BUSINESS


Decoding Machine Learning Methods

#artificialintelligence

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.


Bharti Airtel vows to give Rs 7000 cr to start university teaching AI for free to poor children

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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... ... 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?


How Artificial Intelligence transforms organization at work - Julie Desk

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Data collection, and their analysis with the help of Artificial Intelligence are making work more productive and efficient each day. But soon AI will become more autonomous and more demanding. Not a day passes without progress in the artificial intelligence sector as it slowly becomes mainstream. There are those who are alarmed the whole thing will bring about unemployment for all. One French series, Terpalium, describes a dystopian future where AI has led to a situation in which an elite few enjoy all the joys of modern life while most of the population is in a vegetative state of misery and laziness.


How Machine Learning Is Changing Big Data Management 7wData

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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.


chinese-factories-must-bet-big-digital-technology-industrial

@machinelearnbot

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.


Singapore C2C marketplace turns to AI to combat fraud, improve UX

ZDNet

Carousell believes the focus on artificial intelligence (AI) needs to move past the hype and on how companies can actually adopt it to gain real business benefits. In particular, the Singapore-based consumer-to-consumer (C2C) online marketplace was looking to machine learning and AI to combat fraud as well as improve user experience. Carousell CTO and Co-Founder Lucas Ngoo said establishing user trust was critical for the site, on which buyers would purchase goods from individual sellers they did not personally know or were not backed by big brand names. The company began exploring the use of machine learning less than a year go, tapping TensorFlow and Google's Cloud Machine Learning engine to identify and flag potential fraud risk. For example, the software would be able to highlight an individual who sent out multiple requests to different Carousell users, asking them to leave the site's chat platform to communicate.


AI's Cool New Thing: Capsule Networks (explained)

#artificialintelligence

There's a buzz in AI circles around "capsule networks," a new variant on neural networks that backers say could simplify, cut the costs of, commoditize and, in the end, democratize how deep learning systems are taught to do what we want them to do. How can it do all this? Capsule networks hold out the hope of tacking one of the biggest problems in AI: radically reducing the amount of data (and compute) needed to train deep learning systems. This in turn means AI could become available to the broader market, no longer consigned to a few companies with mammoth compute resources and infinite volumes of data – i.e., the FANG* companies. In fact, a FANG company, Google, is the father of capsule networks.


Getting started with AI and ML: eight projects to consider

#artificialintelligence

Analyst firms such as Gartner and IDC predict 2018 will see a sharp rise in the number of organisations deploying AI technologies as the benefits become more apparent. Gartner reported recently that 59 percent of organisations have started gathering information to build an AI strategy. By 2020, it estimates 30 percent of CIOs will have AI as one of their top five investment priorities. And, IDC predicted in April that global revenues for AI systems would reach US$12.5 billion this year, up almost 60 percent from 2016. By 2020, it expects that figure will hit as high as US$46 billion.


The Evolution of IT Operations Analytics – Trace3

@machinelearnbot

IT Operations Analytics (ITOA) is the practice of utilizing data science principles to perform pattern discovery, correlation, anomaly detection, and root cause analysis against data collected from underlying infrastructure and applications. To fully appreciate the value proposition of ITOA, you must go back in time to witness the transformation and evolution of Operations. In the beginning, there was chaos. Organizations used best effort to guarantee services were available for consumers. However, the lack of visibility into how these services were operating increased operational cost and risk often resulting in poor customer satisfaction.


Amazon unveils AI consulting program for cloud customers

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Amazon Web Services wants to make it easier for people to get the most out of machine learning in the cloud, and it just unveiled a new consulting program that's aimed at helping folks get off the ground. Called the Amazon ML Solutions Lab, the program will provide customers with access to machine learning experts from Amazon who can help them tackle business problems using purpose-built intelligent models. It's designed to help businesses without extensive machine learning expertise get their problems solved using the latest systems, tailored to tackle particular problems. While machine learning systems built with the latest techniques can address issues computers were previously unsuited for, building them requires extensive expertise. People with the sort of knowledge and talent necessary are in high demand, so it can be hard for companies to build ML teams in-house.