Since September, residents on South Korea's Jeju island have been spotting small packs of what appear to be bags of Chinese tea washed ashore. Upon closer inspection, however, they were found to contain ketamine. Some 28kg (62 lbs) of the drug, wrapped in foil and labelled with the Chinese character for tea, have been found on at least eight occasions, police say. Ketamine is used as an anaesthetic in medical procedures, but its recreational use is illegal in South Korea. It can cause severe physical and mental damage, including to the heart and lungs, when misused.

Platooning or cooperative adaptive cruise control (CACC) has been investigated for decades, but debate about its lasting impact is still ongoing. Even though platooning benefits and platoon formation are rather well understood for trucks, this is less clear for passenger cars, which have a higher heterogeneity in trips and drivers' preferences. Most importantly, it remains unclear how to form platoons of passenger cars in order to optimize the personal benefit for the individual driver. To this end, in this paper, we propose a novel platoon formation algorithm that optimizes the personal benefit for drivers of individual passenger cars. For computing vehicle-to-platoon assignments, the algorithm utilizes a new metric that we propose to evaluate the personal benefits of various driving systems, including platooning. By combining fuel and travel time costs into a single monetary value, drivers can estimate overall trip costs according to a personal monetary value for time spent. This provides an intuitive way for drivers to understand and compare the benefits of driving systems like human driving, adaptive cruise control (ACC), and, of course, platooning. Unlike previous similarity-based methods, our proposed algorithm forms platoons only when beneficial for the driver, rather than for the sake of platooning only. Results of a large-scale simulation study demonstrate that our proposed algorithm outperforms normal ACC as well as previous similarity-based platooning approaches by balancing fuel savings and travel time, independent of traffic and drivers' time cost.

This chapter focuses on the self-driving technology from a control perspective and investigates the control strategies used in autonomous vehicles and advanced driver-assistance systems from both theoretical and practical viewpoints. First, we introduce the self-driving technology as a whole, including perception, planning and control techniques required for accomplishing the challenging task of autonomous driving. We then dwell upon each of these operations to explain their role in the autonomous system architecture, with a prime focus on control strategies. The core portion of this chapter commences with detailed mathematical modeling of autonomous vehicles followed by a comprehensive discussion on control strategies. The chapter covers longitudinal as well as lateral control strategies for autonomous vehicles with coupled and de-coupled control schemes. We as well discuss some of the machine learning techniques applied to autonomous vehicle control task. Finally, we briefly summarize some of the research works that our team has carried out at the Autonomous Systems Lab and conclude the chapter with a few thoughtful remarks.

While the purpose of most fake news is misinformation and political propaganda, our team sees it as a new type of myth that is created by people in the age of internet identities and artificial intelligence. Seeking insights on the fear and desire hidden underneath these modified or generated stories, we use machine learning methods to generate fake articles and present them in the form of an online news blog. This paper aims to share the details of our pipeline and the techniques used for full generation of fake news, from dataset collection to presentation as a media art project on the internet. Keywords: Fake news, Article generation, LSTM, RNN, Language model, Machine learning, AI, Media art, Internet art, Web, Blog, Human-AI Co-Creation Introduction Is fake news a new type of myth that people are creating in the age of internet and artificial intelligence? K. Shu et al says fake news can have many definitions, and one narrow definition is "a news article that is intentionally and verifiably false."

MEPs have warned European countries must "seriously" consider introducing a general basic income to prepare for wide scale unemployment that could come as a result of robots taking over manual jobs. A draft report, tabled by a socialist MEP Mady Delvaux-Stehres, warns preparations must be made for what it describes as the "technological revolution" currently taking place, including provisions for the "possible effects on the labour market of robotics". The report, which passed by 17 votes to two and will be put in front of the entire European Parliament in February, urges member states to consider a general basic income in preparation for robots taking over people's jobs. It states: "In the light of the possible effects on the labour market of robotics and AI a general basic income should be seriously considered, and invites all Member States to do so." The resolution also suggests that a system of reporting on how robotics are affecting the economic results of companies should be established "for the purpose of taxation and social security contributions".

As in the global survey, we are concerned with platform business models and the design choices that allow these business models to grow. We find the term platform, which is well-established in economic and management literature, offers a more useful and accurate term than some of the terms that have been used such as "share economy companies," "internet companies" or, even more broadly, "tech companies." Network effects are a key characteristic that distinguish platforms from other business models. As more users engage with a platform, the more attractive the platform becomes to potential new users. When more users attract more users, a dynamic is created that in turn triggers a self-reinforcing cycle of growth.