szczys writes: Everyone loves Tamagochi, the little electronic keychains spawned in the '90s that let you raise digital pets. Some time ago, XKCD made a quip about an internet-based matrix of thousands of these digital entities. That quip is now a reality thanks to elite hardware hacker Jeroen Domburg (aka Sprite_TM). In his recent talk called "The Tamagochi Singularity" at the Hackaday SuperConference he revealed that he had built an infinite network of virtual Tamagochi by implementing the original hardware as a virtual machine. This included developing AI to keep them happy, and developing a protocol to emulate their IR interactions. But he went even further, hacking an original keychain to use wirelessly as a console which can look in on any of the virtual Tamagochi living on his underground network. This full-stack process is unparalleled in just about every facet: complexity, speed of implementation, awesome factor, and will surely spark legions of other Tamagochi Matrices.
Lucas123 writes: Volvo has revealed what is sees as the future of self-driving vehicles, a car that has three autonomous driving options, one of which includes a retracting steering wheel, reclining seats with foot rests and a tray table. Unveiled at the Los Angeles Auto Show this week, the Concept 26 also has a 25-in interactive display. Volvo is also among the first to address the subject of self-driving cars and liability, saying we firmly believe that car makers should take full responsibility for the actions of the car when it is driving in full autonomous mode."
StartsWithABang writes: It's well known that by aligning and averaging a wide variety of human faces together, an eerie "average" human face can be arrived at. But we see faces in things all the time, from natural scenes like terrain to artificial ones like cars, coffeemakers and combination locks. For the first time, someone averaged together a large number of images of objects appearing to have faces, and the result, strikingly, was an eerily human face. You'd think this might say more about the algorithm than the images themselves, but when noise was used, no human face emerged at all.
An anonymous reader writes: Microsoft has this week made its Distributed Machine Learning Toolkit (DMTK) openly available to the developer community. Researchers at the Microsoft Asia lab have released the toolkit on GitHub under an MIT (Massachusetts Institute of Technology) license, to encourage the use of multiple computers in parallel to solve complex problems. Its design builds on a parameter server-based programming framework, which allows big data machine learning tasks to be easily scaled, and flexibly and efficiently executed. The toolkit also contains two distributed machine learning algorithms, which can be used to train the world's fastest and largest topic model, as well as the largest word-embedding model.
This is a welcome move, especially after Google did something broadly similar.
This is a welcome move, especially after Google did something broadly similar.
New submitter Colin Robotenomics writes In an important new paper based on a speech at the trade union congress in London, Andy Haldane Chief Economist at the Bank of England and Executive Director of Monetary Analysis and Statistics has examined the history of technological unemployment and has given a thorough review of the literature and implications for public policy. The media will likely focus on the number of jobs that can be displaced and not necessarily Haldane's points on new jobs being created – both of which are highly important as is 'skilling-up'. His report reads in part: "...Taking the probabilities of automation, and multiplying them by the numbers employed, gives a broad brush estimate of the number of jobs potentially automatable. For the UK, that would suggest up to 15 million jobs could be at risk of automation. In the US, the corresponding figure would be 80 million jobs."
Gill Pratt, executive technical adviser at Toyota, offers a note of caution, even as more car companies start putting AI elements into their cars. Speaking in Tokyo at the announcement of a Silicon Valley AI research center that Toyota is to open in early 2016, Pratt pointed out the big shortcoming in an AI system as applied to automobile: Autonomous cars might look great in controlled tests or on pristine highways, "but soon fail when faced with tasks that human drivers find simple." From the article: Drivers, for example, can pretty much get behind the wheel of a car and drive it wherever it may be, he said. Autonomous vehicles use GPS and laser imaging sensors to figure out where they are by matching data against a complex map that goes beyond simple roads and includes details down to lane markings. The cars rely on all that data to drive, so they quickly hit problems in areas that haven't been mapped in advance. ... A truly intelligent self-driving car needs artificial intelligence that can figure out where it is even if it has no map or GPS, and manage to navigate highways and follow routes even if there are diversions or changing in lane markings, he said. I regularly drive a stretch of road that's just a few miles long, but between construction, accidents, poor marking, bicycles, and heavy traffic I'd be nervous about letting an AI system navigate. In what real-world driving scenarios would you most want humans to take over?
