Once thought safe, DDR4 memory shown to be vulnerable to "Rowhammer" Physical weaknesses in memory chips that make computers and servers susceptible to hack attacks dubbed "Rowhammer" are more exploitable than previously thought and extend to DDR4 modules, not just DDR3, according to a recently published research paper. The paper, titled How Rowhammer Could Be Used to Exploit Weaknesses in Computer Hardware... Ars Technica How HTC and Valve built the Vive Long before the Vive was born, both software developer Valve and phone manufacturer HTC were separately looking into virtual reality. In 2012, VR was beginning to creep back into the public imagination. It started in May of that year, when id Software's John Carmack demoed a modified Oculus Rift running Doom 3. The following month, he took the Rift to a wider audience at the E3 games convention. By August, Palmer Luckey launched the Oculus Kickstarter campaign, and it broke records.

Hewlett Packard Enterprise Co., having backed away from a key portion of the cloud computing-on-demand market, is expanding into cloud services to help companies analyze data such as photos, audio clips and comments on social media. Haven OnDemand, which runs on computers operated by Microsoft Corp.'s Azure cloud-computing division, gives users access to sophisticated techniques such as machine learning without the need to maintain...

A report shown at the 2016 World Economic Forum in January says millions of jobs will be lost to robots in the next few years. When thinking about who is most vulnerable, factory workers, drivers, and pilots come to mind. Surely the jobs requiring a human touch, such as artists, entertainers, and managers, will stick around, right? Maybe some of those jobs will be safe. Managers, not so much; very soon, robots will be replacing humans in top management positions, even up to the CEO level.

Updated 15 March 2016: Today concludes the five'Go' matches played by AlphaGo, an AI system built by DeepMind and South Korean champion, Lee Sedol. AlphaGo managed to win the series of games 4-1. 'Go' is a strategy-led board game in which two players aim to gather and surround the most territory on the board. The game is said to require a certain level of intuition and be considerably more complex than Chess. The first three games were won by AlphaGo with Sedol winning the fourth round, but still unable to claim back a victory.

God moves the player, he in turn the piece. But what god beyond God begins the round Of dust and time and sleep and agony? As I write this column, a computer program called AlphaGo is beating the professional go player Lee Sedol at a highly publicized tournament in Seoul. Sedol is among the top three players in the world, having attained the highest rank of nine dan. The victory over one of humanity's best representatives of this very old and traditional board game is a crushing 4 to 1, with one more game to come.

Google is teaching its artificial intelligence how to understand language by making it predict, and replicate, the works of famous dead authors. The company is building systems that are capable of understanding natural language in the same way humans do, with the works of William Shakespeare, Mark Twain and others currently being analysed. "This work has the potential to enrich products through personalisation," Marc Pickett from Google's Natural Language Understanding research group wrote in a recent blog post. Researchers training the deep neural network -- using the work of authors from Project Gutenberg -- fed the AI an input sentence and asked it to say what would come next. The network is given millions of lines from a "jumble" of authors and then works out the style of individual writers.

This week, Google's AlphaGo beat a grandmaster at the complex game Go – an artificial intelligence milestone (see "How victory for Google's Go AI is stoking fear in South Korea", "Machines are teaching themselves to grapple with the real world" and "Humans strike back: How Lee Sedol won a game against AlphaGo"). Here's what the experts say AI's next big challenge should be. No-limit poker: Go represents the ultimate in games where all the information is available to the players. But AI still struggles with games where information is incomplete – like poker, where a player doesn't know what card is coming next. "Computers have beaten the best people at heads-up limit Texas Hold'em, but not yet at no-limit, a much more complicated game," says Peter Stone at the University of Texas at Austin.

Machine Learning is a subfield within Artificial Intelligence that builds algorithms that allow computers to learn to perform tasks from data instead of being explicitly programmed. We can make machines learn to do things! The first time I heard that, it blew my mind. That means that we can program computers to learn things by themselves! The ability of learning is one of the most important aspects of intelligence. Translating that power to machines, sounds like a huge step towards making them more intelligent. And in fact, Machine Learning is the area that is making most of the progress in Artificial Intelligence today; being a trendy topic right now and pushing the possibility to have more intelligent machines.

For example, Dean's affinity for cats comes in handy with his line of work. In this context, cats are a mere vehicle for determining how much a computerMachine learning is next big thing in programming. Read more ... » can see, learn, communicate and understand. It also turns out that machinesAI research nerve centre launched in Cambridge. Read more ... » and humans are complementary in skills.

Empathy is a tricky business. The range and complexity of human emotion makes it difficult, if not impossible, to ever really understand how someone else is feeling. Nevertheless, empathy is considered to be a crucial aspect of what makes us human--indeed, our brains appear to be hardwired for it. So perhaps it won't come as much of a surprise that as machine learning becomes ever more sophisticated and capable of mimicking some of the most complex functions of the human brain, figuring out a way to teach a computer empathy is quickly becoming a business in itself. Known as artificial empathy, the idea here is to train machines to recognize social signals from humans, aka'visual data,' and then produce an appropriate response.