humans, they will be helping us to make smart decisions so we can become more productive - autonomous machines be damned. This use of technology is called "intelligence augmentation" and because of its imminent and extensive impact, it deserves a closer look.

For many in the enterprise, artificial intelligence (AI) vs. intelligence augmentation (IA) is a distinction without a difference. And certainly, that case can be made. In a Wall Street Journal op-ed, IBM President, Chairman and CEO Ginni Rometty points out that, whether you call them AI or IA, "these cognitive systems are neither autonomous nor sentient, but they form a new kind of intelligence that has nothing artificial about it. They augment our capacity to understand what is happening in the complex world around us."

This is absolutely true. But there is still a distinction to be made when it comes to maximizing productivity in the modern, data-diverse workplace. Applying either of these technologies to the wrong task will be counterproductive, however advanced the application might be.

The "intelligence" provided by AI technology entails tapping into increasingly cheap computer processing power to evaluate alternate options more quickly than humans. This is why AI-driven computers have been successful at playing chess, winning at go, and even playing "Jeopardy." Each of these tasks is characterized by the need to evaluate the best move from a finite set of options, however large that number of options might be. Evaluating many options and learning from past experience, using a technology called machine learning, is how artificial intelligence is able to pick the best outcome available. Read More

Source: VB