Oren Etzioni, a well-known AI researcher, complains about news coverage of potential long-term risks arising from future success in AI research (see "No, Experts Don't Think Superintelligent AI is a Threat to Humanity"). After pointing the finger squarely at Oxford philosopher Nick Bostrom and his recent book, Superintelligence, Etzioni complains that Bostrom's "main source of data on the advent of human-level intelligence" consists of surveys on the opinions of AI researchers. He then surveys the opinions of AI researchers, arguing that his results refute Bostrom's. It's important to understand that Etzioni is not even addressing the reason Superintelligence has had the impact he decries: its clear explanation of why superintelligent AI may have arbitrarily negative consequences and why it's important to begin addressing the issue well in advance. Bostrom does not base his case on predictions that superhuman AI systems are imminent.

When Pyotr Stolyarsky died in 1944, he was considered Russia' s greatest violin teacher. He counted among his pupils a coterie of stars, including David Oistrakh and Nathan Milstein, and a school for gifted musicians in his native Odessa was named after him in 1933. But Stolyarsky couldn't play the violin anywhere near as well as his best students. What he could do was whisper metaphors into their ears. He might lean over and explain how his mother cooked Sabbath dinner.

We imagine them to be wickedly intelligent, like Samantha from the movie Her. There is no "I" in their AI. They are so hopeless that some people argue that bots would be better without conversation. But without conversation they are not bots. They are more like the Internet on Nokia feature phones (a.k.a.

In Boston, two major academic centers are teaming up to apply big data and machine learning to the problem of diagnosing cancers earlier and with more accuracy. It is research that might have major implications for the anatomic pathology profession. A collaborative effort between teams at Beth Israel Deaconess Medical Center (BIDMC) and Harvard Medical School (HMS) has resulted in an innovation that could result in more accurate diagnoses in the pathology laboratory. The teams have been working on a machine learning software program that will eventually function as an artificial intelligence (AI) to improve the accuracy of diagnostics. They hope to someday build AI-powered computer systems that can accurately and quickly interpret pathology images.

Tesla might have got an early start when it comes to self-driving car technology but other companies are also stepping on the gas as far as the technology is concerned. While the technology is nowhere near being fully autonomous, with more and more automakers and even companies like Google stepping in, self-driving cars have transformed from a mere fantasy to slowly on the way to becoming a mainstream auto technology. Google's self-driving car project has been in the works for some time. The company has been testing a fleet of modified Lexus SUVs and new prototype cars. The car uses mapping and sensors to determine location and route.

The app boom is over. There are now more than 4.2 million apps available for Android and iOS, but three-quarters of American smartphone users download a grand total of zero new apps per month. They might be mostly free and easy to access, but apps are struggling to make it on to our phones and tablets. According to comScore, we spend the majority of our screen time using just three apps, with the average American spending almost half their time in just one. With the eyeballs of the world glued to WhatsApp, Facebook Messenger, WeChat and Skype, developers have started turning once-simple chat apps into complex ecosystems. And at the centre of this change is a horde of subservient bots. You've just walked into your kitchen after a long week. "Play Etta James," you say.

An update to his DoNotPay bot works by asking users a simple set of questions about their circumstances, before advising them on the best course of action--often helping them draft an effective form letter to apply to their local councils for emergency housing. Councils have to take every letter seriously, and using Freedom of Information requests, he's researched the best ways to prompt them into acting on his bot's clients' behalf. The bot's creator, Joshua Browder, a 19-year-old Brit studying at Stanford University in California, told Ars that since the update launched last Wednesday "almost every local government in the UK has signed up for the website." "I can see on the back end that they are actively trying out the service for themselves," he added. He's also working with Centrepoint, one of the UK's largest and most recognisable homelessness charities, with volunteer lawyers helping out to finesse the legal advice his bot provides.

The world's largest dementia research experiment, which takes the form of a video game, has indicated the ability to navigate declines throughout life. The findings, presented at the Neuroscience 2016 conference, harnessed data from 2.4 million people who downloaded the game. Getting lost is one of the first symptoms of Alzheimer's disease. And the researchers at University College London believe the results could help make a dementia test. Sea Hero Quest is a nautical adventure to save an old sailor's lost memories.

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Machine reading using differentiable reasoning models has recently shown remarkable progress. In this context, End-to-End trainable Memory Networks (MemN2N) have demonstrated promising performance on simple natural language based reasoning tasks such as factual reasoning and basic deduction. However, other tasks, namely multi-fact questionanswering, positional reasoning or dialog related tasks, remain challenging particularly due to the necessity of more complex interactions between the memory and controller modules composing this family of models. In this paper, we introduce a novel end-to-end memory access regulation mechanism inspired by the current progress on the connection short-cutting principle in the field of computer vision. Concretely, we develop a Gated End-to- End trainable Memory Network architecture (GMemN2N). From the machine learning perspective, this new capability is learned in an end-to-end fashion without the use of any additional supervision signal which is, as far as our knowledge goes, the first of its kind. Our experiments show significant improvements on the most challenging tasks in the 20 bAbI dataset, without the use of any domain knowledge. Then, we show improvements on the Dialog bAbI tasks including the real human-bot conversion-based Dialog State Tracking Challenge (DSTC-2) dataset. On these two datasets, our model sets the new state of the art.