"Our research with DeepMind has the potential to revolutionise the way professionals carry out eye tests and could lead to earlier detection and treatment of common eye diseases such as age-related macular degeneration," said Professor Sir Peng Tee Kaw, the head of Moorfields' ophthalmology research centre. DeepMind, which Google paid 400 million to acquire two years ago, hopes to use artificial intelligence to advance medical and climate research after its software defeated the world champion at the ancient Chinese board game Go.

For some, an image of a creepy robot that moves, walks and swims in the same way as a salamander will be the stuff of nightmares, but this bizarre looking bot does have a significant purpose. Created by researchers at École polytechnique fédérale de Lausanne, the 3D-printed robot copies the gait of the Pleurodeles waltl salamander. Dubbed Pleurobot, the engineers behind the work say it is "accurately based on the 3D motion of the animal's skeleton". The team created X-ray videos of a salamander moving and were able to copy its actions by tracking 64 points along the animal's body. The robot itself has 27 motors and 11 segments along its spine.

To inform the Open Philanthropy Project's investigation of potential risks from advanced artificial intelligence, and in particular to improve our thinking about AI timelines, I (Luke Muehlhauser) conducted a short study of what we should learn from past AI forecasts and seasons of optimism and pessimism in the field. In addition to the issues discussed on our AI timelines page, another input into forecasting AI timelines is the question, "How have people predicted AI -- especially HLMI (or something like it) -- in the past, and should we adjust our own views today to correct for patterns we can observe in earlier predictions?"1 We've encountered the view that AI has been prone to repeated over-hype in the past, and that we should therefore expect that today's projections are likely to be over-optimistic. To investigate the nature of past AI predictions and cycles of optimism and pessimism in the history of the field, I read or skim-read several histories of AI2 and tracked down the original sources for many published AI predictions so I could read them in context. I also considered how I might have responded to hype or pessimism/criticism about AI at various times in its history, if I had been around at the time and had been trying to make my own predictions about the future of AI. I can't easily summarize all the evidence I encountered that left me with these impressions, but I have tried to collect many of the important quotes and other data below. Then, in a final subsection, I summarize some questions I might have investigated if I had more time. I would be curious to learn whether people who read a set of sources similar to the set I consulted come away from that exercise with roughly the same impressions impressions I have. I would also be curious to hear how many AI scientists who were active during most of the history of the field share my impressions. The histories I read left me with the impression that some (but not all) of the earliest AI researchers -- starting around the time of the Dartmouth Conference in 1956 -- thought HLMI (or something like it) might only require a couple decades of work. For example, Moravec (1988) claims that John McCarthy founded the Stanford AI project in 1963 "with the then-plausible goal of building a fully intelligent machine in a decade" (p.

I'm not (quite) dead and intend to go back to posting stuff every now and then. Last July, I've also started a new job, as an assistant professor in the Department of Statistics at Harvard University, after having spent two years in Oxford. At some point, I might post something on the cultural difference between the European English and American communities of statisticians. In the coming weeks, I'll tell you all about a new paper entitled Coupling of Particle Filters, co-written with Fredrik Lindsten and Thomas B. Schön from Uppsala University in Sweden. We are excited about this coupling idea because it's simple and yet brings massive gains in many important aspects of inference for state space models (including both parameter inference and smoothing).

Craft beer could be the next unlikely beneficiary of the artificial intelligence revolution. London-based IntelligentX Brewing Company has revealed its new AI Beer range. These are beers that will be improved over time using advanced algorithms. The brewer is employing an online feedback system (via a Facebook Messenger bot) to obtain data on how well-liked its Pale, Amber, Black and Golden beers are by customers. The company will then employ "complex machine learning algorithms," combining reinforcement learning and bayesian optimisation, to search for trends among this feedback and tune the recipes accordingly. "Because our A.I. is constantly reacting to user feedback, we can brew beer that matches what you want, more quickly than anyone else can," says IntelligentX Brewing Company.

Because we'll need something tasty to swill when our robot overlords finally come into their full artificial intelligence, a company in the UK is attempting to figure out if robots can help humans brew a better beer. While there won't be robots stirring batches of wort or sorting hops, artificial intelligence will play a big part in London-based firm IntelligentX's plan to brew beer, CNET reports. Here's how it'd work: consumers would try one of the company's four beers -- Amber AI, Black AI, Golden AI and Pale AI ---- and then weigh in via Facebook chat bot on the experience. That feedback will be fed to an algorithm called Automated Brewing Intelligence, or ABI, which will use the information to make changes to the next batch. Reinforcement learning and a process called bayesian decision making will teach the AI about the brewing experience.

SummaryCMIS and Apache Chemistry in Action is a comprehensive guide to the CMIS standard and related ECM concepts, written by th...ries Building mobile apps with CMIS PART 3 ADVANCED TOPICS CMIS bindings Security and control Performance Building a CMIS server This is the official OOPic (object oriented embedded microcontroller) manual endorsed by the largest manufacturer of OOPics and ...Pic microcontroller, sample code you can incorporate and customize for your projects, as well as special OOPic-related software. Remarkable progress in eye-tracking technologies opened the way to design novel attention-based intelligent user interfaces, and...n human attentional behaviors and face-to-face communication which are essential in designing gaze aware interactive interfaces. Opening with a detailed review of existing techniques for selective encryption, this text then examines algorithms that combine ...heme with enhanced security features; presents an encryption scheme for image and video data based on chaotic arithmetic coding. This book and software package presents a unified approach for doing mathematical statistics with Mathematica. Create your own natural language training corpus for machine learning.

Project Malmo is a platform for Artificial Intelligence experimentation and research built on top of Minecraft. Microsoft today announced that they are making it available for everyone on GitHub via an open-source license. This project was formerly known as Project AIX and has now been renamed Project Malmo. Minecraft is ideal for artificial intelligence research for the same reason it is addictively appealing to the millions of fans who enter its virtual world every day. Unlike other computer games, Minecraft offers its users endless possibilities, ranging from simple tasks like walking around looking for treasure to complex ones like building a structure with a group of teammates.

"Really, we're building collision avoidance for industrial drones," said Alexander Harmsen, CEO and co-founder of Iris Automation. "We see this huge need for industrial drones for mining exploration, pipeline inspection, agricultural surveying, forestry, or even package delivery." Without a way to avoid mid-air collisions, drones risk crashing into a Cessna, a flock of geese or a 747. Worst case scenario: a drone gets sucked into a jet engine causing catastrophic engine failure as high-velocity bits of metal penetrate fuel tanks, hydraulic lines and the cabin. Iris Automation's solution is an AI computer that blends real-time images and 3D maps to track incoming objects.

Word embeddings have been shown to be useful across state-of-the-art systems in many natural language processing tasks, ranging from question answering systems to dependency parsing. (Herbelot and Vecchi, 2015) explored word embeddings and their utility for modeling language semantics. In particular, they presented an approach to automatically map a standard distributional semantic space onto a set-theoretic model using partial least squares regression. We show in this paper that a simple baseline achieves a +51% relative improvement compared to their model on one of the two datasets they used, and yields competitive results on the second dataset.