Engineers at Missouri University of Science & Technology in Rolla are developing algorithms that could provide early warnings for motorists about flooded roads. The system could warn drivers to stay off flooded roads. Researchers began the yearlong project to use artificial intelligence to enhance flood evacuation plans in February for transportation agencies in the Midwest, including the Missouri Department of Transportation. The work focuses on the Meramec River basin in eastern Missouri and the areas of Nebraska and northwest Missouri that experienced record-breaking floods in late March from the Missouri River. Artificial intelligence could help deliver that information to motorists faster so to prevent people from being stranded on flooded roads, said Suzanna Long, the chair of engineering management and systems engineering at Missouri S&T.

The UK government's plan to prevent children and teenagers from viewing pornographic content online has a major flaw that means not all porn will be blocked. Critics have called the so-called porn ban "disastrous" for people's privacy, as it will require people to share their personal data online in order to visit porn sites. But the new rules, which come into effect on 15 July, can be skirted by visiting sites that are not subject to the age verification checks. We'll tell you what's true. You can form your own view.

Daisy, one of Apple's most valued resources, eats iPhones. She's very, very good at it, and getting better: trained with a precision that would have been unimaginable just a few years ago. She is a robot, with a variety of tools built to rip the phones apart. That includes, for instance, a tool that can chill phones down so that the battery holding the glue inside becomes brittle, and it can be knocked out with two aggressive bangs; precise pins that can pick the display off the housing that surrounds it; drills that can punch into the phone and drive out the things that might make it difficult to recycle. It won't surprise anyone to hear that Apple is pretty good at making iPhones.

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.

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.

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.

Intensive care clinicians are presented with large quantities of patient information and measurements from a multitude of monitoring systems. The limited ability of humans to process such complex information hinders physicians to readily recognize and act on early signs of patient deterioration. We used machine learning to develop an early warning system for circulatory failure based on a high-resolution ICU database with 240 patient years of data. This automatic system predicts 90.0% of circulatory failure events (prevalence 3.1%), with 81.8% identified more than two hours in advance, resulting in an area under the receiver operating characteristic curve of 94.0% and area under the precision-recall curve of 63.0%. The model was externally validated in a large independent patient cohort.

Graph classification is a difficult problem that has drawn a lot of attention from the machine learning community over the past few years. This is mainly due to the fact that, contrarily to Euclidean vectors, the inherent complexity of graph structures can be quite hard to encode and handle for traditional classifiers. Even though kernels have been proposed in the literature, the increase in the dataset sizes has greatly limited the use of kernel methods since computation and storage of kernel matrices has become impracticable. In this article, we propose to use extended persistence diagrams to efficiently encode graph structure. More precisely, we show that using the so-called heat kernel signatures for the computation of these extended persistence diagrams allows one to quickly and efficiently summarize the graph structure. Then, we build on the recent development of neural networks for point clouds to define an architecture for (extended) persistence diagrams which is modular and easy-to-use. Finally, we demonstrate the usefulness of our approach by validating our architecture on several graph datasets, on which the obtained results are comparable to the state-of-the-art for graph classification.

This paper proposes the use of an optimization algorithm, namely PSO to decide the initial centroids in K-means, to eventually get better accuracy. The vectorized notation of the optimal centroids can be thought of as entities in an optimization space, where the accuracy of K-means over a random subset of the data could act as a fitness measure. The resultant optimal vector can be used as the initial centroids for K-means.