Carnegie Mellon University professor Russ Salakhutdinov has been hired by Apple to lead a team focused on artificial intelligence, according to a tweet Salakhutdinov sent out this morning. He will continue to teach at Carnegie Mellon, but will also serve as "Director of AI Research" at Apple. In his tweet, Salakhutdinov says he is seeking additional research scientists with machine learning expertise to join his team. An included job posting asks that candidates have experience with Deep Learning, Computer Vision, Machine Learning, Reinforcement Learning, Optimization, and/or Data Mining. Salakhutdinov specializes in statistical machine learning and has authored many papers on neural networks, deep kernel learning, reinforcement learning, and other related topics.

Apple isn't letting Samsung's acquisition of Viv go unanswered. The Cupertino crew has hired Russ Salakhutdinov, a computer science professor at Carnegie Mellon University, as a director of artificial intelligence research. Interestingly, he isn't giving up his school work -- he may well be publishing research at the same time as he's upgrading your iPhone or Mac. It's not certain what he'll be working on, although Recode observes that his recent studies have involved understanding the context behind questions. We've asked Apple if it can comment.

Machine learning is no longer the stuff of science fiction, nor is it all that new. Its development dates back to the mid-20th century and was defined in 1959 by Arthur Samuel as a "field of study that gives computers the ability to learn without being explicitly programmed". As the modern world becomes increasingly dependent on data-driven technologies, machine learning, along with artificial intelligence (AI), has captured the human imagination. It is clear that businesses are spending huge amounts of time and money scrambling to adopt the latest and greatest technologies in the hope of out-pacing and out-smarting their rivals. However, without a customer-centric, business-relevant big data strategy that is embraced company-wide, all the technology in the world won't sell a thing.

This is a follow up to an article Per wrote last year, Machine Learning in a Week, on how he kickstarted his way into machine learning (ml) by devoting five days to the subject. Follow him on Medium and check out his archive. My interest in ml stems back to 2014 when I started reading articles about it on Hacker News. I simply found the idea of teaching machines stuff by looking at data appealing. At the time I wasn't even a professional developer, but a hobby coder who'd done a couple of small projects.

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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?