Towards a classification framework for social machines

The state of the art in human interaction with computational systems blurs the line between computations performed by machine logic and algorithms, and those that result from input by humans, arising from their own psychological processes and life experience. Current socio-technical systems, known as ‘social machines’ exploit the large-scale interaction of humans with machines. Interactions that are motivated by numerous goals and purposes including financial gain, charitable aid, and simply for fun. In this paper we explore the landscape of social machines, both past and present, with the aim of defining an initial classificatory framework. Through a number of knowledge elicitation and refinement exercises we have identified the polyarchical relationship between infrastructure, social machines, and large-scale social initiatives. Our initial framework describes classification constructs in the areas of contributions, participants, and motivation. We present an initial characterization of some of the most popular social machines, as demonstration of the use of the identified constructs. We believe that it is important to undertake an analysis of the behaviour and phenomenology of social machines, and of their growth and evolution over time. Our future work will seek to elicit additional opinions, classifications and validation from a wider audience, to produce a comprehensive framework for the description, analysis and comparison of social machines

Humans, robots and values

The issue of machines replacing humans dates back to the dawn of industrialisation. In this paper we examine what is fundamental in the distinction between human and robotic work by reflecting on the work of the classical political economists and engineers. We examine the relationship between the ideas of machine work and human work on the part of Marx and Watt as well as their role in the creation of economic value. We examine the extent to which artificial power sources could feasibly substitute for human effort in their arguments. We go on to examine the differing views of Smith and Marx with respect to the economic effort contributed by animals and consider whether the philosophical distinction made between human and non-human work can be sustained in the light of modern biological research. We emphasise the non-universal character of animal work before going on to discuss the ideas of universal machines in Capek and Turing giving as a counter example a cloth-folding robot being developed in our School. We then return to Watt and discuss the development of thermodynamics and information theory. We show how recent research has led to a unification not only of these fields but also a unitary understanding of the labour process and the value-creation process. We look at the implications of general robotisation for profitability and the future of capitalism. For this we draw on the work of von Neumann not only on computers but also in economics to point to the {\em real} threat posed by robots

Special issue on smart interactions in cyber-physical systems: Humans, agents, robots, machines, and sensors

In recent years, there has been increasing interaction between humans and non‐human systems as we move further beyond the industrial age, the information age, and as we move into the fourth‐generation society. The ability to distinguish between human and non‐human capabilities has become more difficult to discern. Given this, it is common that cyber‐physical systems (CPSs) are rapidly integrated with human functionality, and humans have become increasingly dependent on CPSs to perform their daily routines.The constant indicators of a future where human and non‐human CPSs relationships consistently interact and where they allow each other to navigate through a set of non‐trivial goals is an interesting and rich area of research, discovery, and practical work area. The evidence of con- vergence has rapidly gained clarity, demonstrating that we can use complex combinations of sensors, artificial intelli- gence, and data to augment human life and knowledge. To expand the knowledge in this area, we should explain how to model, design, validate, implement, and experiment with these complex systems of interaction, communication, and networking, which will be developed and explored in this special issue. This special issue will include ideas of the future that are relevant for understanding, discerning, and developing the relationship between humans and non‐ human CPSs as well as the practical nature of systems that facilitate the integration between humans, agents, robots, machines, and sensors (HARMS).Fil: Kim, Donghan. Kyung Hee University;Fil: Rodriguez, Sebastian Alberto. Universidad Tecnológica Nacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; ArgentinaFil: Matson, Eric T.. Purdue University; Estados UnidosFil: Kim, Gerard Jounghyun. Korea University

Analisis Perancangan Prototype Internet of Things (Iot) Pada STMIK Neumann

The use of computers in the future can dominate human work and defeat human computing such as controlling electronic equipment remotely using internet media, Internet of Things (IoT) allows users to manage and optimize electronics and electrical equipment using the internet. This makes these machines work alone and humans can enjoy the work of these machines without having to bother managing them. The workings of the Internet of Things (IoT) are quite easy. Every object must have an IP Address. After an object has an IP address and is connected to the internet, the sensor is also installed. Currently at STMIK NEUMANN has complete facilities for each classroom and lab room where air conditioners, lamps and other electronic devices are still manually controlled so that staff sometimes forget to turn off air conditioners, lights and other electronic devices. Therefore, by utilizing the internet network at STMIK NEUMANN the author tries to make a simulation of the Internet of Things (IoT) to make it easier for staff to control electronic devices in STMIK NEUMANN

Human-Machine Teamwork: An Exploration of Multi-Agent Systems, Team Cognition, and Collective Intelligence

