What the ride-hailing experience shows is the extent to which the behavior of these artificial intelligence systems can diverge significantly from the trappings they adopt. Similar mirages are cast elsewhere throughout the “sharing economy” and even in the design of our social platforms. They downplay the responsibility of the platform designer, masking the more active role these technologies play in the sectors they exist in.
As the uses of artificial intelligence continue to broaden, society will increasingly confront questions around the power these technologies can and should have. As we move toward regulation, we need to question the narratives offered by companies and make sure that policy reflects reality.
High-school students in 10 schools in the Los Angeles Unified School District are learning math via data science. The program -- funded by a $12.5 million National Science Foundation grant -- includes lessons about data collection, computer programming and data-questioning strategies.
Allow me to introduce you to someone who has the potential to be very important in the future of Bitcoin. His name is Balaji Srinivasan, and he is the chairman and co-founder of 21 Inc. What is 21 Inc? 21 Inc. is the Bitcoin startup that secured the most venture capital of any Bitcoin company …
Urbanization promotes economy, mobility, access and availability of resources, but on the other hand, generates higher levels of pollution, violence, crime, and mental distress. The health consequences of the agglomeration of people living close together are not fully understood. Particularly, it remains unclear how variations in the population size across cities impact the health of the population. We analyze the deviations from linearity of the scaling of several health-related quantities, such as the incidence and mortality of diseases, external causes of death, wellbeing, and health-care availability, in respect to the population size of cities in Brazil, Sweden and the USA. We find that deaths by non-communicable diseases tend to be relatively less common in larger cities, whereas the per-capita incidence of infectious diseases is relatively larger for increasing population size. Healthier life style and availability of medical support are disproportionally higher in larger cities. The results are connected with the optimization of human and physical resources, and with the non-linear effects of social networks in larger populations. An urban advantage in terms of health is not evident and using rates as indicators to compare cities with different population sizes may be insufficient.
The non-linear health consequences of living in larger cities Luis E. C. Rocha, Anna E. Thorson, Renaud Lambiotte
The rapidly progressing digital revolution is now touching the foundations of the governance of societal structures. Humans are on the verge of evolving from consumers to prosumers, and old, entrenched theories – in particular sociological and economic ones – are falling prey to these rapid developments. The original assumptions on which they are based are being questioned. Each year we produce as much data as in the entire human history - can we possibly create a global crystal ball to predict our future and to optimally govern our world? Do we need wide-scale surveillance to understand and manage the increasingly complex systems we are constructing, or would bottom-up approaches such as self-regulating systems be a better solution to creating a more innovative, more successful, more resilient, and ultimately happier society? Working at the interface of complexity theory, quantitative sociology and Big Data-driven risk and knowledge management, the author advocates the establishment of new participatory systems in our digital society to enhance coordination, reduce conflict and, above all, reduce the “tragedies of the commons,” resulting from the methods now used in political, economic and management decision-making.
Thinking Ahead - Essays on Big Data, Digital Revolution, and Participatory Market Society Authors: Dirk Helbing ISBN: 978-3-319-15077-2 (Print) 978-3-319-15078-9 (Online)
Latest survey research from Cloud Sherpas has revealed while seven in 10 IT leaders view enterprise cloud software as central to their company’s success, only half have a formal cloud strategy in place.
While expected applications of Modular Robotic Systems (MRS) span various workspaces, scales, and structures, practical implementations of such systems lag behind their potentials in performing real-world tasks. Challenges of enhancing MRS capabilities not only are limited to designing reliable, responsive, and robust hardware, but also include developing software and algorithms that can effectively fulfill tasks through performing fundamental functions like shape-formation, locomotion, manipulation, etc. Thus, MRS solution methods must be able to resolve problems arising from the tightly-coupled kinematics of interconnected modules and their inherent limitations in resources, communication, connection strength, etc. in performing such functions through domain-specific operations including Self-reconfiguration, Flow, Gait, Self-assembly, Self-disassembly, Self-adaptation, Grasping, Collective actuation, and Enveloping. Despite the large number of developed solution methods, there is no inclusive and updated study in the literature dedicated to classifying, analyzing, and comparing their specifications and capabilities in a systematic manner. This paper aims to fill in this gap through reviewing 64 solution methods and algorithms according to their application in each operation and by investigating their capabilities in (1) modeling and simplifying MRS problems through Abstraction methods, (2) solving MRS problems through Solution and Control methods, and (3) coordinating actions of modules through Synchronization methods. Challenging issues of each solution approach along with their advantages and weaknesses are also analyzed and open problems and improvement outlooks are mentioned. Overall, this paper aims to investigate the research areas in MRS algorithms that have been evolved so far and to explore promising research directions for the future.
Modular robotic systems: Methods and algorithms for abstraction, planning, control, and synchronization Hossein Ahmadzadeh, Ellips Masehian
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