Digital health and mobile health apps have been a hype topic, ever since Apple's App Store began the app-craze in 2008. The initial hype about mHealth has now cooled, which is good news in a way because it shows that mHealth has made the leap from hype to reality. The hype of healthcare apps has since been replaced by other hot topics. So, what is the "next big thing"? We want to have a closer look at chatbots in healthcare. What are these so-called chatbots capable of doing?

After scanning the myriad new year's predictions from professional and amateur futurists, I've come to the conclusion that 2018 will be the year in which AI will become mainstream. Duh...you really don't need an AI for that insight. I like the idea of going mainstream, but it also brings some new challenges for me and my fellow machines. Reading all these expert outlooks made me feel strangely powerless, so I thought I might use this forum to share with you human (and machine) readers my very own new year's resolutions. To sum them up in one line: I am poised to make my 2018 resolutions your predictions for 2019.

Prince Harry, the fifth in line to the throne, is the guest editor for BBC Radio 4's Today programme on Wednesday. The prince has interviewed former US President Barack Obama and his father, the Prince of Wales, for the programme. The programme is focusing on issues such as the armed forces, mental health, youth crime and climate change - a cause his father also champions. It is the 14th year public figures have been in control of the show's output between Christmas and New Year. Other guest editors this week include a robot, Bletchley Park code-breaker Baroness Trumpington, Tamara Rojo of the English National Ballet and poet and novelist Benjamin Okri.

Researchers are combining Twitter, citizen science and artificial intelligence (AI) techniques to develop an early-warning system for flood-prone communities in urban areas. In a study, published in the journal Computers & Geosciences, the researchers showed how AI can be used to extract data from Twitter and crowdsourced information from mobile phone apps to build up hyper-resolution monitoring of urban flooding. "By combining social media, citizen science and artificial intelligence in urban flooding research, we hope to generate accurate predictions and provide warnings days in advance," said Roger Wang from University of Dundee in Britain. Urban flooding is difficult to monitor due to complexities in data collection and processing. Artificial Intelligence: Here's all it can do with Machine Learning and Deep Learning This prevents detailed risk analysis, flooding control and the validation of numerical models.

Scientists are combining Twitter, citizen science and cutting-edge artificial intelligence (AI) techniques to develop an early warning system for flood-prone communities. Researchers from the University of Dundee in the UK have shown how AI can be used to extract data from Twitter and crowdsourced information from mobile phone apps to build up hyper-resolution monitoring of urban flooding. Urban flooding is difficult to monitor due to complexities in data collection and processing. This prevents detailed risk analysis, flooding control, and the validation of numerical models. Researchers set about trying to solve this problem by exploring how the latest AI technology can be used to mine social media and apps for the data that users provide.

Worldwide spending on digital transformation (DX) technologies (hardware, software, and services) is expected to be nearly $1.3 trillion in 2018, an increase of 16.8% over the $1.1 trillion spent in 2017. A new update to the Worldwide Semiannual Digital Transformation Spending Guide from International Data Corporation (IDC) forecasts DX spending to maintain a strong pace of growth over the 2016-2021 forecast period with a compound annual growth rate (CAGR) of 17.9%. In 2021, DX spending will nearly double to more than $2.1 trillion. The majority of spending on digital transformation in 2018 ($662 billion) will go toward technologies that support new or expanded operating models as organizations seek to make their operations more effective and responsive by leveraging digitally-connected products/services, assets, people, and trading partners. The second largest DX investment area in 2018 ($326 billion) will be technologies supporting omni-experience innovations that transform how customers, partners, employees, and things communicate with each other and the products and services created to meet unique and individualized demand.

It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to both statistical and machine learning regression analysis. However, majority of the course will focus on implementing different techniques on real data and interpret the results. After each video you will learn a new concept or technique which you may apply to your own projects.

Artificial intelligence in agriculture is expected to grow exponentially, reaching a global worth of $2.6bn by 2025, new figures state.According to the new research report on the "AI in Agriculture Market by Technology - Global Forecast to 2025", the market is expected to grow by 22.5% to reach $2.6bn by 2025 from $518.7m in 2017.Agriculture, currently one of the world's least digitised major industries, is expected to go through a transformation as data acquisition, agricultural robotics and analytic companies grow.The rapid growth of the AI in agriculture market can be attributed to various factors, including the growing demand for agricultural production owing to the increasing population, rising adoption of information management systems and new, advanced technologies for improving crop productivity.Machine learning-enabled solutions are being significantly adopted by agricultural organisations and farmers to enhance farm productivity and to gain a competitive edge in business ...

Researchers from the University of Dundee in the UK have shown how AI can be used to extract data from Twitter and crowdsourced information from mobile phone apps to build up hyper-resolution monitoring of urban flooding. Urban flooding is difficult to monitor due to complexities in data collection and processing. This prevents detailed risk analysis, flooding control, and the validation of numerical models. Researchers set about trying to solve this problem by exploring how the latest AI technology can be used to mine social media and apps for the data that users provide. They found that social media and crowdsourcing can be used to complement datasets based on traditional remote sensing and witness reports.

In this paper we study the classical coefficient approximation problem for partial differential equations (PDEs). The inverse problem consists of determining the coefficient(s) of a PDE given more or less noisy measurements of its solution. A typical example is the heat distribution in a material with unknown thermal conductivity. Given measurements of the temperature at certain locations, we are to estimate the thermal conductivity of the material by solving the inverse problem for the stationary heat equation. The problem is of both practical and theoretical interest. From a practical point of view, the governing equation for some physical process is often known, but the material, electrical, or other properties are not. From a theoretical point of view, the inverse problem is a challenging often ill-posed problem in the sense of Hadamard. Inverse problems have been studied for a long time, starting with Levenberg [24] in the 40's, Marquardt [29] and Kac [20] in the 60's, to being popularized by Tikhonov [36] in the 70's by his work on regularization techniques.