Welcome to the fifth edition of our annual Robot Gift Guide! This year, we bring you a dozen robots that we think will make fantastic holiday gifts. Just as we've done in the past (for a quick trip through recent robotics history, check out the 2012, 2013, 2014, and 2015 editions), this year's selection includes mostly new products released in 2016 but also some items from previous years that we still like. We tested many of these bots ourselves, and you might have seen our in-depth reviews here on the blog. And while we provide prices and links to places where you can buy these items, we're not endorsing any in particular, and a little bit of searching may result in better deals (all prices are in U.S. dollars).

Question How does the performance of an automated deep learning algorithm compare with manual grading by ophthalmologists for identifying diabetic retinopathy in retinal fundus photographs? Finding In 2 validation sets of 9963 images and 1748 images, at the operating point selected for high specificity, the algorithm had 90.3% and 87.0% sensitivity and 98.1% and 98.5% specificity for detecting referable diabetic retinopathy, defined as moderate or worse diabetic retinopathy or referable macular edema by the majority decision of a panel of at least 7 US board-certified ophthalmologists. At the operating point selected for high sensitivity, the algorithm had 97.5% and 96.1% sensitivity and 93.4% and 93.9% specificity in the 2 validation sets. Meaning Deep learning algorithms had high sensitivity and specificity for detecting diabetic retinopathy and macular edema in retinal fundus photographs. Importance Deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior, removing the need to specify rules explicitly. Application of these methods to medical imaging requires further assessment and validation. Objective To apply deep learning to create an algorithm for automated detection of diabetic retinopathy and diabetic macular edema in retinal fundus photographs. Design and Setting A specific type of neural network optimized for image classification called a deep convolutional neural network was trained using a retrospective development data set of 128 175 retinal images, which were graded 3 to 7 times for diabetic retinopathy, diabetic macular edema, and image gradability by a panel of 54 US licensed ophthalmologists and ophthalmology senior residents between May and December 2015.

I want to make one point, that this is on the record. But we're going to have a great time discussing "Robots and the Future of Jobs: The Economic Impact of Artificial Intelligence." So I'll start with simple introductions, and then we'll lay out some definitions about the kinds of terms that will be involved in this conversation. So my name is John Paul Farmer. Very happy to be here with three experts on the topic. Next to me is Dr. James Manyika, who is a recovering roboticist. And his day job is at McKinsey, at the McKinsey Global Institute, where he's been focusing on the future of jobs and the future of work in this new era. In the middle, we have Dr. Daniela Rus. Dr. Rus is a professor and roboticist at MIT, and she is also the director of the Computer Science and Artificial Intelligence Lab there. And at the end, we have Edwin van Bommel. Edwin is formerly of McKinsey, but now he's the chief cognitive officer at IPsoft. So, with that, let me lay out some definitions that are going to be important, I think, to following this conversation. You may have read in Foreign Affairs and elsewhere about this fourth industrial revolution, the changes that are happening in our society today and many more that will be coming down the pike. So as we--as we talk about these things, one, we should all be on the same page in terms of what artificial intelligence is. What do we mean when we say AI? And the definition that many accept is it's the development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and even translation between languages. AI is sometimes humorously referred to as whatever computers can't do today. Machine learning is another term you're going to hear a lot, sometimes thought of as a rebranding of AI, of artificial intelligence. But there's one key difference, which is that it takes a much more probabilistic approach as opposed to deterministic. So it looks at not just yes or no; it looks at a 30 percent chance of X, a 10 percent chance of Y, and so on. Big data, a term that I think we've all heard. Data is the raw material. Some people call it the new oil for this new era.

It's hard to not get swept up in all the hype surrounding AI at the moment. The more you research the seemingly limitless potential, the grander the scope of your ambition becomes. At first, you automate part of a workflow only to inevitably be struck with the fact you could automate the whole worker or even the entire company! Before you know it, you are positive your AI could stage a convincing coup of a small island nation -- under the right political conditions, of course. Before we can develop compelling AI solutions, however, it is important to check our technical hubris and consider some very limiting factors to AI in its current stage.

