Ed Ikeguchi, chief medical officer at AiCure, an artificial intelligence (AI) and data analytics company, said 2018 was a year filled with change – both positive and negative. On the upside, the pharmaceutical industry more broadly adopted innovative technology leveraging AI and machine learning for use across R&D as well as commercial development, he told us. Regulators also got on board, with the US Food and Drug Administration (FDA) releasing a statement backing the idea of AI-enabled technology and encouraging its use in health care. Larger technology companies like Google, Apple, and Amazon also have shown greater investment and interest in the health care space, Ikeguchi noted. As one example, Amazon, JP Morgan, and Berkshire Hathaway teamed up to form a new company that aims to address US employee health care.

Four new malware strains are created every second, and the banking industry is almost 300 times as likely to get attacked by malware as other types of businesses, according to Nayeem Islam, CEO of Blue Hexagon. That's why his deep-learning cybersecurity startup, which has been in stealth mode for a year and a half and officially launched Tuesday, is making financial services one of the primary industries it serves. The company has received $31 million in funding from the venture capital firms Benchmark and Altimeter Capital. Heffernan Insurance is its first public customer. Blue Hexagon uses self-learning technology to catch network threats, Islam said.

Digital technologies are constantly evolving and finding new applications in healthcare, even while the industry is struggling with adoption and'digital transformation'. Each year new applications emerge, but the underlying technologies driving them remain the same. For 2019, we asked companies around the world one basic question: "Please indicate the key technology which you believe will have the most profound impact on the healthcare industry during 2019?" Of course, these respondents are distributed across widely different sectors – pharmaceuticals and biotechnology, medical devices, medical imaging equipment, in-vitro diagnostics, remote patient monitoring, healthcare IT and digital health solution providers – but excluding care delivery settings such as hospitals and other facilities. This means that these technologies are being viewed through a different lens, depending on which sector the respondent belongs to.

Displaced workers transition to new jobs, some of which are created by automation. The government helps to facilitate this transition via investments in training and education. Increased productivity raises incomes, lowers work hours (average work time in the U.S. has fallen more than 50% since the early 1900s5), and lowers prices, creating more demand for goods and services, leading to more jobs and broader economic growth. How well do we expect this pattern to hold with AI-enabled automation in the near future, and will they replace jobs faster than they create them?

At least 14 UK police forces have made use of crime-prediction software or plan to do so, according to Liberty. The human rights group said it had sent a total of 90 Freedom of Information requests out last year to discover which forces used the technology. It believes the programs involved can lead to biased policing strategies that unfairly focus on ethnic minorities and lower-income communities. And it said there had been a "severe lack of transparency" about the matter. Defenders of the technology say it can provide new insights into gun and knife crime, sex trafficking and other potentially life-threatening offences at a time when police budgets are under pressure.

Build stuff or buy stuff? China has long been a believer in the former to deal with a slowdown in its economy. Now it's trying to shift the emphasis to the latter. This year will be a big test of how far it's come in the transition from state-backed investment to domestic consumption as the main driver of growth. China's President Xi Jinping has warned of a "struggle" as his country faces an economic slowdown, the likes of which it hasn't seen for almost 30 years. A series of stimulus measures have been unveiled by the government not to boost the economy, but to manage the slowdown.

Since 2012, a sophisticated artificial intelligence has dug through big data to find signs of corruption in the Chinese government -- but local officials in many areas are now shutting it down, according to the South China Morning Post. One researcher involved in the program, which is portentously called "Zero Trust," told the Hong Kong newspaper that local officials might be shutting the program down because they don't "feel quite comfortable with the new technology." But the SCMP has another explanation: the AI works too well. It might notice a suspicious transfer of money, for instance, or a new car or property registered to the name of a government official's family or friends.

The Australian Human Rights Commission is conducting a project on Human Rights and New Technology (the Project). As part of the Project, the Commission and the World Economic Forum are working together to explore models of governance and leadership on artificial intelligence (AI) in Australia. This White Paper has been produced to support a consultation process that aims to identify how Australia can simultaneously foster innovation and protect human rights – as we see unprecedented growth in new technologies, such as AI. The White Paper complements the broader issues raised in the Commission's Human Rights and Technology Issues Paper. The consultation conducted on the Issues Paper and White Paper will inform the Commission's proposals for reform, to be released in mid-2019. The White Paper asks whether Australia needs an organisation to take a central role in promoting responsible innovation in AI and related technology and, if so, what that organisation could look like.

This is an integrative review that address the question, "What makes for a good explanation?" with reference to AI systems. Pertinent literatures are vast. Thus, this review is necessarily selective. That said, most of the key concepts and issues are expressed in this Report. The Report encapsulates the history of computer science efforts to create systems that explain and instruct (intelligent tutoring systems and expert systems). The Report expresses the explainability issues and challenges in modern AI, and presents capsule views of the leading psychological theories of explanation. Certain articles stand out by virtue of their particular relevance to XAI, and their methods, results, and key points are highlighted. It is recommended that AI/XAI researchers be encouraged to include in their research reports fuller details on their empirical or experimental methods, in the fashion of experimental psychology research reports: details on Participants, Instructions, Procedures, Tasks, Dependent Variables (operational definitions of the measures and metrics), Independent Variables (conditions), and Control Conditions.

Many post-disaster and -conflict regions do not have sufficient data on their transportation infrastructure assets, hindering both mobility and reconstruction. In particular, as the number of aging and deteriorating bridges increase, it is necessary to quantify their load characteristics in order to inform maintenance and prevent failure. The load carrying capacity and the design load are considered as the main aspects of any civil structures. Human examination can be costly and slow when expertise is lacking in challenging scenarios. In this paper, we propose to employ deep learning as method to estimate the load carrying capacity from crowd sourced images. A new convolutional neural network architecture is trained on data from over 6000 bridges, which will benefit future research and applications. We tackle significant variations in the dataset (e.g. class interval, image completion, image colour) and quantify their impact on the prediction accuracy, precision, recall and F1 score. Finally, practical optimisation is performed by converting multiclass classification into binary classification to achieve a promising field use performance.