It's actually, like, kind of a problem that affects, um, roughly 74 per cent of people. Whether it's long pauses, or the use of'hedging' language (see previous sentence), the way you speak can negatively affect your credibility. Focus all that anxious blinking on one particular, bewildered person? Orai is a mobile public speaking course designed to tackle this specific problem, and in doing so, transforms your smartphone into a speaking coach. Created by engineering students at Drexel University, Orai helps you curate your word choices.

OCBC has become the first bank in Singapore to launch a human resource (HR) mobile app for its employees. The app, called "HR in your pocket", includes an artificial intelligence (AI)-powered chatbot that is integrated with the bank's HR information system. This chatbot can provide responses that are specific to each employee. Examples of use include leave applications, tracking of medical and lifestyle expense reimbursement claims, internal job postings, and the bank's people directory. Praveen Raina, SVP, group operations and technology, OCBC Bank, says the app "helps to boost productivity as it frees up resources and time that could be used for other purposes".

In working to improve heart health care diagnosis, Cardiotrack came across an unsettling statistic – almost 43 percent of a test group that had recently gone through cardiac tests showed an abnormality in their ECG scans. It was one of those moments when you could almost hear a pin drop, as we realized how large the cardiovascular problem is and how the fight to rein it in needs to be powered up. Deaths caused by CVDs still top the global list. The conventional way of dealing with the issue continues to be the most prevalent method, i.e. get a heart health checkup done regularly. And keep cholesterol and blood pressure under control.

In implementing AI, Alibaba is applying numerous complex machine-learning methods and disciplines, such as online machine learning, transfer learning, deep learning, artificial neural networks and reinforcement learning. The latter mimics the way the human brain learns through experience. Simply put, the computer "learns" to recognize patterns in data that lead to right and wrong answers through an iterative process, similar to the way a child learns by trial-and-error to throw a ball. This technology depends on the creation of software algorithms that enable computers to reach correct conclusions by being fed huge numbers of examples, building up a base of "experiences" that can be applied to recognize patterns and solve problems in new situations. These learning machines become smarter and more accurate as they are given more and more data. This means Alibaba, with its vast computer resources and access to online data from hundreds of millions of consumers, is well positioned to introduce practical, real-world AI applications into everyday use. The company over the last 17 years has built up one of the world's largest distributed-computing networks, allowing it to marshall the computational horsepower necessary for AI. Alibaba's cloud computing subsidiary, one of the world's top 5 public cloud providers, has developed an operating system called Apsara that organizes data centers into a computational engine that can process more than 175,000 transactions a second.

Go is an ancient, aristocratic Chinese board game that's reputed to have as many possible moves as there are atoms in the universe. And Google recently trained an artificial intelligence computer to play against one of the best human players in the world. At Google's Future of Go Summit, 19-year-old Chinese Go prodigy Ke Jie was defeated by the AI AlphaGo in a three-match series. AI evangelists are happy with the win, but AI doomsayers are worried it's coming for our jobs next. And China is just mad that an American company beat the world at a Chinese game.

In April, United Airlines hit a huge pocket of public relations turbulence after a passenger was forcibly removed from one of its partners' airplanes. The incident raised questions about blindly following procedures, passenger rights, and United's executive leadership. Here's another question it raised: Could artificial intelligence (AI) have prevented the embarrassing drama from even happening? Get the latest insights with our CIO Daily newsletter. AI and machine learning are already impacting many areas of business, such as marketing, as well as most industries, including retail.

I was recently invited to moderate a discussion at the excellent Fuel 2017 technology and innovation conference, hosted by Belgian media company Medialaan. We really enjoyed the partnership between our company Nexxworks and medialaan to set up this event. I really loved the panel debate, so happy to share the full video and some key results in this blog. As we enter a new phase of automation and Artificial Intelligence, we are looking at an incredibly exciting time for marketing and customer experience, with huge benefits for the consumer and faster than real-time customer service. The idea of hyper-personalised offerings such as Amazon Go, where you can just walk into a store and take what you want, with no need for waiting in line or stopping for payment, is starting to take hold; everyone is talking about the AI takeover.

The terrifying faces may look like creatures from a horror movie, but these digital images were actually generated by artificial intelligence (AI). Pix2pix project has unleashed a new tool that analyzes portraits and fills them in with colors and textures using a technique called generative adversarial networks (GANs). During the process, the system determines if its result match the sketch and will keep repeating the generation process until its own passes as'real' – regardless of how nightmarish the results may look. The terrifying faces may look like creatures from a horror movie, but these digital images were actually generated by artificial intelligence (AI). Users are presented with an input box and an output box and are prompted to draw a face in input, select process and in seconds, the AI will reveal its version of the sketch.

In the 'Big Data' era, many real-world applications like search involve the ranking problem for a large number of items. It is important to obtain effective ranking results and at the same time obtain the results efficiently in a timely manner for providing good user experience and saving computational costs. Valuable prior research has been conducted for learning to efficiently rank like the cascade ranking (learning) model, which uses a sequence of ranking functions to progressively filter some items and rank the remaining items. However, most existing research of learning to efficiently rank in search is studied in a relatively small computing environments with simulated user queries. This paper presents novel research and thorough study of designing and deploying a Cascade model in a Large-scale Operational E-commerce Search application (CLOES), which deals with hundreds of millions of user queries per day with hundreds of servers. The challenge of the real-world application provides new insights for research: 1). Real-world search applications often involve multiple factors of preferences or constraints with respect to user experience and computational costs such as search accuracy, search latency, size of search results and total CPU cost, while most existing search solutions only address one or two factors; 2). Effectiveness of e-commerce search involves multiple types of user behaviors such as click and purchase, while most existing cascade ranking in search only models the click behavior. Based on these observations, a novel cascade ranking model is designed and deployed in an operational e-commerce search application. An extensive set of experiments demonstrate the advantage of the proposed work to address multiple factors of effectiveness, efficiency and user experience in the real-world application.

In several realistic situations, an interactive learning agent can practice and refine its strategy before going on to be evaluated. For instance, consider a student preparing for a series of tests. She would typically take a few practice tests to know which areas she needs to improve upon. Based of the scores she obtains in these practice tests, she would formulate a strategy for maximizing her scores in the actual tests. We treat this scenario in the context of an agent exploring a fixed-horizon episodic Markov Decision Process (MDP), where the agent can practice on the MDP for some number of episodes (not necessarily known in advance) before starting to incur regret for its actions. During practice, the agent's goal must be to maximize the probability of following an optimal policy. This is akin to the problem of Pure Exploration (PE). We extend the PE problem of Multi Armed Bandits (MAB) to MDPs and propose a Bayesian algorithm called Posterior Sampling for Pure Exploration (PSPE), which is similar to its bandit counterpart. We show that the Bayesian simple regret converges at an optimal exponential rate when using PSPE. When the agent starts being evaluated, its goal would be to minimize the cumulative regret incurred. This is akin to the problem of Reinforcement Learning (RL). The agent uses the Posterior Sampling for Reinforcement Learning algorithm (PSRL) initialized with the posteriors of the practice phase. We hypothesize that this PSPE + PSRL combination is an optimal strategy for minimizing regret in RL problems with an initial practice phase. We show empirical results which prove that having a lower simple regret at the end of the practice phase results in having lower cumulative regret during evaluation.