For the past decade, electric cars have been the next big thing in the auto world. Now, self-driving cars are starting to steal that thunder.

In recent years Artificial Intelligence (AI) has rapidly gone from an obscure academic research field, to an ever more useful and ubiquitous applied discipline. We increasingly rely on AI for more and more of our everyday tasks, and whole lines of work are being thoroughly transformed by its advances. AI's increasing ubiquity is not making it any easier to understand. AI concepts and techniques are still domain of advanced undergraduate or graduate school level courses. There are a few popular AI books out there, but most of them don't get "under the hood" of how AI actually works. "Artificial Intelligence for Humans, Volume 2: Nature-Inspired Algorithms" is the second volume in the series of short introductions to the general field of modern Artificial Intelligence.

Maciej Ceg?owski's (previously) speech at the Library of Congress, "Deep-Fried Data," describes the way that data begs to be analyzed and how machine learning is like a deep-fat fryer -- a fryer makes anything you put in it "kind of" delicious, and machine learning "kind of" finds insights in your data-set. But unless you know what your food is being fried in, you have no idea what's actually happening to it. And unless you know what data is used to train your machine-learning system, you can't know if it's finding real insight or just serving as a "money-laundry for bias." Ceg?owski does a great job on the structural limits and seductive appeal of machine learning, but then moves on to how archivists and librarian can use large data-sets, and the weird problems of providing data to strangers who use it in ways you may find disturbing or frivolous, or just inexplicable. From here, Ceg?owski talks about where data-sets to analyze can come from -- whether you can work with companies addicted to the surveillance business-model and keep your ethics intact -- and what good archiving practice should be in an era of dynamic documents served to rapidly obsoleted technologies.

Watson's capabilities are now available via the cloud-powering services in the health care, financial services, retail and education markets.?Image credit: IBMWatson, a cognitive system technology platform that combines machine learning, natural language processing akin to a human's, and statistical analysis, was created over a four-year period specifically to compete in television favorite Jeopardy! Algorithmic techniques used include computational linguistics, reasoning, and machine learning and data retrieval. Watson, as a concept, was born in 2006, a full five years before its Jeopardy! Within IBM, executives were mulling over the company's next demonstration projects involving human vs. machine. Past challenges included the Blue Gene supercomputer and Deep Blue--the computer that beat Garry Kasparov at chess.

A consortium of Silicon Valley's biggest names are uniting around one common cause. Google, Facebook, IBM, and Microsoft are launching a new non-profit dedicated to developing the public's understanding of artificial intelligence as well as establishing ground rules for researchers to use in future projects, reported The Guardian. The name of the venture is the Partnership on Artificial Intelligence to Benefit People and Society. Its board will have equal representation with members coming from the corporate and non-corporate sectors. As a scientific community, we are still a long way from being able to do things the way humans do things, but we're solving unbelievably complex problems every day and making incredibly rapid progress," said Amazon's Director of Machine Learning Science and Core Machine Learning Ralf Herbrich in a statement. "This partnership will ensure we're including the best and the brightest in this space in the conversation to improve customer trust and benefit society.

Hello there, I know there are a ton of ML tools for Python SciPy, Sklearn, Pandas, Numpy etc. A lot of which I am currently using for my Machine Learning Nano degree on Udacity. However I do like the c style syntax available in C# and I was wondering if there is rich set of tools available for this language as well.

Doctors will one day be able to more accurately predict how long patients with fatal diseases will live. Medical systems will learn how to save money by skipping expensive and unnecessary tests. Radiologists will be replaced by computer algorithms. These are just some of the realities patients and doctors should prepare for as "machine learning" enters the world of medicine, according to Dr. Ziad Obermeyer, an assistant professor at Harvard Medical School, and Dr. Ezekiel Emanuel of the University of Pennsylvania, who recently coauthored an article in the New England Journal of Medicine on the topic. But what exactly is "machine learning"? And how will medical systems make use of it?

The use of artificial intelligence is more widespread in the law than in other sectors, and IT chiefs see more applications for it in the future, a survey has found. It said that 55% of senior IT decision makers in law firms have adopted predictive coding and 48% machine learning technologies, compared to 30% and 38% respectively in non-legal sectors. According to the study of 100 IT specialists in law firms and 100 in other industries conducted by IT company CenturyLink, 76% of legal chief information officers (CIOs) believed that AI would be capable of operating without supervision within the next decade, compared to 60% of non-legal CIOs. Legal CIOs also have a firm understanding of liability that coincides with the adoption of AI technology, it said – 73% of legal CIOs believed that machines would eventually be held liable for their own errors, compared to just 47% of non-legal CIOs. However, legal IT staff were also more conscious of possible problems caused by AI, with 62% citing concerns over errors in any work performed by artificial intelligence and automation systems.

AI is hot, I mean really hot. Consumer companies like Google and Facebook also love AI, with notable apps like Newsfeed, Messenger, Google Photos, Gmail and Search leveraging machine learning to improve their relevance. As a founder of an emerging AI company in the enterprise space, I've been following these recent moves by the big titans closely because they put us (as well as many other ventures) in an interesting spot. How do we position ourselves and compete in this environment? In this post, I'll share some of my thoughts and experiences around the whole concept of AI-First, the "last mile" problems of AI that many companies ignore, the overhype issue that's facing our industry today (especially as larger players enter the game), and my predictions for when we'll reach mass AI adoption. A few years ago, I wrote about the key tenants of building Predictive-First applications, something that's synonymous to the idea of AI-First, which Google is pushing.

Isn't pop music written by robots already? But really: Researchers at Sony's Computer Science Laboratory in Paris have shared a pair of tracks created with the assistance of software called Flow Machines. The program analyzes a database of existing songs to "learn" musical styles and identify commonalities, then "[exploits] unique combinations of style transfer, optimization, and interaction techniques" to synthesize original music. Researchers can tailor the process to produce tunes that sound like the work of a particular artist--for example, "Daddy's Car," which is intended to emulate the style of the Beatles: Shadow," is fashioned in the style of Great American Songbook composers like Irving Berlin and Duke Ellington: Neither of these songs were entirely composed by artificial intelligence, nor did a computer write the words. As a post from the Flow Machines blog explains, French musician Benoît Carré "arranged and produced the songs, and wrote the lyrics."