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.

A very popular recipe in the data science world is IPython, scipy, Jupyter and matplotlib. This is because it is very convenient to collaborate and share examples by publishing a notebook that can be quickly put on the internet, or shared. Lately this recipe has been used to create and publish deep learning examples with Tensorflow. Here are the projects, if you just want the code well here it is. Although this recipe is very popular, little has been done up to this point, to emulate a similar pattern for deeplearning4j, which is the most popular deeplearning platform for Java.

Are you concerned about securing your users and data in cloud based collaboration applications like Office 365? You're not alone. Over 35% of Microsoft Exchange installed base is now on Office 365. Many of these enterprises are actively seeking to extend the same level of security and consistent policies they have in place for existing on-premise and cloud applications, to Office 365. Consider these statistics from IDC: • Over 50% enterprises have users that access their Office 365 applications using unmanaged mobile devices • Over 90% of threats to enterprises emanate from email • 65% of threats go undetected for weeks/months IT administrators lose traditional visibility and control when enterprises move email, content creation, file sharing, and collaboration to the cloud; making it harder to detect inappropriate behavior. This makes it critical for organizations to extend the basic security capabilities of Office 365 and ensure consistency in the level of security across all their cloud services.

The idea of artificial intelligence (AI) -- autonomous computers that can learn independently -- makes some people extremely uneasy, regardless of what the computers in question might be doing. Those individuals probably wouldn't find it reassuring to hear that a group of researchers is deliberately training computers to get better at scaring people witless. The project, appropriately enough, is named "Nightmare Machine." Digital innovators in the U.S. and Australia partnered to create an algorithm that would enable a computer to understand what makes certain images frightening, and then use that data to transform any photo, no matter how harmless-looking, into the stuff of nightmares. Images created by Nightmare Machine are unsettling, to say the least.

A team of Google Brain researchers used neural networks to develop artificial intelligence-generated (AI) encryption of information processed by the networks. In a research paper entitled "Learning to Protect Communications with Adversarial Neural Cryptography," Martín Abadi and David G. Andersen of Google's deep learning project demonstrated that neural networks could develop their own encryption methods without having been "taught" cryptographic algorithms. Two of the neural networks, code-named Alice and Bob, developed an ability to prevent a third network, named Eve, from "eavesdropping" on their communication. The neural networks were able to complete increasingly complex tasks, such as generating realistic images and solving multiagent problems.