China Leads in Highly Cited AI Papers

China AI Development Report 2018 (p. 20f.): Using the Web of Science database, this report puts China in the lead ahead of the US and any European country, both for the decade between 2007 and 2017 and in terms of annual output in 2017. Aggregating all European countries out of the top 10 from 2007 to 2017 (the UK, Germany, France, Italy, Spain), they almost catch up to the US and China, reaching 2,096 highly cited papers, compared to 2,241 and 2,349, respectively. The same applies to “hot papers.” Unfortunately, I do not know how they operationalized either “highly cited” or “hot.” Eyeballing combined European output in 2017 alone; China is still in the lead with an increasingly large margin, ahead of the US and Europe, which seem to be roughly tied.

Source: Stefan Torges

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How to Compete with Amazon

Chris Lynch, CEO, AtScale

Chris Lynch, CEO, AtScale

Data is eating the world and Amazon got it very early on. Twenty years ago, Jeff Bezos wrote in his letter to the shareholders of his $1.64 billion company: “We are doubly-blessed. We have a market-size unconstrained opportunity in an area where the underlying foundational technology we employ improves every day.”

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2020 Predictions for the Internet of Things (IoT)

IDC 2020 predictions for the Internet of Things (IoT):

In 2020, 90% of organizations will have determined key performance indicators to measure the success of their IoT projects.

By 2021, 75% of organizations embarking on an IoT project will work with a systems integrator to strategize, plan, deploy, and/or manage the initiative.

By 2022, 70% of new enterprise IoT applications built on IoT platforms will leverage container deployment.

By 2023, 70% of enterprises will run varying levels of data processing at the IoT edge. In tandem, organizations will spend over $16 billion on IoT edge infrastructure in that time.

By 2023, 20% of cybersecurity incidents will stem from Smart City IoT device deployments, forcing double-digit increases in cybersecurity software and staff training budgets.

To lessen critical equipment failures, by 2024, 40% of manufacturers will use field asset IoT data to intelligently diagnose issues and resolve autonomously, improving unplanned downtime by 25%.

By 2023, 70% of IoT deployment will include AI solutions for autonomous or edge decision making, supporting organizations’ operational and strategic agendas.

By 2025, there will be 79ZB of data created by billions of IoT devices, causing organizations to reevaluate their data governance, retention, and usage policies.

By 2025, 60% of manufacturers will use IoT platforms with digital innovation platforms to operate networks of asset, product, and process digital twins for a 25% reduction in cost of quality.

By 2023, enterprises will struggle to manage all the different access types used to connect their IoT endpoints, with 75% adopting more than one connectivity type.

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Logistic Regression in R: A Classification Technique to Predict Credit Card Default

Logistic regression is one of the statistical techniques in machine learning used to form prediction models. It is one of the most popular classification algorithms mostly used for binary classification problems (problems with two class values; however, some variants may deal with multiple classes as well). It’s used for various research and industrial problems. Therefore, it is essential to have a good grasp on the logistic regression algorithm. This tutorial is a sneak peek from many of Data Science Dojo’s hands-on exercises from their 5-day data science bootcamp, you will learn how logistic regression fits a dataset to make predictions, as well as when and why to use it.

In short, Logistic Regression is used when the dependent variable (target) is categorical. For example:

  • To predict whether an email is spam (1) or not spam (0)
  • Whether the tumor is malignant (1) or not (0)

It is named as ‘Logistic Regression’, because it’s underlying technique is quite the same as Linear Regression. There are structural differences in how linear and logistic regression operate. Therefore, linear regression isn’t suitable to be used for classification problems. This link answers in detail why linear regression isn’t the right approach for classification.

Its name is derived from one of the core functions behind its implementation called the logistic function or the sigmoid function. It’s an S-shaped curve that can take any real-valued number and map it into a value between 0 and 1, but never exactly at those limits.

For complete tutorial click here

Sponsored by Data Science Dojo

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When Robots Will Overtake Humans

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AI by the Numbers: The Value of Augmented Intelligence

Recent surveys, studies, forecasts and other quantitative assessments of the progress of AI highlighted the role of augmented intelligence, combining human intelligence with artificial intelligence to produce better results in cybersecurity defense and in getting more business value from the use of IoT data.

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The Roomba Envisioned in 19th Century France

The Public Domain Review:

A series of futuristic pictures by Jean-Marc Côté and other artists issued in France in 1899, 1900, 1901 and 1910. Originally in the form of paper cards enclosed in cigarette/cigar boxes and, later, as postcards, the images depicted the world as it was imagined to be like in the then distant year of 2000. As is so often the case their predictions fell some way off the mark, failing to go far enough in thinking outside the confines of their current technological milieu (hence the ubiquity of propellors, not to mention the distinctly 19th-century dress).

There are at least 87 cards known that were authored by various French artists, the first series being produced for the 1900 World Exhibition in Paris. Due to financial difficulties the cards by Jean-Marc Côté were never actually distributed and only came to light many years later after the science-fiction author Isaac Asimov chanced upon a set and published them in 1986, with accompanying commentary, in the book Futuredays: A Nineteenth Century Vision of the Year 2000.

See also Wikimedia

Roomba® Robot Vacuum was introduced in 2002 and its developer, iRobot, has sold by 2019 more than 25 million robots worldwide.

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