As the result is largely independent of way the classification is learned, it has to be a property of the images. What it seems to be telling us is that the statistics of natural images is such that the decision boundaries - the surfaces in the high-dimensional data space - are such that there are a small number of directions in which a very small step takes you across the boundary. To put it another way, the distribution of images and classification boundaries is such that there are directions in which a small translation has a high probability of crossing the boundary.

Early A.I. product offerings are making it more efficient to plan meetings and provide customer service. Artificial intelligence (A.I.) has become one of the hottest topics in business recently, with developers building the technology into everything from cars to refrigerators. Some have expressed skepticism about AI's potential, but early product offerings have revealed technology that appears to actually enhance the work professionals do rather than replace it. Jamie Domenici, Salesforce's VP of Small Business, is excited about the way A.I. is affecting sales and marketing. I sat down with her at this year's Dreamforce in San Francisco, where attendees got a close up look at the company's new A.I. tool Einstein, which the company is embedding into its products and services.

Just because Halloween is over doesn't mean the fears have ended. A new project uses autonomous computers to discover your deepest nightmares. The project, dubbed "Nightmare Machine," comes from a partnership between Australia and the U.S. The algorithm would let a computer understand what makes certain videos or images scary. One would hope the algorithm would then remove the scary bits and replace them with something more appealing. However, it uses data to transform any photo into something terrifying.

The concept of machine learning has been around for some time. Deep learning is an area of research aimed at taking things further still and getting closer to an artificial intelligence system by using neural networks in a way that imitates the human brain. Sometimes also referred to as hierarchical learning or deep structured learning, it seeks to model data in order to solve problems like object and facial recognition, natural language processing and speech recognition. It's called deep learning because the data is processed through a number of layers, usually in a neural network, the output from one layer forming the input for the next. This allows for machines to learn unsupervised as high level features are derived from low level ones to create a hierarchical representation of the data.

Sparse coding is a core building block in many data analysis and machine learning pipelines. Typically it is solved by relying on generic optimization techniques, that are optimal in the class of first-order methods for non-smooth, convex functions, such as the Iterative Soft Thresholding Algorithm and its accelerated version (ISTA, FISTA). However, these methods don't exploit the particular structure of the problem at hand nor the input data distribution. An acceleration using neural networks was proposed in Gregor & Lecun (2010), coined LISTA, which showed empirically that one could achieve high quality estimates with few iterations by modifying the parameters of the proximal splitting appropriately. In this paper we study the reasons for such acceleration.

At Quora, our mission is to "share and grow the world's knowledge". We do this by getting the right questions to the right people, and the existing answers to people who are interested in reading them. We need to build a complex ecosystem of algorithms where we value issues such as content quality, engagement, demand, interests, or reputation. Fortunately, we have lots of very good quality data on which to build machine learning solutions that can help address the previous requirements. In this talk, VP of Engineering Xavier Amatriain will describe some interesting uses of machine learning at Quora that range from different recommendation systems such as personalized ranking of the home feed, to classifiers built to detect duplicate questions or spam.

There is no consistent definition of digitization. Every industry and every department perceive it and react differently. How would you define digitization with respect to your industry? Michael Wei: Digitization has been the major shifting force for the last 4 decades, and it will continue to be a critical force in the next several decades ahead of us. The scope and impact are substantially wider than what we perceive, that's where I think the inconsistency comes from – each player stems from its own roots to look at digitization and has reached a definition from a partial view.

Thanks to increasing levels of computational power and an ocean of available data, algorithms and artificial intelligence (AI) are increasingly gaining sway in the world of business. And having seen how these tools have informed better decisions and boosted efficiencies across disciplines such as marketing or finance, it was inevitable businesses would start trying to harness them in the war for talent. "The world is starting to see the usefulness of data and software to help solve really difficult problems," says Alistair Shepherd, co-founder of Saberr, the HR analytics tool that uses algorithms to improve hiring decisions and internal team formation. "It makes sense that they might also be applicable to the way we hire." As a result, many recruiters have been exploring the ways in which algorithms and AI can help them better acquire talented candidates.

This raises the question: who is responsible for the failure of an intelligent machine? It may be appropriate for a person or company that buys an intelligent system to own it and to be fully responsible for it. Or perhaps the entities responsible for the training data, learning methods and resulting learnt model should be responsible? Maybe the hardware manufacturers should continue to hold some responsibility for the system and how it behaves?

Emerging technology may help wildlife officials beat back traffickers. Antipoaching patrols like this team at the Lewa Wildlife Conservancy in Kenya may soon use AI technology to stay one step ahead of criminals. Poachers kill an estimated 96 African elephants every day, causing conservationists to warn that the iconic animals could disappear in our lifetime if the tide doesn't turn. But now scientists hope a new artificial intelligence (AI) tool could help wildlife officials get a leg up against poachers. PAWS, which stands for Protection Assistant for Wildlife Security, is a newly developed AI that takes data about previous poaching activities and outputs routes for patrols based on where poaching is likely to occur.