A new report highlights an artificial intelligence (AI) knowledge gap in public relations. The CIPR is helping its members plug the gap. It's too early to identify best practice for AI communication. Practitioners need to step up and take personal responsibility for learning and development. This includes exploring the impact of AI on society and the communication workflow.
Advances on multiple fronts are bringing big improvements to the way computers learn, increasing the accuracy of speech and vision systems. Sign in using your ACM Web Account username and password to access premium content if you are an ACM member, Communications subscriber or Digital Library subscriber. Non-members can purchase this article or a copy of the magazine in which it appears.
Many tasks in AI require the collaboration of multiple agents. Typically, the communication protocol between agents is manually specified and not altered during training. In this paper we explore a simple neural model, called CommNet, that uses continuous communication for fully cooperative tasks. The model consists of multiple agents and the communication between them is learned alongside their policy. We apply this model to a diverse set of tasks, demonstrating the ability of the agents to learn to communicate amongst themselves, yielding improved performance over non-communicative agents and baselines. In some cases, it is possible to interpret the language devised by the agents, revealing simple but effective strategies for solving the task at hand.
Today, the amount of data that can be retrieved from communications networks is extremely high and diverse (e.g., data regarding users behavior, traffic traces, network alarms, signal quality indicators, etc.). Advanced mathematical tools are required to extract useful information from this large set of network data. In particular, Machine Learning (ML) is regarded as a promising methodological area to perform network-data analysis and enable, e.g., automatized network self-configuration and fault management. In this survey we classify and describe relevant studies dealing with the applications of ML to optical communications and networking. Optical networks and system are facing an unprecedented growth in terms of complexity due to the introduction of a huge number of adjustable parameters (such as routing configurations, modulation format, symbol rate, coding schemes, etc.), mainly due to the adoption of, among the others, coherent transmission/reception technology, advanced digital signal processing and to the presence of nonlinear effects in optical fiber systems. Although a good number of research papers have appeared in the last years, the application of ML to optical networks is still in its early stage. In this survey we provide an introductory reference for researchers and practitioners interested in this field. To stimulate further work in this area, we conclude the paper proposing new possible research directions.
Weakened and distrusted central governments around the world have been incapable of responding to the way the internet and social media have empowered populist but previously fringe groups, a unique world-wide survey of government communication chiefs has found. The survey spanning 40 countries is the first international review to reveal how deeply governments feel they are losing control and authority over communications. It shows they have been collectively shaken by public distrust of governments, and are struggling to keep pace with how modern voters gather information and form their opinions. The advent of fake news, the dissemination of knowingly inaccurate news, has only deepened the crisis. The study synthesising the responses of over 300 communication chiefs was undertaken by the advertising and strategy group WPP and was steered by a global advisory board spanning Australia, Europe, Asia and America, including leading academics and practitioners in communications, business and the public sector.