'Google' gave up to the challenge of finding the meaning of my name, like most other sources I conferred with. And here I'm left with a keen hope that it gets discovered someday, to mean something as creative as my notions of it… The green land to the tail of India- Kerala, "The God's own Country", is where I live. I blew off my engineering degree to become a writer and have not regretted it a bit. Am no builder or a designer, but I do believe that I can create my world with my imaginations, pen, paper and of course, Microsoft Word and that is why I am here …'coz, I write…!
Machine Learning, thinking systems, expert systems, knowledge engineering, decision systems, neural networks - all synonymous loosely woven words in the evolving fabric of Artificial Intelligence. Of these Machine Learning (ML) and Artificial Intelligence (AI) are often debated and used interchangeably. In very abstract terms, ML is a structured approach for deriving meaningful predictions/insights from both structured and unstructured data. ML methods employ complex algorithms that enable analytics based on data, history and patterns. The field of data science continues to scale new heights enabled by the exponential growth in computing power over the last decade.
It was just a few weeks ago that Sophia, an artificial intelligence (AI)-powered humanoid robot developed by Hong Kong-based Hanson Robotics, was given honorary citizenship by Saudi Arabia. While AI is making huge inroads into our day-to-day life, was this something expected to happen so soon? Meanwhile, even though the popular perception is that AI is only going to be a job killer, reports also say that 2020 onwards, AI will start adding more jobs than it would take away. According a report by research firm Gartner, Inc., AI will create 2.3 million new jobs while eliminating only 1.8 million jobs in 2020. At the recently-held Wall Street Journal CEO Council meeting in Washington DC, I was fortunate enough to listen to two eminent futurists and authors--Martin Ford and Jerry Kaplan--who are known for their pioneering work in the field of AI.
I've been asked to recommend a good book on big data that's not Big Data (which I coauthored with Viktor Mayer-Schönberger, images below). It's a hard question for several reasons. First, though I'm usually brutally critical of my work, it's not a bad book. Yet it's also a difficult question because there are so many good books to chose from. The question becomes: which book is right for whom?
In an era of great uncertainty and disruption for automotive manufacturers, Mercedes and its parent company Daimler are jumping in full throttle as leaders of the 4th Industrial Revolution. Not only are they designing new vehicles, but their services, influence in the transportation industry and factories are transforming to embrace the new opportunities and demands of their customers. Other companies should follow their lead to thrive in the new industrial revolution. What is the 4th Industrial Revolution? Often referred to as industry 4.0, the 4th Industrial Revolution is the shift to smart factories that use a combination of cyber-physical systems, the Internet of Things and the Internet of Systems to connect the entire production chain and make decisions on its own.
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... ... 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... So, what is machine learning anyways?
A mere mention of Artificial Intelligence would have our heads spinning, thinking about the latest sci-fi movie, robots conspiring to overtake human existence, space travels and whatnot. However, outside the realms of science fiction, the technology has been majorly helping organizations sift through heavy data sets to discover anomalies and best practices. By taking into account enormous data points, organizations can not only envision the future with a certain degree of certainty but also be better prepared for the same. Supply chain is perhaps the most data-rich environment in the businesses of today. The chain supports an open flow of data from a diverse set of sources.
Few things have fundamentally reshaped how companies overcome business challenges than the application of machine learning in the market. Today's companies, from the tech behemoths of Silicon Valley to the eager entrepreneurs cropping up in cities nation-wide, all exploit machine learning to cut cost and get better results. This widespread adoption of machine learning has consequences; big data isn't an easy beast to tame, and companies today are facing serious challenges when it comes to keeping their data management systems up to date with rapidly evolving algorithms. So how exactly is machine learning fueling a revolution in big data management, and what are today's wisest companies doing to find solution to their big data problems? A quick review of the evolution of big data management shows how machine learning has already driven serious change within the field, and how that change is just getting started.
China remains the manufacturing powerhouse of the world, but many of its leading players are facing challenges such as overbuilt capacity and weak demand. Revenue growth has slowed, and profitability has stagnated and in some cases declined. While China enjoys some advantages such as mature manufacturing bases, fiscal support, a large base of tech-savvy consumers and more platform players, it also has hurdles such as increasing labour and material costs. Plus, the piecemeal deployment and implementation of investments in digital technologies hinder the ability of Chinese businesses to innovate with connected and intelligent products. Recognising the challenges, in 2015 China launched "Made in China 2025" as part of a road map for the country's latest industrial modernisation.
While advancements in big data analytics have done a good job at helping marketers target mass markets and people of like interests, they fall short of understanding a person's unique interests and going that extra mile of treating people like individuals. While there is no argument that people have overlapping interests, there is a false assumption that just because a person falls into a certain category (i.e. A person will express hundreds of different interests that extend beyond any given category; this is what makes a person unique. Big data approaches don't treat people like individuals. Instead, they tend to bucket people into broad categories.