One of the major ways through which humans overcome complex challenges is teamwork. When humans share knowledge and information, and cooperate and coordinate towards shared goals, they overcome their individual limitations and achieve better solutions to difficult problems. The rise of artificial intelligence provides a unique opportunity to study teamwork between humans and machines, and potentially discover insights about cognition and collaboration that can set the foundation for a world where humans work with, as opposed to against, artificial intelligence to solve problems that neither human or artificial intelligence can solve on its own. To better understand human-machine teamwork, it’s important to understand human-human teamwork (humans working together) and multi-agent systems (how artificial intelligence interacts as an agent that’s part of a group) to identify the characteristics that make humans and machines good teammates. This perspective lets us approach human-machine teamwork from the perspective of the human as well as the perspective of the machine. Thus, to reach a more accurate understanding of how humans and machines can work together, we examine human-machine teamwork through a series of studies. In this dissertation, we conducted 4 studies and developed 2 theoretical models: First, we focused on human-machine cooperation. We paired human participants with reinforcement learning agents to play two game theory scenarios where individual interests and collective interests are in conflict to easily detect cooperation. We show that different reinforcement models exhibit different levels of cooperation, and that humans are more likely to cooperate if they believe they are playing with another human as opposed to a machine. Second, we focused on human-machine coordination. We once again paired humans with machines to create a human-machine team to make them play a game theory scenario that emphasizes convergence towards a mutually beneficial outcome. We also analyzed survey responses from the participants to highlight how many of the principles of human-human teamwork can still occur in human-machine teams even though communication is not possible. Third, we reviewed the collective intelligence literature and the prediction markets literature to develop a model for a prediction market that enables humans and machines to work together to improve predictions. The model supports artificial intelligence operating as a peer in the prediction market as well as a complementary aggregator. Fourth, we reviewed the team cognition and collective intelligence literature to develop a model for teamwork that integrates team cognition, collective intelligence, and artificial intelligence. The model provides a new foundation to think about teamwork beyond the forecasting domain. Next, we used a simulation of emergency response management to test the different teamwork aspects of a variety of human-machine teams compared to human-human and machine-machine teams. Lastly, we ran another study that used a prediction market to examine the impact that having AI operate as a participant rather than an aggregator has on the predictive capacity of the prediction market. Our research will help identify which principles of human teamwork are applicable to human-machine teamwork, the role artificial intelligence can play in enhancing collective intelligence, and the effectiveness of human-machine teamwork compared to single artificial intelligence. In the process, we expect to produce a substantial amount of empirical results that can lay the groundwork for future research of human-machine teamwork

The human role in space. Volume 3: Generalizations on human roles in space

The human role in space was studied. The role and the degree of direct involvement of humans that will be required in future space missions, was investigated. Valid criteria for allocating functional activities between humans and machines were established. The technology requirements, ecnomics, and benefits of the human presence in space were examined. Factors which affect crew productivity include: internal architecture; crew support; crew activities; LVA systems; IVA/EVA interfaces; and remote systems management. The accomplished work is reported and the data and analyses from which the study results are derived are included. The results provide information and guidelines to enable NASA program managers and decision makers to establish, early in the design process, the most cost effective design approach for future space programs, through the optimal application of unique human skills and capabilities in space

The Semantic Web … Sounds Logical!

The Semantic Web will be an enabling technology for the future because as all of life\u27s components continue to progress and evolve, the demand on us as humans will continue to increase. Work will expect more productivity; family will demand more quality time, and even leisure activities will be technologically advanced. With these variables in mind, I believe humans will demand technologies that help to simplify this treacherous lifestyle. As patterns already indicate, one of the driving forces of technological development is efficiency. Developers are consistently looking for ways to make life\u27s demands less strenuous and more streamlined. The benefits of the semantic web are two-fold. Conceptually, it will enable us to be productive at home while at work, and productive at work while at home. The Semantic Web will be a technology that truly changes our lifestyle. The Web has yet to harness its full potential. We have yet to realize that in addition to computers, other machines can actually participate in the decision-making process via the Internet. This will allow virtually all devices the opportunity to be a helpful resource for humans via the Web. It must be taken into consideration that the Semantic Web will not be separate from the World Wide Web, but an extension of it. It will allow information to be given a well-defined meaning, which will allow computers and people to work in cooperation. With this technology, humans will be able to establish connections to machines that are not currently connected to the World Wide Web. For the Semantic Web to function, computers must have access to structured collections of information and sets of inference rules that they can use to conduct automated reasoning (Scientific American: Feature Article: The Semantic Web, 3). Using rules to make inferences, choosing a course of action, and answering questions will add functional logic to the Web. Currently the Semantic Web community is developing this new Web by using Extensible Markup Language (XML) and Resource Description Framework (RDF) and ultimately, Ontologies

Disentangling Abstraction from Statistical Pattern Matching in Human and Machine Learning

The ability to acquire abstract knowledge is a hallmark of human intelligence and is believed by many to be one of the core differences between humans and neural network models. Agents can be endowed with an inductive bias towards abstraction through meta-learning, where they are trained on a distribution of tasks that share some abstract structure that can be learned and applied. However, because neural networks are hard to interpret, it can be difficult to tell whether agents have learned the underlying abstraction, or alternatively statistical patterns that are characteristic of that abstraction. In this work, we compare the performance of humans and agents in a meta-reinforcement learning paradigm in which tasks are generated from abstract rules. We define a novel methodology for building "task metamers" that closely match the statistics of the abstract tasks but use a different underlying generative process, and evaluate performance on both abstract and metamer tasks. In our first set of experiments, we found that humans perform better at abstract tasks than metamer tasks whereas a widely-used meta-reinforcement learning agent performs worse on the abstract tasks than the matched metamers. In a second set of experiments, we base the tasks on abstractions derived directly from empirically identified human priors. We utilize the same procedure to generate corresponding metamer tasks, and see the same double dissociation between humans and agents. This work provides a foundation for characterizing differences between humans and machine learning that can be used in future work towards developing machines with human-like behavior