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In this post, we'll show how easy it is to use MLDB to build your own real time image classification service. We will use different brand of cars in this example, but you can adapt what we show to train a model on any image dataset you want. We will be using a TensorFlow deep convolutional neural network, transfer learning, and everything will run off MLDB. At a high level, transfer learning allows us to take a model that was trained on one task and use its learned knowledge on another task. We use the Inception- v3 model, a deep convolutional neural network, that was trained on the ImageNet Large Visual Recognition Challenge dataset.

Artificial intelligence (AI) continues to play an expanding role in the future of high-performance computing (HPC). As machines increasingly become able to learn and even reason in ways similar to humans, we're getting closer to solving the tremendously complex social problems that have always been beyond the realm of compute. Deep learning, a branch of machine learning, uses multi-layer artificial neural networks and data-intensive training techniques to refine algorithms as they are exposed to more data. This process emulates the decision-making abilities of the human brain, which until recently was the only network that could learn and adapt based on prior experiences.

Drones won't replace proper sailors anytime soon because, believe it or not, they need more manpower to operate, a Royal Navy admiral has insisted. Naval drones are "not about reducing the requirement for people", Rear Admiral Paul Bennett told a press briefing attended by El Reg on Friday. Instead, they are for putting people into positions where they add "real value". At present, unmanned systems - drones - require on average something like four or five operators each, we understand. Rather than enabling cuts in manpower, if anything they require ever more personnel aboard ships to operate them; not a good situation to be in when the Navy is already critically short of heads.

Narrative Science has been a regular feature on Dataconomy over the past year, from Chief Scientist Kris Hammond's post about the impact of artificial intelligence on banking, to the launch of their Quill Connect application for processing unstructured text data from social media. I think for AI in general, the goal is not to make the machine smarter and destroy us, but to make machines smarter and as a result, put us in a position where we no longer have to deal with the machine, as an unintelligent device which requires frequent input and supervision. We can deal with the machine as a partner, whose job is to make us smarter. We get smarter because it gets smarter. Because who in the world wants to actually look at a spreadsheet, or figure out what's going on in the visualization, or go to massive textual data to get the answer to a question?

Human-level intelligence is familiar in biological hardware – you're using it now. Science and technology seem to be converging, from several directions, on the possibility of similar intelligence in non-biological systems. It is difficult to predict when this might happen, but most artificial intelligence (AI) specialists estimate that it is more likely than not within this century. Freed of biological constraints, such as a brain that needs to fit through a human birth canal (and that runs on the power of a mere 20W lightbulb), non-biological machines might be much more intelligent than we are. What would this mean for us?

When we type in a search query, access our email via the cloud or stream a viral video, chances are we don't spend any time thinking about the technological plumbing that is behind that instant gratification. They are engineers who spend their days thinking about ever-better and faster ways to get you all that information with the tap of a finger, as you've come to expect. A team of Microsoft engineers and researchers, working together, has created a system that uses a reprogrammable computer chip called a field programmable gate array, or FPGA, to accelerate Bing and Azure. Utilizing the FPGA chips, Lanka and Chiou's teams can write their algorithms directly onto the hardware they are using, instead of using potentially less efficient software as the middle man. What's more, an FPGA can be reprogrammed at a moment's notice to respond to new advances in artificial intelligence or meet another type of unexpected need in a datacenter.

Most amputees use purely aesthetic prostheses. They find it difficult to accept a robotic limb that is not only by and large complicated to use but also has somewhat unnatural motion. Most of the models on the market today can only execute a few simple gestures, for example opening and closing the fist, and often in a very jarring way. Furthermore, users can't always properly control the magnitude of the movement, which adds a safety risk to the mixture. Scientists are therefore striving to bring prosthetic movements closer to those of the human body by using machine learning, a technique also used in artificial intelligence.

Any organization looking to remain competitive in today's high-tech digital world must constantly innovate and pilot new technologies and, most importantly, listen to consumers and the market for indicators of change. The basic commercial landscape is rapidly shifting towards automated processes and data-backed decision making, and marketing is no different. Automated and personalization are now key elements to engaging and communicating with consumers. All marketing efforts rely in some part on the ability to personalize the consumer journey and create incredible and memorable experiences that keep customers coming back again and again. Today, it's big data, machine learning, and artificial intelligence (AI) that have taken the spotlight as the new tools of highly effective marketing teams.

That's just one takeaway from a new 48-page report White House AI report it released to policy makers this week, according to Jim Hendler, AI expert, researcher and coauthor of the new book, Social Machines: The Coming Collision of Artificial Intelligence, Social Networking and Humanity. Addressing such concerns, the report says that fears about super-intelligent and evil computers, shouldn't have much impact on current US policy toward AI. "And it gets that part right … we all need to cut past the hype and look at what's really on in AI," he said, "so we can pay attention to the real risks and opposed to the science fiction risks," he said. The things to worry about aren't the frightening HAL 9000 or Skynet scenarios, Hendler says, "but the very real economic, societal ethical and safety concerns AI poses for the foreseeable future." The report does a fair job of addressing such issues overall, he says, "and it is good as far as it goes," Hendler says.