An anonymous reader writes: Google's research blog today announced a new feature for their Inbox email app: a neural network that composes short responses to emails you receive. For example, if somebody emails you an invitation to an event, the app will detect that by scanning the words in the message and present you with three options for a quick response. Google says, "A naive attempt to build a response generation system might depend on hand-crafted rules for common reply scenarios. But in practice, any engineer's ability to invent 'rules' would be quickly outstripped by the tremendous diversity with which real people communicate. A machine-learned system, by contrast, implicitly captures diverse situations, writing styles, and tones. These systems generalize better, and handle completely new inputs more gracefully than brittle, rule-based systems ever could." Of course, you can skip them entirely, or use them and add your own words as well. How long until our email systems do most of our talking for us?
AmiMoJo writes: A tweet from Tom Conrad has highlighted an issue with Apple's Siri digital assistant. When asked certain questions about music, Siri refuses to answer unless you subscribe to Apple Music. Instead of falling back to a web search for the information, Siri tells the user that it cannot respond due to the lack of a subscription. Apple Music has been the source of music related data for Siri since it launched, but until now did not require a subscription to answer questions.
itwbennett writes: Sundar Pichai took part in his first earnings call Thursday when Google's parent company Alphabet reported its quarterly results, and 'in between discussing the numbers he revealed how important Google thinks machine learning is to its future,' writes James Niccolai. 'Machine learning is a core, transformative way by which we're rethinking everything we're doing,' Pichai said. 'We're thoughtfully applying it across all our products, be it search, ads, YouTube, or Play. We're in the early days, but you'll see us in a systematic way think about how we can apply machine learning to all these areas.'
Quartz describes an MIT study with the surprising conclusion that at least in some circumstances, an algorithm can not only sift numbers faster than humans (after all, that's what computers are best at), but also discern relevant factors within a complex data set more accurately and more quickly than can teams of humans. In a competition involving 905 human teams, a system called the Data Science Machine, designed by MIT master's student Max Kanter and his advisor, Kalyan Veeramachaneni, beat most of the humans for accuracy and speed in three tests of predictive power, including one about "whether a student would drop out during the next ten days, based on student interactions with resources on an online course." Teams might have looked at how late students turned in their problem sets, or whether they spent any time looking at lecture notes. But instead, MIT News reports, the two most important indicators turned out to be how far ahead of a deadline the student began working on their problem set, and how much time the student spent on the course website. ... The Data Science Machine performed well in this competition. It was also successful in two other competitions, one in which participants had to predict whether a crowd-funded project would be considered “exciting” and another if a customer would become a repeat buyer.
An anonymous reader writes: Norwegian developer and blogger Lars Eidnes has designed a clickbait generator using a neural network, which is able to create sensationalist headlines that play on human readers' curiosity. Eidnes trained his neural network by scanning around two million clickbait titles from online media sites such as Buzzfeed, Jezebel and Upworthy. When asked to form a sentence, the system can now output a single word and continues the prediction process to find related words, in a pattern known as Recurrent Neural Networks (RNNs).
