Robots in the work place can perform hazardous or even 'impossible' tasks; e.g., toxic waste clean-up, desert and space exploration, and more. AI researchers are also interested in the intelligent processing involved in moving about and manipulating objects in the real world.
If you had your very own home robot, what would you want it to do, exactly? Yeah, me too, but that kind of robot is a long, long ways off. Consider Jibo, essentially a dancing Amazon Alexa. And Kuri, a miniaturized R2-D2 that roams around your house taking pictures. If that doesn't sound particularly impressive to you, well, the market felt the same way.
Bruce Newsome reviews the recently published book: "Strategy, Evolution, and War: From Apes to Artificial Intelligence," authored by Kenneth Payne and published by Georgetown University Press. Artificial intelligence (AI) has been explicit in the practices and policies of defence since at least the 1970s, at least in high-capacity countries, given the exponential growth in the power of electronic computing per unit cost. It was already specified in training and forecasting simulations, decision-making aids, targeting aids, robotics, adaptive navigation systems (as in the Tomahawk Cruise Missile), and ballistic missile defence. Any child with a video game could experience AI. AI raced up Western governmental priorities in the 2000s by application to countering terrorism; in 2009, the US escalated its cyber capabilities and authorities, partly on the promise of AI; in 2014, the Russians seemed to know first what the defenders of Ukraine were doing, in part because of integration of AI; and in 2016, Western governments consensually blamed Russia for unprecedented interference in American and other elections, partly aided by AI.
The benefits of autonomous vehicles (AVs) are widely acknowledged, but there are concerns about the extent of these benefits and AV risks and unintended consequences. In this article, we first examine AVs and different categories of the technological risks associated with them. We then explore strategies that can be adopted to address these risks, and explore emerging responses by governments for addressing AV risks. Our analyses reveal that, thus far, governments have in most instances avoided stringent measures in order to promote AV developments and the majority of responses are non-binding and focus on creating councils or working groups to better explore AV implications. The US has been active in introducing legislations to address issues related to privacy and cybersecurity. The UK and Germany, in particular, have enacted laws to address liability issues, other countries mostly acknowledge these issues, but have yet to implement specific strategies. To address privacy and cybersecurity risks strategies ranging from introduction or amendment of non-AV specific legislation to creating working groups have been adopted. Much less attention has been paid to issues such as environmental and employment risks, although a few governments have begun programmes to retrain workers who might be negatively affected.
Autonomous systems will play an essential role in many applications across diverse domains including space, marine, air, field, road, and service robotics. They will assist us in our daily routines and perform dangerous, dirty and dull tasks. However, enabling robotic systems to perform autonomously in complex, real-world scenarios over extended time periods (i.e. weeks, months, or years) poses many challenges. Some of these have been investigated by sub-disciplines of Artificial Intelligence (AI) including navigation & mapping, perception, knowledge representation & reasoning, planning, interaction, and learning. The different sub-disciplines have developed techniques that, when re-integrated within an autonomous system, can enable robots to operate effectively in complex, long-term scenarios. In this paper, we survey and discuss AI techniques as 'enablers' for long-term robot autonomy, current progress in integrating these techniques within long-running robotic systems, and the future challenges and opportunities for AI in long-term autonomy.
Technology continues to change society at a rapid pace, and accounting and auditing are by no means immune. New technologies are increasingly able to mimic human activity, taking on repetitive tasks more quickly and accurately than people can. The authors provide an overview of the ways in which robotic process automation may change how the profession operates, with a particular focus on the area of revenue audits. Auditing has historically incorporated many computer-dependent tools and processes, which were often interlinked by many manual steps and keystrokes. A new set of overlay software has emerged, however, that combines these disparate actions into a single smooth automated process.
This paper presents a multidisciplinary task approach for assessing the impact of artificial intelligence on the future of work. We provide definitions of a task from two main perspectives: socio-economic and computational. We propose to explore ways in which we can integrate or map these perspectives, and link them with the skills or capabilities required by them, for humans and AI systems. Finally, we argue that in order to understand the dynamics of tasks, we have to explore the relevance of autonomy and generality of AI systems for the automation or alteration of the workplace.
In the future, artificial intelligence may be the facilitator of decision-making, financial instruments and even conclusion of contracts. However, its actual power to impact these and other activities is difficult to predict. That's why various leading experts and organizations are pushing for the regulation of AI at many levels, all the way from consumer-grade to international markets. But it remains to be seen how AI use within companies can be regulated, what form this regulation will take and whether it can be truly effective. By contrast, Musk appears to fear that autonomous machines will take control of the world, having seemingly failed to grasp the definition of AI for the purposes of the debate over regulation.
In order to make educated decisions in this fast-moving field, all managers should have a basic understanding of AI. Here are four key facts that will give you an edge. AI systems learn from the data and feedback that they receive in response to their earlier decisions. Their predictions and actions are only as good as the data they have been trained on. This characteristic makes AI systems very different from traditional deduction- based programming.
Launching at Singapore port's Marina South Pier in quarter three 2018, Wilhelmsen Ships Service and Airbus will be piloting the delivery of spare parts, documents, water test kits and 3D printed consumables via Airbus' Skyways unmanned air system (UAS) to vessels at anchorage. With the signing of an MOU at maritime trade show Posidonia, the Maritime UAS project agreement covers a joint ambition to establish a framework for cooperation between the Parties, with the aim of investigating the potential deployment and commercialization of UAS for maritime deliveries use cases. Marking the very first time, the viability of autonomous drone delivery to vessels has been put to the test in hectic, real-world port conditions, Marius Johansen, VP Commercial, Ships Agency at Wilhelmsen Ships Service is confident with Airbus now onboard his agency team's long-term drone delivery aspirations will be fulfilled. "We are absolutely thrilled to be working with a forward thinking, industry leader like Airbus. When we announced last year that we were pursuing drone delivery, we were greeted with a fair amount of scepticism, but our collaboration with Airbus, shows we really do mean business".