Opinion


Meet the Netflix of Big Data & Data Science

@machinelearnbot

KDnuggets has the 7 Steps and Key Terms, Explained series (among others), as well as numerous in-house one-off tutorials and opinion pieces covering a wide variety of machine learning, data science, Big Data and AI topics, such as 10 Free Must-Read Books for Machine Learning and Data Science and Machine Learning overtaking Big Data? Netflix finds quality content from elsewhere and runs it as guest programming in order to increase reach, just as KDnuggets does with republished guest posts. Netflix syndicates quality content originally found elsewhere, which has allowed so very many British shows to make their way to North America for mass consumption, as but one example of this. KDnuggets also runs quality tutorials, overviews, and opinion pieces from other blogs and sites around the web, in order to increase their exposure.


Awesome Deep Learning: Most Cited Deep Learning Papers

@machinelearnbot

We believe that there exist classic deep learning papers which are worth reading regardless of their application domain. Rather than providing overwhelming amount of papers, We would like to provide a curated list of the awesome deep learning papers which are considered as must-reads in certain research domains.


Ashby: Artificial intelligence already displaying the flaws of its inventors

#artificialintelligence

It's a question posed by the "partnership on AI" formed by major American technology firms. The goal of the partnership, which includes Google, IBM, Microsoft and Facebook, is to "conduct research, recommend best practices, and publish research under an open license (sic) in areas such as ethics, fairness and inclusivity; transparency, privacy, and interoperability; collaboration between people and AI systems; and the trustworthiness, reliability and robustness of the technology." That's the type of artificial intelligence that technology companies investing in machine learning have focused on. As Microsoft researcher and MIT professor Kate Crawford pointed out recently in The New York Times, "Sexism, racism and other forms of discrimination are being built into the machine-learning algorithms that underlie the technology behind many'intelligent' systems that shape how we are categorized and advertised to," thanks to the overwhelmingly homogenous hiring patterns in major technology firms.


Is it ok to speak ill of the dead?

Los Angeles Times

To the editor: Ann Friedman's opinion piece touched home. To the editor: Friedman's piece defending badmouthing of the dead is just more proof of the hypocrisy of liberals. Bayer buying Monsanto, Uber's self-driving cars, Cup of noodles is changing its recipe, new battle over child vaccination, an Emmys preview, the new Chevy Bolt, Ryan Lochte's "Dancing with the Stars" premiere gets overshadowed by protesters. Bayer buying Monsanto, Uber's self-driving cars, Cup of noodles is changing its recipe, new battle over child vaccination, an Emmys preview, the new Chevy Bolt, Ryan Lochte's "Dancing with the Stars" premiere gets overshadowed by protesters.


o EDITION

#artificialintelligence

In my opinion, the marriage of the leading professional social network and the world's largest software company demonstrates that we are decidedly at the start of a new era in software, where proprietary data is king, and will start to come bundled together with software. We've seen this rise in the consumer realm, where technology companies are fundamentally aggregating and analyzing user behavior, and providing value back to users (and, of course, advertisers.) There are countless other examples that also demonstrate that consumer technology puts behavioral and user data front and center, in a way that I expect we will start to see from the enterprise as the divide between these two segments starts to collapse. Taken together, this demonstrates that proven machine learning algorithms have both the horsepower and access to granular datasets that are unprecedented.


The method to Microsoft's madness with LinkedIn deal

#artificialintelligence

In my opinion, the marriage of the leading professional social network and the world's largest software company demonstrates that we are decidedly at the start of a new era in software, where proprietary data is king, and will start to come bundled together with software. We've seen this rise in the consumer realm, where technology companies are fundamentally aggregating and analyzing user behavior, and providing value back to users (and, of course, advertisers.) There are countless other examples that also demonstrate that consumer technology puts behavioral and user data front and center, in a way that I expect we will start to see from the enterprise as the divide between these two segments starts to collapse. Taken together, this demonstrates that proven machine learning algorithms have both the horsepower and access to granular datasets that are unprecedented.


Raja-Mandala: India, US and Artificial Intelligence

#artificialintelligence

How the leading powers mobilise and deploy these technologies will shape the balance of economic and military power among them in the coming decades. Carter has been driving the Pentagon to leverage AI to America's strategic advantage over rivals like China and Russia as well as sophisticated terror groups like the Islamic State. Unlike in the earlier military revolutions, India has some capabilities and knowledge in the emerging AI sector. Effective use of these will help India accelerate its own economic growth, address its national security challenges and gain an effective voice in the international regulation of autonomous weapons and robotic warfare.


The Data Science Puzzle, Explained

#artificialintelligence

I think that, while there may be an awful lot of opinion pieces defining and comparing these related terms, the fact is that much of this terminology is fluid, is not entirely agreed-upon, and, frankly, being exposed to other peoples' views is one of the best ways to test and refine your own. The reader is encouraged to compare this Venn diagram with Drew Conway's now famous data science Venn diagram, as well as my own discussion below and modified process/relationship diagram near the bottom of the post. There are all sorts of articles available defining big data, and I won't spend much time on this concept here. The main artifacts of machine learning research are algorithms which facilitate this automatic improvement from experience, algorithms which can be applied in a variety of diverse fields.