Nobody wants a robot apocalypse. From the mechanical worker's revolt in R.U.R. (the play that gave us the word "robot") to the bleak, nuke-scarred hellscapes of the Terminator and Matrix films, the idea of humanity destroyed by tools of its own creation is compelling, if still the domain of fiction. To keep the apocalypse firmly in the realm of the speculative, today the International Committee of the Red Cross released an unusual statement for a humanitarian group: "Decisions to kill and destroy are a human responsibility." The Red Cross isn't encouraging human decisions to kill and destroy. Instead, it's arguing that if such decisions are going to be made (and little in human history suggests they won't be), then it's really important that it is actual humans with that authority and power, not lethal autonomous weapon systems.

Netflix has mysteriously been removed from the American film industry's search engine for legal streaming sites. WhereToWatch, which was set up by the Motion Picture Association of America (MPAA) in 2014, lets users search a range of legal streaming services for their favourite TV shows and films. The idea was to provide internet users with a handy resource which would steer them away from illegal streams and downloads, protecting them from potential legal trouble and helping the studios at the same time. However, as TorrentFreak reports, Netflix has been removed from the WhereToWatch search results, despite being the one of the most-used legal streaming services in the world. Netflix results have also been removed from the UK equivalent of the site, FindAnyFilm, although some Netflix results are still available on GoWatchIt, the search engine which powers WhereToWatch.

Tree-based models provide robust first-cut solutions to such data. I introduce various kinds of trees and how they are different from each other. After understanding these trees, you can build better custom models of your own.

Machines are helping people learn faster, better and more efficiently. Join us as we explore cognitive technology and learn what advances in artificial intelligence will impact analytics and demand for human interaction on the next episode of Modern Workplace, "Machine Learning: How technology advances human intelligence," airing April 12 at 8 a.m. This month, two of the industry's most forward thinkers share their predictions of where business is going with big data and how we can take advantage of the changes in technology to come. Dr. Tom Davenport, president's distinguished professor of IT & Management at Babson College, shares how analytics technology is being advanced by the progression of artificial intelligence and will discuss how to strike the right balance between man and machine. Max Wessel, vice president at Sapphire Ventures, gives the inside scoop on what current trends in technology will succeed and fail this year.

Anthony Goldbloom is the founder and CEO of Kaggle. In 2011 & 2012, Forbes Magazine named Anthony as one of the 30 under 30 in technology, in 2013 the MIT Tech Review named him one of top 35 innovators under the age of 35 and the University of Melbourne awarded him an Alumni of Distinction Award. He holds a first call honors degree in Econometrics from the University of Melbourne. Anthony has published in the The Economist and the Harvard Business Review. Title: What Kaggle has learned from 2MM machine learning models Abstract: Kaggle is a community of over 500K data scientists who have built almost 2MM machine learning models to participate in our competitions.

Remember a few weeks back, when we learned that Google's artificial neural network was having creepy daydreams, turning buildings into acid trips and landscapes into Magic Eye pictures? Well, prepare to never sleep again, because last week, Google made its "inceptionism" algorithm available to the public, and the nightmarish images are cropping up everywhere. The "Deep Dream" system essentially feeds an image through a layer of artificial neurons, asking an AI to enhance and build on certain features, such as edges. Over time, pictures can become so distorted that they morph into something entirely different, or just a bunch of colorful, random noise. Now that the code for the system is publicly available, anyone can upload a photo of their baby and watch it metamorphose into a surrealist cockroach, or whatever.

When you think about it, the tech world has a lot in common with old-time religion. There are impassioned camps -- think Android vs. iOS or Mac vs. Windows -- devoted to a particular software or hardware platform. And tech conferences that attract the faithful often embrace the fevered intensity of a tent revival. This isn't a bad thing, of course, as a little enthusiasm never hurt anybody. And it makes sense that major companies, including IBM, hire charismatic, persuasive individuals as evangelists to spread the word about their products and services.

Experts in computer science and medicine explored issues related to the ethical use of artificial intelligence in medicine during a panel discussion at the University of Toronto. The University of Toronto (U of T) hosted a panel discussion Tuesday on the ethical use of artificial intelligence (AI) in medicine. Integrating AI successfully into the nuanced setting of patient and doctor interaction and communication creates intriguing challenges for researchers. Natural language expert Graeme Hirst says a medical AI would have to talk to patients in language used in the real world and deal with all issues of complex conversation and health communication. Hirst has developed methods for detecting cognitive decline, including Alzheimer's disease, by examining linguistic changes in a person's writing over time.

Jen-Hsun Huang, CEO of chip maker Nvidia, introduces a new chip designed to make machine learning software more capable. The field of artificial intelligence has experienced a striking spurt of progress in recent years, with software becoming much better at understanding images, speech, and new tasks such as how to play games. Now the company whose hardware has underpinned much of that progress has created a chip to keep it going.

Recurrent Neural Networks (RNNs), and specifically a variant with Long Short-Term Memory (LSTM), are enjoying renewed interest as a result of successful applications in a wide range of machine learning problems that involve sequential data. I will summarize my own experience with training these models for automated image captioning and for generating text character by character, with a particular focus on understanding the source of their impressive performance and their limitations.