It's hard enough for non-technical users to deal with ransomware infections: understanding public-key cryptography, connecting to the Tor anonymity network and paying with Bitcoin cryptocurrency. A new malicious program now makes it even more difficult by completely locking victims out of their computers. The new Petya ransomware overwrites the master boot record (MBR) of the affected PCs, leaving their operating systems in an unbootable state, researchers from antivirus firm Trend Micro said in a blog post. The MBR is the code stored in the first sectors of a hard disk drive. It contains information about the disk's partitions and launches the operating system's boot loader.

Zevenbergen Capital Investments Llc decreased its stake in Criteo S.A. (NASDAQ:CRTO) by 37.62% based on its latest Q4 2015 regulatory filing with the SEC. Zevenbergen Capital Investments Llc sold 276,870 shares as the company's stock rose 8.32% with the market. The institutional investor held 459,180 shares of the advertising company at the end of Q4, valued at 18.18 million, down from 736,050 at the end of the previous reported quarter. Zevenbergen Capital Investments Llc who had been investing in Criteo S.A. since many months, could be less bullish the 2.52 billion market cap company. The stock increased 1.72% or 0.67 on March 24, hitting 39.73.

Some very intelligent people – including Stephen Hawking, Elon Musk and Bill Gates – seem to have been seduced by the idea that because computers are becoming ever faster calculating devices that at some point relatively soon we will reach and pass a "singularity" at which computers will become "more intelligent" than humans. Some are terrified that a society of intelligent computers will (perhaps violently) replace the human race, echoing films such as the Terminator; others – very controversially – see the development of such technologies as an opportunity to evolve into a "post-human" species. Already, some prominent technologists including Tim O'Reilly are arguing that we should replace current models of public services, not just in infrastructure but in human services such as social care and education, with "algorithmic regulation". Algorithmic regulation proposes that the role of human decision-makers and policy-makers should be replaced by automated systems that compare the outcomes of public services to desired objectives through the measurement of data, and make automatic adjustments to address any discrepancies. Not only does that approach cede far too much control over people's lives to technology; it fundamentally misunderstands what technology is capable of doing. For both ethical and scientific reasons, in human domains technology should support us taking decisions about our lives, it should not take them for us. At the MIT Sloan Initiative on the Digital Economy last week I got a chance to discuss to discuss some of these issues with Andy McAfee and Erik Brynjolfsson, authors of "The Second Machine Age", recently highlighted by Bloomberg as one of the top books of 2014. Andy and Erik compare the current transformation of our world by digital technology to the last great transformation, the Industrial Revolution.

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

Just like nuclear physics, machine learning, AI, and data science can be used either for the better of for the worse. You can make either useful energy or terrible bombs using nuclear fission. The same applies to machine learning, and in my example below, it gets even worse than Hiroshima or Nagasaki. Here I am discussing a potential use of machine learning in military operations. The scenario below is entirely hypothetical.

Google's artificial intelligence (AI) ethics board, established when Google acquired London AI startup DeepMind in 2014, remains one of the biggest mysteries in tech, with both Google and DeepMind refusing to reveal who sits on it. Google set up the board at DeepMind's request after the cofounders of the 400 million research-intensive AI lab said they would only agree to the acquisition if Google promised to look into the ethics of the technology it was buying into. Business Insider asked Google once again who is on its AI ethics board and what they do but it declined to comment. A number of AI experts told Business Insider that it's important to have an open debate about the ethics of AI given the potential impact it's going to have on all of our lives. Artificial intelligence is the field of building computer systems that understand and learn from observations without the need to be explicitly programmed, as defined by Nathan Benaich, an AI investor at venture capital firm Playfair Capital.

Major portions of this series of posts are excerpts from my new book Augmented: Life in the Smart Lane. I also asked my Facebook followers if there were any questions they'd like answered about AI here, and I've tried to incorporate answers to those questions into this series of posts also. Deep learning is a term we're increasingly using to describe how we teach Artificial Intelligence (AI) to absorb new information and apply it in their interactions with the real world. In an interview with the Guardian newspaper in May 2015, Professor Geoff Hinton, an expert in artificial neural networks, said Google is "on the brink of developing algorithms with the capacity for logic, natural conversation and even flirtation." Google is currently working to encode thoughts as vectors described by a sequence of numbers.

TOKYO Advances in artificial intelligence are blurring the line between humans and robots. As robots interact ever more closely with us, new ethical questions are emerging related to issues from violence to sex and privacy. In February, a video uploaded to YouTube by Boston Dynamics, an American robot developer, sparked controversy. Some viewers were apparently shocked by a scene in which a man knocks down a box that was being lifted by a two-legged humanoid robot, developed by the company, and another scene in which the man knocks the robot down from behind with a stick. "Stop bullying robots," one viewer commented below the video.

The details of the project are revealed in a research paper titled "A Neural Algorithm of Artistic Style." "Here we introduce an artificial system based on a Deep Neural Network that creates artistic images of high perceptual quality," reads the paper, penned by a group of researchers from the University of Tubingen in Germany. "The system uses neural representations to separate and recombine content and style of arbitrary images, providing a neural algorithm for the creation of artistic images." Using an image of a street in Germany (above), the team demonstrated the ability to recreate the photo using the visual styles of Vincent van Gogh, Edvard Munch and Pablo Picasso. Each transformed street photo, at least upon casual inspection, looks like it was indeed painted by one of the masters. Over the last couple of days, Andrej Karpathy, a PhD student at Stanford studying Machine Learning, posted a few experiments on Twitter using the technique, and the results were incredibly accurate. Karpathy had since removed all examples of his experiments with the image transfer, but we managed to capture some screenshots of his tests, including one that turned a photo of Gandalf into a Picasso-style portrait. Does this mean that, with a little more code tweaking, we'll begin to see fake works (created using this technique) suddenly "discovered" in coming years?

An important step in data analysis is data exploration and representation. In this tutorial we will see how by combining a technique called Principal Component Analysis (PCA) together with Cluster Analysis we can represent in a two-dimensional space data defined in a higher dimensional one while, at the same time, being able to group this data in similar groups or clusters and find hidden relationships in our data. More concretely, PCA reduces data dimensionality by finding principal components. These are the directions of maximum variation in a dataset. By reducing a dataset original features or variables to a reduced set of new ones based on the principal components, we end up with the minimum number of variables that keep the maximum amount of variation or information about how the data is distributed. If we end up with just two of these new variables, we will be able to represent each sample in our data in a two-dimensional chart (e.g. a scatterplot). As an unsupervised data analysis technique, clustering organises data samples by proximity based on its variables.