Human Rights Watch issued a report Friday urging a ban on the development of fully autonomous weapons. The 49-page report entitled "Making the Case: The Dangers of Killer Robots and the Need for a Preemptive Ban" detailed the various dangers of creating weapons that could think for themselves, including the concern that self-operating defense technology would remove the human element from warfare upon which most clauses of military law are bound. The organization argued machines would not be able to responsibly make the same complex tactical decisions involved in warfare and that existing laws did not adequately cover their use in combat. Human Rights Watch also contended that removing the human element of warfare raised serious moral issues, saying lack of empathy would exacerbate unlawful and unnecessary violence. The organization warned that such technology could be used by authoritarian leaders as a means of controlling and subjugating populations without fear of revolt.

DeepMind, Google's artificial intelligence outfit, wants to streamline health care by using machine learning to provide medics with intelligent notifications. But not everyone is happy with the piles of data being shared with the company. The project will provide medics across a number of London hospitals with alerts about patients via an app called Streams. The app is meant to provide easy access to patient histories and test results for nurses and doctors. But its system will also learn to track patterns in patients' blood test data and flag cases that show early signs of kidney injury to the appropriate doctors.

There's been some stunning progress in artificial intelligence of late, but it's been surprisingly flat. Now AI researchers are moving beyond two-dimensional images and pixels. The work could have a big impact on robotics and self-driving cars, helping to make machines that can learn how to act more intelligently in the real world. "An exciting and important trend is the move in learning-based vision systems from just doing things with images to doing things with three-dimensional objects," says Josh Tenenbaum, a professor in MIT's Department of Brain and Cognitive Sciences. "That includes seeing objects in depth and modeling whole solid objects--not just recognizing that this pattern of pixels is a dog or a chair or table." Tenenbaum and colleagues used a popular machine-learning technique known as generative adversarial modeling to have a computer learn about the properties of three-dimensional space from examples.

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One of the most defining aspects of this century is the mass of data that is available everywhere. Using this data competently and analysing it is one of the many things that deep learning (DL) does. From fighting cancer by studying its pathology and patterns to warding off asteroid attacks, deep learning has put the world on a whole different trajectory. In deep learning the computer solves a problem independently by going through multiple levels of learning, which makes a lot of simple tasks much more efficient and accurate. This intricate level of computing requires GPU-accelerated computing to speed up tasks such as image, handwriting and voice identification.

The time of year when crystal balls get a viewing and many pundits put out their annual predictions for the coming year. Rather than thinking up my own, I figured I'd regurgitate what many others are expecting to happen. Chris Preimesberger (@editingwhiz), who does a monthly #eweekchat on twitter, covers many of the worries facing organizations. People focus so much on the'things' themselves rather than the risk of an internet connection. This list discusses how IoT will grow up in 2017, how having a service component will be key, the complete mess of standards and simply, 'just because you can connect something to the Internet doesn't mean that you should.' NW talks about how cyber attacks will get worse due to IoT and gives some ideas on how to protect your data in 2017.

Are we approaching Economic Singularity? My recent book, The Economic Singularity, argues that improvements in machine intelligence are going to make it impossible for many of us –maybe all of us – to earn a living. If this is even an outside possibility, we'd all be wise to spend some time working out how we intend to deal with it. In 1900, 40 per cent of American workers were employed in agriculture. That has now fallen below three per cent and while the farm workers eventually found better jobs elsewhere in the economy, their horses didn't.

Connected hospitals with intelligent messaging In today's hospitals, pacemakers, defibrillators and oximeters are all connected to the internet and share vitals immediately with doctors, in turn speeding response times. Hospitals have technicians, nurses, staff, billing departments, insurance providers, patients and patients' families as stakeholders, each with different requirements of information about the care given to patient. Unified Inbox offers an AI-based unified cloud IoT messaging platform for internet of things devices to connect various stakeholders, giving them the freedom to receive different messages at different frequency, with different senses of urgency in different mediums of their choice. Unified Inbox launched this at Nanyang Polytechnic in Singapore as "CUBE," the IoT-secured messaging gateway for healthcare. The artificial intelligence makes the hospitals connected, giving peace of mind to patients and their loved ones while improving efficiency in the overall hospital management and interaction with all stakeholders.

Join our experts and learn more about How can organizations benefit from #MachineLearning? #saschat on December 9th at 3.00 CEST pic.twitter.com/GCNJDPnT3A SAS Institute France (@SASFrance)Mon, Dec 05 2016 15:04:01 Q1: #MachineLearning is a hot topic and is taking hold. Peter Pugh-Jones (@rubricsinger)Fri, Dec 09 2016 14:11:39 A1: With increased processing power and data science skills more and more complex decisions can be made using machine learning. Henrikki Hervonen (@henkkuh)Fri, Dec 09 2016 14:09:04 #saschat A1: Also the biggest tech companies have used a machine learning approach to deal with our personal data. Marcel Lemahieu (@MarcelLemahieu)Fri, Dec 09 2016 14:10:36 A1. #machinelearningn has gained momentum with the tremendous computing power we have today.

With that in mind, I'd like to share one way that Avnet has been using machine learning in the enterprise this year, with highly compelling results. In October 2016, the internet experienced a massive denial of service attack that affected more than 80 major websites and cloud service providers from Amazon to Twitter. A bit of malware that hijacked hundreds of thousands of unsecure Internet of Things devices to overwhelm specific targets. In the cybersecurity arms race, malicious hackers are increasingly looking to machines to strengthen their attacks. So larger organizations like Avnet are also tapping into the power of machines--in this case, machine learning--to strengthen our defenses.