New facial recognition technology has identified three immigration cheats at one of America's busiest airports - in just forty days. The trio - who travelled separately during August and September - were intercepted at Washington's Dulles International Airport, which processes more than 21 million people per year. According to a statement by U.S. Customs and Border Protection, they were each flagged-up by new software which scans minute differences between the person seeking entry and their passport photo. Impressively, mismatches are identified in as little as two seconds. Most recently, on Monday, a woman arriving on a flight from Accra, Ghana, presented a U.S. passport with a suspect image.

Effective data science is becoming the differentiator between business success and failure, but few have the broad range of skills and expertise needed to deliver on the promise of data science. It is both an art and a science, demanding a range of seemingly disparate skills. Much like a conductor must coordinate and guide a performance to deliver a meaningful final product, the data scientist must apply a range of toolkits and skills to not only interrogate data, but ask meaningful questions that deliver true value, and then ensure the data product drives meaningful business change. These maestros of data science are in short supply. Both globally and in South Africa, forward-thinking enterprises are looking to harness skilled data scientists to drive more than just competitive edge; they need their insights to help companies reinvent, innovate and disrupt into the future.

Many types of data can be represented as graphs. Edges may correspond to actual links in the data (e.g., users connected by some social network) or to levels of similarity induced from the data (e.g., users having liked a large common set of movies). The resulting graph is typically sparse in the sense that the number of edges is much lower than the total number of node pairs, which makes the data hard to exploit. A standard approach to the analysis of sparse graphs consists in embedding the graph in some vectorial space of low dimension, typically much smaller than the number of nodes [15, 19, 4]. Each node is represented by some vector in the embedding space so that close nodes in the graph (linked either directly or through many short paths in the graph) tend to be represented by close vectors in terms of the Euclidian distance.

Read "technology" and "children" in the same sentence, and you'll probably think about screen time or social media. But technology's implications are vastly more profound: AI, machine learning, big data and automation will fundamentally reshape the lives of our youngest generation. How might we direct the power of emerging innovations to fulfill their rights? One trailblazer addressing this question is Erica Kochi, Co-Founder of UNICEF Innovation at the United Nations Children's Fund, who was named one of TIME's most influential people in the world. Erica continues to accelerate action – unveiling a new urban tech bets opportunity just this week – and to drive crucial dialogues as Co-Chair of the World Economic Forum's Global Future Council on Human Rights.

Robotic process automation (RPA) is still relatively new to South Africa, with mainly the major banks moving to deploy it to manage certain repetitive and manual processes. But RPA presents significant promise in many sectors where manual processes delay operations and add costs in a price-sensitive market. The insurance industry is one sector that stands to achieve multiple gains from deploying RPA: through intelligent automation, they can achieve more streamlined processes, improved customer service, lower overheads and reduced risk. RPA is akin to deploying an army of artificial intelligence workers, or bots, to automate processes both in customer-facing and internal functions. From managing invoices and onboarding new customers, to validating data, assessing risk and confirming the market value of insured items, RPA tools can replace human resources; delivering outputs faster and more accurately.

A young mother and her baby visit the local nutrition center in rural Madagascar to participate in a discussion around children's nutrition and learn new cooking recipes that promote a varied diet. Persistent humanitarian crises caused by natural and man-made disasters, oppression and other emergencies have plagued our world as long as time itself. And while global relief organizations scramble to respond to these events, their work by definition is often reactive and difficult to scale. We believe that technology, like artificial intelligence (AI) combined with cloud technology, can be a game changer, helping save more lives, alleviate suffering and restore human dignity by changing the way frontline relief organizations anticipate, predict and better target response efforts. Today, in conjunction with the United Nations General Assembly meeting, we are announcing AI for Humanitarian Action, a new $40 million, five-year Microsoft program.