The photo shows the many layers of the atmosphere in shades of blue, with the dramatic appearance of the setting sun stretching above the dark surface beneath. The AI that can predict the FUTURE: Algorithm studies images... Is your partner'phubbing' you? Researchers say phone... Amazon's Alexa set for an upgrade with'SuperEcho' speaker... Earth-like planet is spotted 150 light years away - and it... The AI that can predict the FUTURE: Algorithm studies images... Is your partner'phubbing' you? Researchers say phone... Amazon's Alexa set for an upgrade with'SuperEcho' speaker... Earth-like planet is spotted 150 light years away - and it...

Auto parts and electronics maker Delphi Corp. has signed a deal with Intel to buy high-powered computer processors for Delphi's future autonomous vehicle systems. Delphi says Intel's added computing capacity will be needed as autonomous car systems gather and store more and more information while expanding their ability to deal with situations on real roads. In August, Delphi announced that it had joined with Israeli software maker Mobileye to develop the building blocks for a fully autonomous car in about two years. Intel Corp. will supply Delphi with high-capacity computers needed to process input from radar, cameras and laser sensors as well as maps of roadside landmarks. Glen De Vos, vice president of Delphi's business-services unit, says the Intel deal gives the company everything it needs to develop an autonomous-driving package to sell to automakers. Delphi makes its own radar and laser sensors and uses Mobileye's cameras and software.

When you upload photos to Facebook, have you noticed that the website already seems to know who's in them? It's remarkable, and you can give the credit to big data. Face recognition software, like fraud detection and ad matching algorithms, draws on deep libraries of content in order to deliver the correct results. And these data collections are hard at work across the web and in many of your favorite apps. It comes as no surprise that developers have been hard at work on face recognition software since it's an integral part of security programs.

The platform parses various business reports and other sources to assemble background information in specific industry verticals, like ready meals or food/beverages. Currently, the focus is on consumer packaged goods (CPG) and retailers, although new categories can be built. Marketers define goals, then answer a series of questions. The platform pulls appropriate questions from hundreds pre-written by human researchers, and the questions are auto-assembled, without any writing by the marketer, into an online questionnaire of 25–30 questions. The platform learns about a specific business from the questionnaire's results and feedback from the business. The questionnaire is then made available to an online panel of paid respondents via the UK-based firm Lucid.

Sky has launched a new mobile network that it hopes can fix the problems with all the existing ones. The new carrier, known as Sky Mobile, is intended to be the UK's "most flexible mobile service". It's looking to do so by including new features, perhaps the biggest of which is letting people roll over their unused data so that tehy can use it when they want. The other headline features of the network – which the company is calling a smart network for smart devices – include tariffs that let people only pay for the data they use and letting people change how much data is in their plan each month. In its facilities, JAXA develop satellites and analyse their observation data, train astronauts for utilization in the Japanese Experiment Module'Kibo' of the International Space Station (ISS) and develop launch vehicles 32/39 The robot developed by Seed Solutions sings and dances to the music during the Japan Robot Week 2016 at Tokyo Big Sight.

Amazon's Alexa set for an upgrade with'SuperEcho' speaker complete with built in 7inch touchscreen Alexa voice assistant can respond to voice commands from user Next version rumour to have much improved sound and a touchscreen Will compete against Google's Home and rumoured Apple Siri speaker Will compete against Google's Home and rumoured Apple Siri speaker The Amazon Echo and smaller Echo Dot speakers will be available in black or white. The Echo will cost £149.99 Why the left is ALWAYS a man's best side: Researchers find... You can now play Pac-Man in Messenger: Facebook adds... Are YOU smart enough to answer Google's tricky interview... Does this look like a lizard on Mars? Why the left is ALWAYS a man's best side: Researchers find...