Let's be clear here: What Musk is proposing may be far-fetched, but it's a response to a very real problem that's going to affect a lot of people in the near future. Jobs that involve predictable manual labor are in danger of becoming obsolete. McKinsey & Company estimates that about 78 percent of those types of jobs (along with 69 percent of data processing and 64 percent of data collecting) could become completely automated. Driving-related jobs are likely to become increasingly automated as self-driving technology improves, and given that those were the most common jobs in 29 states as of 2014, we should absolutely be focused on finding a solution. But Musk has also floated a much simpler--and more realistic--solution.

Overwrought love letters began turning up on the notice board at the University of Manchester's computer lab in August, 1953. Dripping with lustful vocabulary, they were all variations on a basic syntactic template: "YOU ARE MY [adjective] [noun]. And the signatory was always the same: "M.U.C.," for the Manchester University computer, a Ferranti Mark 1, the world's first general-purpose and commercially available machine of its kind. But the real author of the letters (in the first instance, anyway) was Christopher Strachey, a pioneering programmer. As he confessed in an article the following year, "There are many obvious imperfections in this scheme (indeed very little thought went into its devising), and the fact that the vocabulary was largely based on Roget's Thesaurus lends a very peculiar flavor to the results." For Strachey, though, the interesting thing was how a simple setup, using only about seventy base words, could produce a combinatorial explosion of results--on the order of three hundred billion different letters. The lovelorn user could run the program over and over until his fingers seized up, and never see the same letter twice. Strachey was something of an outlier, according to Martin Campbell-Kelly, a historian of computing at the University of Warwick. While scientists and mathematicians of the day typically used computers strictly for numerical calculations, like analyzing weapons trajectories or seeking prime factors of huge numbers, his fascination was with non-numerical computations--what soon became known as artificial intelligence. "Strachey grabbed hold of that much more than anybody else," Campbell-Kelly told me. The results were not always lovey-dovey. Besides training the Mark 1 to churn out billets-doux, he also taught it to play checkers ("draughts," in British parlance). If M.U.C.'s opponent made too many mistakes, it would get crotchety and print out a reprimand: "I refuse to waste any more time.

Quasi-supervised Learning (QSL) computes the posterior probabilities of two classes at the data points in Z. The class labels are stored in the vector Y. k denotes the number of points to be included in the reference set for exhaustive nearest neighbor classifications, and D is the symmetrical matrix of pairwise distances of the points in Z. In order to use qsl function, you need to add kstar.R function into your workspace.

My most recent post about analyzing my university's gym crowdedness over the last year using machine learning generated a lot of great responses -- including: First, I'm sorry you feel that way about the gym and I sincerely hope the oncoming wave of New Years Resolutioners doesn't completely crush your dreams of working out forever. I'm in the process of creating more predictive models for the other ten campus locations Packd tracks. Machine learning requires a lot of data, so it may take some time before the models are ready for training. I've since realized this requires a lot more explanation, and it's the subject of this post. My process may not be the best, but hopefully by illustrating it, I can learn from my mistakes and give you a starting point.

IBM Watson has a new job: Cybersecurity specialist. At the RSA Conference…IBM announced the availability of Watson for Cyber Security, with the aim of assisting cybersecurity professionals with threat assessment and mitigation…The company said it is the industry's first augmented intelligence technology with the ability to power cognitive security operations centers (SOCs). But what need does Watson fill here?

What are the most important unanswered questions in natural science that are likely to be answered by 2025? What are the most important unanswered questions in natural science that are likely to be answered by 2025? It's difficult to say, partly as it's hard to know which questions are within our reach and partly because it's impossible to say which questions are the "most important". I personally think the greatest unanswered questions lie within the human brain. Specifically, we still have little understanding of the connection between the different levels of brain hierarchy: from detailed molecular mechanisms to the understanding of whole neuron cells, to entire neural networks comprising billions of cells and inter-neuronal connections.

We are going to move towards a world where I open an app where I talk to my device and I get an answer. I ask a question and I get an answer. I don't do a lot of work, I don't navigate too much. I'm not given too many options.That's going to be fueled by A.R., in particular, and I think A..I is going to help make a world where you're sort of spending less time being inundated with sort of content and noise and more time sort of focusing on quality and the answers... There might be a moment when Tinder is just so good at predicting the few people that you're interested in, and Tinder might do a lot of the leg work in organizing a date, right?

The insideBIGDATA Guide to Deep Learning & Artificial Intelligence is a useful new resource directed toward enterprise thought leaders who wish to gain strategic insights into this exciting area of technology. In this guide, we take a high-level view of AI and deep learning in terms of how it's being used and what technological advances have made it possible. We also explain the difference between AI, machine learning and deep learning, and examine the intersection of AI and HPC. We present the results of a recent insideBIGDATA survey, "insideHPC / insideBIGDATA AI/Deep Learning Survey 2016," to see how well these new technologies are being received. Finally, we take a look at a number of high-profile use case examples showing the effective use of AI in a variety of problem domains.

For years, marketing was considered more art than science. But more recently, as marketing automation software has proliferated, marketers have had to blend the art of storytelling with the science of data. Then along comes artificial intelligence (AI) and machine learning, which promise to help marketers make sense of all that data. Some experts believe AI's impact on marketing will be hugely significant, that it could even change the nature of marketing entirely -- enabling brands to break through the noise and deliver a more personalized experience to customers. Not surprisingly, though, there are challenges ahead for organizations seeking to add AI to their marketing technology stack.

Yahoo, model Apache Spark citizen and developer of CaffeOnSpark, which made it easier for developers building deep learning models in Caffe to scale with parallel processing, is open sourcing a new project called TensorFlowOnSpark. The pairing of Spark and TensorFlow should make the deep learning framework more attractive to developers who are creating models that need to run on large computing clusters. For those that zoned out during the big-data boom, Apache Spark is an open source framework designed to increase the efficiency of parallel computing. Following in the steps of tools like Hadoop, Spark made it possible for companies like Netflix to process huge amounts of user data to offer up recommendations at scale. Machine learning frameworks like Google's TensorFlow and Caffe help people create deep learning models without the rigorous skill-set of a machine learning specialist.