At this week's United Nations General Assembly, the World Bank, the United Nations, and the Red Cross teamed up with tech giants Amazon, Microsoft, and Google to announce an unlikely new tool to stop famine before it starts: artificial intelligence. The Famine Action Mechanism (FAM), as they're calling it, is the first global tool dedicated to preventing future famines -- no small news in a world where one in nine people don't have enough food. Building off of previous famine-prediction strategies, the tool will combine satellite data of things like rainfall and crop health with social media and news reports of more human factors, like violence or changing food prices. It will also establish a fund that will be automatically dispersed to a food crisis as soon as it meets certain criteria, speeding up the often-lengthy process for funding famine relief. For a famine to be declared in a country or region, three criteria have to be met: At least one in five households has an extreme lack of food; over 30 percent of children under five have acute malnutrition; and two out of 10,000 people die each day.

Software defined networking (SDN) represents a promising networking architecture that combines central management and network programmability. SDN separates the control plane from the data plane and moves the network management to a central point, called the controller, that can be programmed and used as the brain of the network. Recently, the research community has showed an increased tendency to benefit from the recent advancements in the artificial intelligence (AI) field to provide learning abilities and better decision making in SDN. In this study, we provide a detailed overview of the recent efforts to include AI in SDN. Our study showed that the research efforts focused on three main sub-fields of AI namely: machine learning, meta-heuristics and fuzzy inference systems. Accordingly, in this work we investigate their different application areas and potential use, as well as the improvements achieved by including AI-based techniques in the SDN paradigm.

Human action recognition refers to automatic recognizing human actions from a video clip, which is one of the most challenging tasks in computer vision. In reality, a video stream is often weakly-annotated with a set of relevant human action labels at a global level rather than assigning each label to a specific video episode corresponding to a single action, which leads to a multi-label learning problem. Furthermore, there are a great number of meaningful human actions in reality but it would be extremely difficult, if not impossible, to collect/annotate video clips regarding all of various human actions, which leads to a zero-shot learning scenario. To the best of our knowledge, there is no work that has addressed all the above issues together in human action recognition. In this paper, we formulate a real-world human action recognition task as a multi-label zero-shot learning problem and propose a framework to tackle this problem. Our framework simultaneously tackles the issue of unknown temporal boundaries between different actions for multi-label learning and exploits the side information regarding the semantic relationship between different human actions for zero-shot learning. As a result, our framework leads to a joint latent embedding representation for multi-label zero-shot human action recognition. The joint latent embedding is learned with two component models by exploring temporal coherence underlying video data and the intrinsic relationship between visual and semantic domain. We evaluate our framework with different settings, including a novel data split scheme designed especially for evaluating multi-label zero-shot learning, on two weakly annotated multi-label human action datasets: Breakfast and Charades. The experimental results demonstrate the effectiveness of our framework in multi-label zero-shot human action recognition.

In this interview, Tamim Saleh cuts through the hype around artificial intelligence with guidance for executives about where and how to employ AI in their businesses. In this episode of our Inside the Strategy Room podcast, senior partner Tamim Saleh cuts through the hype around artificial intelligence (AI) and offers clear guidance for executives looking to make precise strategic decisions about where and how to employ AI in their businesses. Tamim shares insights on the impact of machine vision on AI, the future of voice recognition, and the latest developments in advanced analytics, virtual assistants, and robotics. He outlines the challenges companies face when adopting AI and the steps CEOs can take to overcome them. Tamim is a senior partner in our London office, and he is with me at our Global CFO Forum, where he's speaking about AI and machine learning. Tamim, one of the things you've talked about is the notion of five different developments of AI. Tamim Saleh: Machine learning and AI are limited by the fact that when we input data as humans, first of all we are slow, and we make mistakes. One of the fastest-growing technologies is capturing data through image analytics and cameras. And the beauty of this is, cameras don't make the same mistakes we do, because they capture things the way they are, and they don't see the world the same way that we do. In fact, the spectrum is much wider than what we see. It includes infrared, et cetera.