Artificial Intelligence (AI) and space are both popular subjects in the current policy climate. AI techniques are being applied to space datasets and accelerating progress in the satellite and space industry through natural language processing, machine vision and advanced analytics. The combination of AI and space could play an integral role in increasing global connectivity and closing the digital divide. AI space services face the same problems as terrestrial AI services. They are exposed to the same policy challenges when delivered through a fibre network as they are when transmitted wirelessly from a satellite.

LONDON – Helicopter cash, climate crises, smart cities and the space economy -- investors have all those possibilities ahead as they enter the third decade of the 21st century. They go into the new decade with a spring in their step after watching world stocks add over $25 trillion in value in the past 10 years and a bond rally put $13 trillion worth of bond yields below zero. They also saw internet-based firms transform the way humans work, shop and relax. Now investors are positioning for the tech revolution's next 10 years. Could we see a repeat of the Roaring '20, as the 1920s were known -- years of prosperity, technological innovation and such social developments as women winning the right to vote?

Every company is going to experience a cyberattack; what's hard to know is how to prepare and how to respond. Protecting an industrial process is a lot more complicated than downloading the latest anti-virus software, and most executives do not know where to begin. More than half of electric utility executives surveyed by the Ponemon Institute, which studies cybersecurity, said they expect a cyberattack on a significant piece of infrastructure in the next 12 months. Only 42 percent said their defenses were high. They listed their problems as lack of skilled workers, fragmented control systems and slow detection of system breaches. Only 31 percent said they were prepared to respond to an attack.

ISIS is quickly recruiting to supplant existing al-Shabab fighters in Somalia to declare a more entrenched presence in the horn of Africa. Three drone airstrikes on Sunday against the Al Qaeda-linked Islamic terrorist group Al-Shabab in Somalia killed four militants, according to the U.S. military. U.S. Africa Command officials said an initial assessment concluded that two airstrikes killed two militants and destroyed two vehicles in Qunyo Barrow, and the third airstrike killed two militants in Caliyoow Barrow. The precision airstrikes, which were in coordination with the Somali government, came a day after a truck bombing in Somalia's capital killed at least 78 people. People salvaging goods after a car bomb destroyed shops in Mogadishu, Somalia, on Saturday.

The use of face recognition software by governments is a current topic of controversy around the globe. The world's major powers, primarily the United States and China, have made major advances in both development and deployment of this technology in the past decade. Both the US and China have been exporting this technology to other countries. The rapid spread of facial recognition systems has alarmed privacy advocates concerned about the increased ability of governments to profile and track people, as well as private companies like Facebook tying it to intimately detailed personal profiles. A recent study by the US National Institute of Standards and Technology (NIST) that examines facial recognition software vendors has found that there is definitely some merit to claims of racial bias and poor levels of accuracy in specific demographics.

This publication draws on a wide range of expertise to illuminate the year ahead. Even so, all our contributors have one thing in common: they are human. But advances in technology mean it is now possible to ask an artificial intelligence (AI) for its views on the coming year. We asked an AI called GPT-2, created by OpenAI, a research outfit. GPT-2 is an "unsupervised language model" trained using 40 gigabytes of text from the internet.

This paper proposes Evolutionary Multi-objective Optimization (EMO)-based Adversarial Example (AE) design method that performs under black-box setting. Previous gradient-based methods produce AEs by changing all pixels of a target image, while previous EC-based method changes small number of pixels to produce AEs. Thanks to EMO's property of population based-search, the proposed method produces various types of AEs involving ones locating between AEs generated by the previous two approaches, which helps to know the characteristics of a target model or to know unknown attack patterns. Experimental results showed the potential of the proposed method, e.g., it can generate robust AEs and, with the aid of DCT-based perturbation pattern generation, AEs for high resolution images.

We investigate heterogenous employment effects of Flemish training programmes. Based on administrative individual data, we analyse programme effects at various aggregation levels using Modified Causal Forests (MCF), a causal machine learning estimator for multiple programmes. While all programmes have positive effects after the lock-in period, we find substantial heterogeneity across programmes and types of unemployed. Simulations show that assigning unemployed to programmes that maximise individual gains as identified in our estimation can considerably improve effectiveness. Simplified rules, such as one giving priority to unemployed with low employability, mostly recent migrants, lead to about half of the gains obtained by more sophisticated rules.

Urban air pollution has been linked to various human health concerns, including cardiopulmonary diseases. Communities who suffer from poor air quality often rely on experts to identify pollution sources due to the lack of accessible tools. Taking this into account, we developed Smell Pittsburgh, a system that enables community members to report odors and track where these odors are frequently concentrated. All smell report data are publicly accessible online. These reports are also sent to the local health department and visualized on a map along with air quality data from monitoring stations. This visualization provides a comprehensive overview of the local pollution landscape. Additionally, with these reports and air quality data, we developed a model to predict upcoming smell events and send push notifications to inform communities. We also applied regression analysis to identify statistically significant effects of push notifications on user engagement. Our evaluation of this system demonstrates that engaging residents in documenting their experiences with pollution odors can help identify local air pollution patterns, and can empower communities to advocate for better air quality. All citizen-contributed smell data are publicly accessible and can be downloaded from https://smellpgh.org.

AI is being increasingly used to make important decisions. Many AI experts (including Jeff Dean, head of AI at Google, and Andrew Ng, founder of Coursera and deeplearning.ai) I am an AI researcher, and I'm worried about some of the societal impacts that we're already seeing. At the end, I'll briefly share some positive ways that we can try to address these. Before we dive in, I need to clarify one point that is important to understand: algorithms (and the complex systems they are a part of) can make mistakes. These mistakes come from a variety of sources: bugs in the code, inaccurate or biased data, approximations we have to make (e.g.