The annual exercise of looking forward to all the exciting innovations the next year can reasonably be expected to bring is here once again. Last year at Telegraph tech we predicted 2016 would witness the rise of mobile payments, the creation of smart cities that can think and function autonomously, and the premiere of virtual reality in people's living rooms. Trials have proven artificial intelligence to be effective in suggesting treatments by analysing patients' genomes This year we've expanded our horizons somewhat to include moonshot projects, the social ramifications of technology and one disaster scenario. Here are our predictions of the technology events to come in 2017. Self-driving vehicles have arrived more swiftly than anybody thought: Google and Apple have been experimenting with the technology for years, the Autopilot mode on Tesla cars has clocked up over 200 million miles, and every carmaker is scrambling to get self-driving software into their vehicles.
Volvo is set to run self-driving versions of its family 4x4s on roads around London next year as the motor industry's trial of autonomous vehicles accelerates. While self-driving pods and shuttles were already due to operate on pavements in Greenwich and Milton Keynes this summer, the Swedish carmaker is planning to test autonomous vehicles on public roads in the capital from 2017. Volvo's UK test, called Drive Me London, will go a step further than other programmes by using real families driving autonomous cars on public roads. The manufacturer has conducted tests with the same vehicles in Gothenburg since 2014, and plans a parallel public trial in the Swedish city next year. The cars will record data from everyday users to help develop driverless cars for real-world conditions.
The field of Multi-Agent System (MAS) is an active area of research within Artificial Intelligence, with an increasingly important impact in industrial and other real-world applications. Within a MAS, autonomous agents interact to pursue personal interests and/or to achieve common objectives. Distributed Constraint Optimization Problems (DCOPs) have emerged as one of the prominent agent architectures to govern the agents' autonomous behavior, where both algorithms and communication models are driven by the structure of the specific problem. During the last decade, several extensions to the DCOP model have enabled them to support MAS in complex, real-time, and uncertain environments. This survey aims at providing an overview of the DCOP model, giving a classification of its multiple extensions and addressing both resolution methods and applications that find a natural mapping within each class of DCOPs. The proposed classification suggests several future perspectives for DCOP extensions, and identifies challenges in the design of efficient resolution algorithms, possibly through the adaptation of strategies from different areas.
Is your government ready for the Internet of Things (IoT)? The news media has been full of stories of self-driving cars being tested around the world and drones being used in diverse places. But a quiet global technology revolution is now occurring that is transforming the way we live and work in almost every area of life. And while robots at Amazon and smart home devices seem to be getting regular media attention, much more is happening in cyberspace. We live in exciting times with vast technological possibilities merging our online and offline lives.
Certain kinds of autonomous vehicles may not be safe, especially in an emergency situation, according to a new study published by the Lords Science and Technology Committee on Wednesday. With driverless technology, drivers may become over-reliant and complacent. However, with the development in the automotive technology over time, accidents by human error may be significantly reduced -- but they just might increase before they do. The committee also reported people may use driverless cars for shorter distances, as well, causing laziness and may prevent them from "getting exercise by walking." The UK Economic Opportunity split vehicles into levels from 0 to 5. Zero was fully controlled by an individual, and five was completely automated.