The top 100 papers

Article from Nature

The discovery of high-temperature superconductors, the determination of DNA’s double-helix structure, the first observations that the expansion of the Universe is accelerating — all of these breakthroughs won Nobel prizes and international acclaim. Yet none of the papers that announced them comes anywhere close to ranking among the 100 most highly cited papers of all time.

Citations, in which one paper refers to earlier works, are the standard means by which authors acknowledge the source of their methods, ideas and findings, and are often used as a rough measure of a paper’s importance. Fifty years ago, Eugene Garfield published the Science Citation Index (SCI), the first systematic effort to track citations in the scientific literature. To mark the anniversary, Nature asked Thomson Reuters, which now owns the SCI, to list the 100 most highly cited papers of all time. (See the full list at Web of Science Top 100.xls or the interactive graphic, below.) The search covered all of Thomson Reuter’s Web of Science, an online version of the SCI that also includes databases covering the social sciences, arts and humanities, conference proceedings and some books. It lists papers published from 1900 to the present day.

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Machine-Learning Maestro Michael Jordan on the Delusions of Big Data and Other Huge Engineering Efforts

Big-data boondoggles and brain-inspired chips are just two of the things we’re really getting wrong

The overeager adoption of big data is likely to result in catastrophes of analysis comparable to a national epidemic of collapsing bridges. Hardware designers creating chips based on the human brain are engaged in a faith-based undertaking likely to prove a fool’s errand. Despite recent claims to the contrary, we are no further along with computer vision than we were with physics when Isaac Newton sat under his apple tree.

Those may sound like the Luddite ravings of a crackpot who breached security at an IEEE conference. In fact, the opinions belong to IEEE Fellow Michael I. Jordan, Pehong Chen Distinguished Professor at the University of California, Berkeley. Jordan is one of the world’s most respected authorities on machine learning and an astute observer of the field. His CV would require its own massive database, and his standing in the field is such that he was chosen to write the introduction to the 2013 National Research Council report “Frontiers in Massive Data Analysis.” San Francisco writer Lee Gomes interviewed him for IEEE Spectrum on 3 October 2014.

Read the interview

http://spectrum.ieee.org/robotics/artificial-intelligence/machinelearning-maestro-michael-jordan-on-the-delusions-of-big-data-and-other-huge-engineering-efforts

How Smart Are Smartphones?: Bridging the marketing and information technology gap.

My latest IEEE article (co-authored with A. Amanatiadis) is out. Please read the article and send me your comments!!

How Smart Are Smartphones?: Bridging the marketing and information technology gap.

The term "smart" has become widespread in consumer electronics in recent years, reflecting the consumers' need for devices that assist them in their daily activities. The term has a long history of usage in marketing science as one of the most appealing ways of promoting or advertising a product, brand, or service. However, even today, there is much controversy in the definition of this term and even more ambiguities for the right use in consumer electronic devices. Furthermore, it is not possible to carry out any quantitative or qualitative analysis of how smart a device is without having some adequate conception of what a smart or intelligent application means. This article attempts to explore the smart and intelligent capabilities of the current and next-generation consumer devices by investigating certain propositions and arguments along with the current trends and future directions in information technology (IT).

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6914691&sortType%3Dasc_p_Sequence%26filter%3DAND%28p_IS_Number%3A6914657%29