Artificial intelligence (AI) could displace millions of jobs in the future, damaging growth in developing regions such as Africa, says Ian Goldin, professor of globalisation and development at Oxford University. I have spent my career in international development, and in recent years have established a research group at Oxford University looking at the impact of disruptive technologies on developing economies. Perhaps the most important question we have looked at is whether AI will pose a threat - or provide new opportunities - for developing regions such as Africa. Optimists say that such places could use rapidly advancing AI systems to boost productivity and leapfrog ahead. But I am becoming increasingly concerned that AI will, in fact, block the traditional growth path by replacing low-wage jobs with robots. As Kai-Fu Lee, a Beijing-based venture capitalist who invests in artificial intelligence, tells us, AI is potentially the most revolutionary technology to emerge this century.

With more than 300M people in the United States, no one has the same fingerprint. Similar to your fingerprint, U.S. Bank realizes that your banking and financial needs and expectations are uniquely yours. Yet too often, banks and finance institutions apply broad brush-strokes to target your perceived needs and wants instead of monitoring the breadcrumbs and signals that customers give every day that infer what they want. Seeking to understand what makes you unique and your journey to improving financial success, U.S. Bank is using AI and ML to predict and deeply personalize the banking experience for its customers, bringing new products and better solutions to their financial needs, ultimately being one step ahead of their customers. If you have ever been a victim of suspected fraudulent activity on your account, you know it is a frustrating and disruptive experience.

Your reporting on the use of facial recognition in China for "minority identification" is a stark reminder that the battle over the future of artificial intelligence will not simply be about who gathers the top scientists or who is first to innovate. It will also be about who is able to preserve fundamental rights during a period of rapidly changing technology. The White House has already made some progress on this front, highlighting American values, including privacy and civil liberties, in an executive order earlier this year, and backing an important international framework at the Organization for Economic Cooperation and Development. But there is much more to be done. The United States must work with other democratic countries to establish red lines for certain A.I. applications and ensure fairness, accountability and transparency as A.I. systems are deployed.

During the past 50 years, the frequency of recorded natural disasters has surged nearly five-fold. In this blog, I'll be exploring how converging exponential technologies (AI, robotics, drones, sensors, networks) are transforming the future of disaster relief--how we can prevent them in the first place and get help to victims during that first golden hour wherein immediate relief can save lives. When it comes to immediate and high-precision emergency response, data is gold. Already, the meteoric rise of space-based networks, stratosphere-hovering balloons, and 5G telecommunications infrastructure is in the process of connecting every last individual on the planet. Aside from democratizing the world's information, however, this upsurge in connectivity will soon grant anyone the ability to broadcast detailed geo-tagged data, particularly those most vulnerable to natural disasters.

Irish agtech company Cainthus uses vision technology to improve dairy herd management. Ireland's multi-generations of dairy farmers know a thing or two about raising dairy cows. Its more than 18,000 dairy farmers tend 1.4 million animals and are recognized globally for productivity and quality. So, it's no surprise that an Irish agtech company called Cainthus would invent a way to use artificial intelligence--the same technology developed for terrorist detection of humans--to manage dairy cows. At its simplest, Cainthus' technology has been described as facial recognition for cows, but Cainthus CEO Aidan Connolly explains that it is actually much more.

The White House is deliberately engaging with "like-minded international allies" to assist in the stewardship of artificial intelligence and help the world recognize its full potential, Assistant Director for AI in the Office of Science and Technology Policy Lynne Parker said Thursday. "There are a lot of conversations internationally right now on AI," Parker said at the National Academy of Public Administration's Forum on Artificial Intelligence, held in Washington. "And we are leading many of those conversations." Parker explained that the Organisation for Economic Co-operation and Development, the G7 and G20 international forums and organizations within the United Nations are all addressing the appropriate use of AI at a global level, and America's top federal officials are actively involved in those efforts. For instance, she noted OECD is likely to publish recommendations on governments' use of AI in May.

Modern e-commerce catalogs contain millions of references, associated with textual and visual information that is of paramount importance for the products to be found via search or browsing. Of particular significance is the book category, where the author name(s) field poses a significant challenge. Indeed, books written by a given author (such as F. Scott Fitzgerald) might be listed with different authors' names in a catalog due to abbreviations and spelling variants and mistakes, among others. To solve this problem at scale, we design a composite system involving open data sources for books as well as machine learning components leveraging deep learning-based techniques for natural language processing. In particular, we use Siamese neural networks for an approximate match with known author names, and direct correction of the provided author's name using sequence-to-sequence learning with neural networks. We evaluate this approach on product data from the e-commerce website Rakuten France, and find that the top proposal of the system is the normalized author name with 72% accuracy.

In this paper, we will provide an introduction to the derivative-free optimization algorithms which can be potentially applied to train deep learning models. Existing deep learning model training is mostly based on the back propagation algorithm, which updates the model variables layers by layers with the gradient descent algorithm or its variants. However, the objective functions of deep learning models to be optimized are usually non-convex and the gradient descent algorithms based on the first-order derivative can get stuck into the local optima very easily. To resolve such a problem, various local or global optimization algorithms have been proposed, which can help improve the training of deep learning models greatly. The representative examples include the Bayesian methods, Shubert-Piyavskii algorithm, Direct, LIPO, MCS, GA, SCE, DE, PSO, ES, CMA-ES, hill climbing and simulated annealing, etc. This is a follow-up paper of [18], and we will introduce the population based optimization algorithms, e.g., GA, SCE, DE, PSO, ES and CMA-ES, and random search algorithms, e.g., hill climbing and simulated annealing, in this paper. For the introduction to the other derivative-free optimization algorithms, please refer to [18] for more information.

In this paper, we will provide an introduction to the derivative-free optimization algorithms which can be potentially applied to train deep learning models. Existing deep learning model training is mostly based on the back propagation algorithm, which updates the model variables layers by layers with the gradient descent algorithm or its variants. However, the objective functions of deep learning models to be optimized are usually non-convex and the gradient descent algorithms based on the first-order derivative can get stuck into the local optima very easily. To resolve such a problem, various local or global optimization algorithms have been proposed, which can help improve the training of deep learning models greatly. The representative examples include the Bayesian methods, Shubert-Piyavskii algorithm, Direct, LIPO, MCS, GA, SCE, DE, PSO, ES, CMA-ES, hill climbing and simulated annealing, etc. One part of these algorithms will be introduced in this paper (including the Bayesian method and Lipschitzian approaches, e.g., Shubert-Piyavskii algorithm, Direct, LIPO and MCS), and the remaining algorithms (including the population based optimization algorithms, e.g., GA, SCE, DE, PSO, ES and CMA-ES, and random search algorithms, e.g., hill climbing and simulated annealing) will be introduced in the follow-up paper [18] in detail.

Smartphones and portable devices have become ubiquitous and part of everyone's life. Due to the fact of its portability, these devices are perfect to record individuals' traces and life-logging generating vast amounts of data at low costs. These data is emerging as a new source for studies in human mobility patterns raising the number of research projects and techniques aiming to analyze and retrieve useful information from it. The aim of this paper is to explore GPS raw data from different individuals in a community and apply data mining algorithms to identify meaningful places in a region and describe user's profiles and its similarities. We evaluate the proposed method with a real-world dataset. The experimental results show that the steps performed to identify points of interest (POIs) and further the similarity between the users are quite satisfactory serving as a supplement for urban planning and social networks.