The view from space has forever changed our vision on our home planet, revealing its beauty while pointing at the same time to its inherent fragility. This new perspective from above contributed to the emergence of the concept of Sustainable Development (SD), by convincing many of the need to (better) manage our (rapidly depleting) resources in a sustainable manner that would "meet the needs of the present without compromising the ability of future generations to meet their own needs". Over the last decades, the principles of SD were progressively adopted by world leaders on the occasion of a series of Earth Summits. One of the key challenges to implement SD however lies in one's ability to measure it. As stated by Lord Kelvin, "if you cannot measure it, you cannot improve it". The challenge is further compounded by the inherent global nature of the problem, which calls for global data sets.

Today the UK government publishes'Using artificial intelligence in the public sector,' an initiative led by the Office for Artificial Intelligence (OAI) and the Government Digital Service (GDS), with The Alan Turing Institute's public policy programme contributing guidance on AI ethics and safety. The new guide states that several public sector organisations are already successfully using AI for tasks ranging from fraud detection to answering customer queries. It explains how the potential uses for AI in the public sector are significant, but must be balanced with ethical, fairness and safety considerations. These ethical and safety issues are laid out in full in a section of the guide titled'Understanding artificial intelligence ethics and safety' by Dr David Leslie, Ethics Fellow in the Turing's public policy programme. This groundbreaking work is the most comprehensive guidance on the topic of AI ethics and safety in the public sector to date.

Hideki Nagafuji, a 42-year-old former Osaka Prefectural Assembly member, defeated two other contenders. Voter turnout stood at 40.83 percent. The regional party's so-called Osaka metropolis plan calls for reorganizing the prefectural capital of Osaka into special wards. In April, the party won the Osaka prefectural and mayoral elections. Nagafuji collected 137,862 votes, against 123,771 votes garnered by Tomoaki Nomura, 45, a former Sakai Municipal Assembly member, and 14,110 votes by Takashi Tachibana, 51, a former assembly member for Katsushika Ward, Tokyo.

"We will work constructively with other WTO members to undertake necessary WTO reform with a sense of urgency," the statement said. Hiroshige Seko, minister of economy, trade and industry, said that it is significant that specifics concerning WTO reform were included in a G20 ministerial statement for the first time. Improving the organization's system for resolving disputes was one of the issues raised. "We agree that action is necessary regarding the functioning of the dispute settlement system consistent with the rules as negotiated by the WTO members," the statement said. Foreign Minister Taro Kono said it was a "big feat" for Japan, which places importance on this issue, that the need to address the system was explicitly mentioned in the statement.

MetroLab Network has partnered with Government Technology to bring its readers a segment called the MetroLab Innovation of the Month Series, which highlights impactful tech, data, and innovation projects underway between cities and universities. If you'd like to learn more or contact the project leads, please contact MetroLab at info@metrolabnetwork.org for more information. In this month's installment of the Innovation of the Month series, we explore how the Vanderbilt Initiative for Smart Cities Operation and Research has been working on emergency response with the Nashville Fire Department and the Information Technology Services Department for the Metropolitan Government of Nashville and Davidson County. MetroLab's Ben Levine and Stefania Di Mauro-Nava spoke with Abhishek Dubey, senior research scientist at the Institute for Software Integrated Systems and an assistant professor in the Electrical Engineering and Computer Science department at Vanderbilt University; Geoffrey Pettet, graduate research assistant at Vanderbilt University; Colleen Herndon, project manager at the Metro Information Technology Services Department for the Metropolitan Government of Nashville and Davidson County; and Ayan Mukhopadhyay, graduate research assistant at Vanderbilt University to learn more. Stefania Di Mauro-Nava: Could you please describe what the Integrated Safety Incident Forecasting and Analysis project is?

With cyber threats growing in complexity, this world increasingly reliant on computers cannot afford to lag in security. One way we can sure we're always up-to-date is through the use of artificial intelligence (AI) and machine learning (ML) in our cybersecurity solutions. AI and ML enable cybersecurity experts to scour the cyber terrain for threats faster than any human could. The capacity of AI and ML systems to analyze large amounts of data and look at patterns enables them to deploy security solutions quickly. The way we work with cybersecurity couldn't ever hope to keep up with the ability of AI and ML to adapt to the quickly-changing threats as well as their wide offering of solutions.

The great power nations that master the use of artificial intelligence are likely to gain a tremendous military and economic benefits from the technology. The United States benefitted greatly from a relatively fast adoption of the internet, and many of its most powerful companies today are the global giants of the internet age. I believe these to be fatal assumptions. The decade ahead will make it clear that the United States must, as it has in the past, earn its prosperity and its technological leadership – something that many Americans now take completely for granted. This will involve a focus on the competitiveness of the US economy – and a willingness to continually earn its place in the international order.

There are two broad – and distinct – areas of application of AI and machine learning in which the ICRC has a particular interest: its use in the conduct of warfare or in other situations of violence; and its use in humanitarian action to assist and protect the victims of armed conflict. This paper sets out the ICRC's perspective on the use of AI and machine learning in armed conflict, the potential humanitarian consequences, and associated legal obligations and ethical considerations that should govern its development and use. AI and machine-learning systems could have profound implications for the role of humans in armed conflict, especially in relation to: increasing autonomy of weapon systems and other unmanned systems; new forms of cyber and information warfare; and, more broadly, the nature of decision-making. In the view of the ICRC, there is a need for a genuinely human-centred approach to any use of these technologies in armed conflict. It will be essential to preserve human control and judgement in applications of AI and machine learning for tasks and in decisions that may have serious consequences for people's lives, especially where they pose risks to life, and where the tasks or decisions are governed by rules of international humanitarian law.

Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.

For a broad variety of critical applications, it is essential to know how confident a classification prediction is. In this paper, we discuss the drawbacks of softmax to calculate class probabilities and to handle uncertainty in Bayesian neural networks. We introduce a new kind of prediction layer called radial prediction layer (RPL) to overcome these issues. In contrast to the softmax classification, RPL is based on the open-world assumption. Therefore, the class prediction probabilities are much more meaningful to assess the uncertainty concerning the novelty of the input. We show that neural networks with RPLs can be learned in the same way as neural networks using softmax. On a 2D toy data set (spiral data), we demonstrate the fundamental principles and advantages. On the real-world ImageNet data set, we show that the open-world properties are beneficially fulfilled. Additionally, we show that RPLs are less sensible to adversarial attacks on the MNIST data set. Due to its features, we expect RPL to be beneficial in a broad variety of applications, especially in critical environments, such as medicine or autonomous driving.