An anonymous reader writes with the news that Tesla owners today found their cars had been upgraded with the company's new autopilot feature: "That means the next time you see a Model S cruising next to you on the interstate, look closely: It may be driving itself." Adds the submitter: Well, I guess some of you will be celebrating this; but this submitters' fear, is that if this technology becomes pervasive, the skill of operating a vehicle will be lost, as is any skill that isn't practiced regularly. It is unlikely that 'self-driving cars' will reach a point where they can handle 100% of all driving circumstances without human intervention, emergency circumstances being the first and foremost example of what an automated system could not adequately handle unaided; what will we do then, when injuries that could have been avoided or when lives are lost because people aren't competent to operate a vehicle any longer?
subh_arya writes: Automatic crowd counting has been an extremely challenging computer vision problem. However, researchers from UCF, seem to have found a reasonably accurate solution using sophisticated probabilistic models. Although there has been several previous efforts in this direction, this is the first time the technology has been put to use on a realistic scenario where around 550,000 protesters participated for Catalunyan Independence. A freely available technical paper published in IEEE Trans. on Pattern Analysis and Machine Intelligence, 2015 is available here.
An anonymous reader writes: David Mindell, an MIT professor, says self-driving cars should never be fully autonomous. "There's an idea that progress in robotics leads to full autonomy. That may be a valuable idea to guide research but when automated and autonomous systems get into the real world, that's not the direction they head. We need to rethink the notion of progress, not as progress toward full autonomy, but as progress toward trusted, transparent, reliable, safe autonomy that is fully interactive: The car does what I want it to do, and only when I want it to do it." Mindell writes, "Google's utopian autonomy is a more brittle, less functional solution than a rich, human-centered automation."
An anonymous reader writes: Japanese firms NTT Communications and SoftBank are working to develop new artificial intelligence (AI) platforms, offering cyber-attack protection services to their customers. Up until recently, AI-based security systems were only used for certain scenarios, in online fraud detection for example. The new offerings will be the first commercially-available platforms of their type for use in a wide range of applications.
MojoKid writes: Part of Microsoft's strategy to unite different devices to a single ecosystem means offering the same services and features across the board. One of those features is Cortana, Microsoft's digital assistant, which is available on Windows 10. It will also be available for the Xbox One, though not until sometime next year, at least officially. Don't feel like waiting? You might not have to. Here's a quick and dirty guide on how to unlock Cortana on the Xbox One, provided you're running the latest Xbox One Experience Preview.
dcblogs writes: Gartner's near-future predictions include: Writers will be replaced. By 2018, 20% of all business content, one in five of the documents you read, will be authored by a machine. By 2018, 2 million employees will be required to wear health and fitness tracking devices as a condition of employment. This may seem Orwellian, but certain jobs require people to be fit, such as public safety workers. By 2020, smart agents will facilitate 40% of mobile interactions. This is based on the belief that the world is moving to a post-app era, where assistants such as Apple's Siri act as a type of universal interface.
Engadget reports that Daimler has tested an autonomous truck in one environment guaranteed to put stress on any car: the German Autobahn. While the Mercedes Actros truck was guided with a mix of "radar, a stereo camera array and off-the-shelf systems like adaptive cruise control," there was a human crew on hand, too, just in case. From the article: This doesn't mean you'll see fleets of robotic trucks in the near future. Daimler had to get permission for this run, and the law (whether European or otherwise) still isn't equipped to permit regular autonomous driving of any sort, let alone for giant cargo haulers. Still, this could make a better case for approving some form of self-driving transportation.
jfruh writes: IBM's Jeopardy-winning supercomputer Watson is now suite of cloud-based services that developers can use to add cognitive capabilities to applications, and one of its powers is visual analysis. Visual Insights analyzes images and videos posted to services like Twitter, Facebook and Instagram, then looks for patterns and trends in what people have been posting. Watson turns what it gleans into structured data, making it easier to load into a database and act upon — which is clearly appealing to marketers and just as clearly carries disturbing privacy implications.
the_newsbeagle writes: Outbreaks of infectious diseases like Ebola follow a depressing pattern: People start to get sick, public health authorities get wind of the situation, and an all-out scramble begins to determine where the disease started and how it's spreading. Barbara Han, a code-writing ecologist, hopes her algorithms will put an end to that reactive model. She wants to predict outbreaks and enable authorities to prevent the next pandemic. Han takes a big-data approach, using a machine-learning AI to identify the wild animal species that carry zoonotic diseases and transmit them to humans.