Alphabet Inc. (GOOG) subsidiary Google's artificial intelligence company DeepMind Health has signed an agreement with a hospital that is part of the U.K.'s National Health Service (NHS) network to deploy Streams, an app that monitors critical indicators of a patient's health and alerts doctors. This is the second such deal signed by DeepMind this year. Earlier, the company signed a similar agreement for patient data with three hospitals that operate under the NHS umbrella. As part of the agreements, DeepMind receives access to important patient data and medical histories. News reports have alleged that DeepMind obtained the data without explicit consent from the affected patients.

BOSSES are more likely to groan than feel giddy about advances in artificial intelligence (AI). They need a strategy, but few companies can hope to own a unit like Google's DeepMind, whose algorithms not only beat the world's best Go players but made a 40% improvement in the energy efficiency of its parent's data centres. A Canadian startup, Element AI, wants to let all businesses tap into the world's best AI minds. The brain behind the new firm is Yoshua Bengio, a pioneer in "deep learning", a branch of AI. As firms such as Google and Facebook lured dozens of AI academics, some in the field expressed fears about a brain drain from academia. Mr Bengio meanwhile stayed at the University of Montreal (though in January he became an adviser to Microsoft).

Deep learning is sometimes referred to as the intersection between machine learning and artificial intelligence. It is about designing algorithms that can make robots intelligent, such a face recognition techniques used in drones to detect and target terrorists, or pattern recognition / computer vision algorithms to automatically pilot a plane, a train, a boat or a car. Many deep learning algorithms (clustering, pattern recognition, automated bidding, recommendation engine, and so on) -- even though they appear in new contexts such as IoT or machine to machine communication -- still rely on relatively old-fashioned techniques such as logistic regression, SVM, decision trees, K-NN, naive Bayes, Bayesian modeling, ensembles, random forests, signal processing, filtering, graph theory, gaming theory, and many others. Some are new, such as indexation algorithms to automate digital publishing, improve search engines, or create and manage large catalogs such as Amazon's product listing. As a result, many deep learning practitioners call themselves data scientist, computer scientist, statistician, or sometimes engineer.

Deep-learning systems tend to be one-trick wonders: they're great at the task they've been trained to do, but pretty awful at everything else. Now a new neural network from Google suggests that AI can be taught to multitask after all. Most deep-learning systems are built to solve specific problems, such as recognising animals in photos from the Serengeti or translating between languages. But if you take, for instance, an image-recognition algorithm and then retrain it to do a completely different task, such as recognising speech, it usually becomes worse at its original job. Humans don't have that issue.

During the past few years, deep learning has revolutionized nearly every field it has been applied to, resulting in the greatest leap in performance in the history of computer science. With many problems, for which we were used to seeing small, gradual improvements every year, we are now witnessing 20% -- 30% improvements within months, due to the application of deep learning. This success has also stirred lots of media and PR buzz, as a result of which, nowadays the terms "artificial intelligence", "machine learning", and "deep learning" are used very widely, and most often inaccurately and confusingly. It's important that we attempt to clarify, and demystify, the distinction between these technical terms, and then focus on their application to the field of cybersecurity. Artificial Intelligence (AI), a phrase coined by the pioneering computer scientist John McCarthy in the 1950s, is an umbrella term for all the methods and disciplines that result in any form of intelligence exhibited by machines.

Elon Musk has hired a new director of AI research at Tesla, and it may signal a plan to rethink the way its automated driving works. This week, Musk poached Andrej Karpathy, an expert on vision, deep learning, and reinforcement learning, from OpenAI, a nonprofit that Musk and others are funding that's dedicated to "discovering and enacting the path to safe artificial general intelligence." Karpathy, who will apparently report directly to Musk, is a rising star in the world of AI, having studied at Stanford with Fei-Fei Li, a leading AI expert who is now the chief scientist of Google Cloud. Li is famous in tech circles for having developed a data set of images that helped inspire a breakthrough in machine vision. Many have pointed to Karpathy's expertise in computer vision as a key asset for Tesla, and that's true.

From Uber using past trips to predict its customers' future habits to Facebook automatically tagging a picture you upload of your family, data is everywhere these days, and smart companies are using it to inform a better experience for their customers. Could the same be true for your company? When it comes to making sense of big data, enterprises are heavily invested in machine learning. Simply put, machine learning uses algorithms to find patterns in data fed to it by humans. Typically, machine learning deals with data that is relatively simple.

As with most practical AI, the experiment will use machine learning algorithms to teach the machine to react to situations politely: for instance, saying thank you when it is given something. It will then derive new manners not directly taught to it via deep learning.

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Recent advances in neural networks have inspired people to design hybrid recommendation algorithms that can incorporate both (1) user-item interaction information and (2) content information including image, audio, and text. Despite their promising results, neural network-based recommendation algorithms pose extensive computational costs, making it challenging to scale and improve upon. In this paper, we propose a general neural network-based recommendation framework, which subsumes several existing state-of-the-art recommendation algorithms, and address the efficiency issue by investigating sampling strategies in the stochastic gradient descent training for the framework. We tackle this issue by first establishing a connection between the loss functions and the user-item interaction bipartite graph, where the loss function terms are defined on links while major computation burdens are located at nodes. We call this type of loss functions "graph-based" loss functions, for which varied mini-batch sampling strategies can have different computational costs. Based on the insight, three novel sampling strategies are proposed, which can significantly improve the training efficiency of the proposed framework (up to $\times 30$ times speedup in our experiments), as well as improving the recommendation performance. Theoretical analysis is also provided for both the computational cost and the convergence. We believe the study of sampling strategies have further implications on general graph-based loss functions, and would also enable more research under the neural network-based recommendation framework.