Factions: acts of worldbuilding on social media platforms

The surge in social media as a primary source for communication—basic interpersonal relations, news, and entertainment—means that modern humans have a steep learning curve for interpreting and creating messages in digital spaces. In addition to the difficulties of communication between multi-lingual and multi-cultural online communities, there is now the complication of computer languages (or “code”) that often do not overlap between software programs, let alone with humans. Additionally, humans use definitions and labels as artificial intelligence (AI) training methods. AI bias comes from the human labels, categorizations, and linguistic perimeters embedded in the code. The objective of Factions, the thesis website, is to represent a speculative future showing what communication may look like if we follow on the current trajectory of interaction in social media spaces—with less agreement on basic linguistic, audio, and visual terms and definitions coupled with more insistence on personal perspective as paramount. From a base set on the oldest forms of social media—websites and blogs—Factions acts out conversations mining for answers to the questions: • How do words change in meaning and function in a digital environment focused on the faction pillars of social media communication—search engine optimization, algorithm, and template? • In what ways might human-computer interaction improve and conversely impair human language and performance choices in digital realms of communication? Through practice-based research using web-building tools as aids to literal digital worldbuilding, the thesis website is a prototype of a speculative future built with the conceptual applications of design fiction—creating a fictional world as a space to explore the impact of future technology. To that end, my digital twin (a digital model that drives material data) is an AI mystic called Wu—imagined AI tech so advanced it transcended into a higher spiritual realm. Wu narrates and curates Factions and uses it to build a network of narratives, bridging the creative and critical through hypertext links and tooltip popups and applies their mystical power to channel any person, place, thing, or time typically focused on key social media topics of justice, race, spirituality, politics, and pop culture. Factions uses satirical techniques alongside appropriation and pastiche to examine transformative tech and human-computer interaction. It mixes the creative and the critical to arrive at a digital storytelling and learning landscape of the future

Artificial Intelligence Policy: A Primer and Roadmap

Talk of artificial intelligence is everywhere. People marvel at the capacity of machines to translate any language and master any game. Others condemn the use of secret algorithms to sentence criminal defendants or recoil at the prospect of machines gunning for blue, pink, and white-collar jobs. Some worry aloud that artificial intelligence will be humankind’s “final invention.” This essay, prepared in connection with UC Davis Law Review\u27s 50th anniversary symposium, explains why AI is suddenly on everyone\u27s mind and provides a roadmap to the major policy questions AI raises. The essay is designed to help policymakers, investors, technologists, scholars, and students understand the contemporary policy environment around AI at least well enough to initiate their own exploration

Artificial general intelligence: Proceedings of the Second Conference on Artificial General Intelligence, AGI 2009, Arlington, Virginia, USA, March 6-9, 2009

Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI – to create broad human-like and transhuman intelligence, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called narrow AI – the production of AI systems displaying intelligence regarding specific, highly constrained tasks. In recent years, however, more and more researchers have recognized the necessity – and feasibility – of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of human level intelligence and more broadly artificial general intelligence

Artificial intelligence applied to marketing management: Trends and projections according to specialists

Marketing Management is one of the areas that has been progressively integrating artificial intelligence systems, and the pace of the development of intelligent software that is very useful for marketing seems not to slow down. In fact, the growth and sophistication of technological systems promise to increase even more, which will inevitably affect operations as well as management and planning. In an attempt to assess and measure the expected impacts of AI on marketing departments in the short / medium term, a Delphi was carried out. Thereby, a panel of 21 marketing specialists (13 Portuguese and 8 international) was gathered, which was asked to evaluate on a Likert scale a series of statements, and to comment and debate among them. In this case it was a Real Time Delphi since the study was conducted using an online platform, which allowed all comments to be immediately available and visible to all participants. With this exploratory study, it was possible to conclude that the areas that are expected to be helped by intelligent systems to a greater extent – this is, the areas that will assist the automation of more operations - are customer recognition , market segmentation, sales forecasting and programmatic communication. On the other hand, the two most controversial statements among experts - thus risky to draw lessons - were statements regarding the autonomous operation of website adjustments and developments, as well as the adoption of intelligent systems to support strategic and strategic decision-making.A Gestão de Marketing é uma das áreas que tem vindo progressivamente a integrar sistemas de inteligência artificial, e a cadência do desenvolvimento de softwares inteligentes com grande utilidade para parece não abrandam. Na verdade, o crescimento e o grau de sofisticação dos sistemas tecnológicos prometem aumentar cada vez mais, o que promete afetar a vários níveis as operações e até a definição de estratégias de marketing e de gestão. Na tentativa de avaliar e medir os impactos da inteligência artificial nos departamentos de marketing no curto/médio prazo, procedeu-se à realização de um Delphi. Para isso reuniu-se um painel de 21 especialistas na área do marketing e da inteligência artificial (13 portugueses e 8 internacionais), ao qual foi colocada uma série de afirmações para que fossem avaliadas numa escala de Likert, comentadas e debatidas. Neste caso tratou-se de um Real Time Delphi uma vez que o estudo foi realizado recorrendo a uma plataforma online, o que permitiu que todos comentários ficassem imediatamente disponíveis e visíveis a todos os participantes. Com este estudo, de cariz marcadamente exploratório, concluiu-se que as áreas que se esperam vir a ser auxiliadas por sistemas inteligentes em maior medida – ou seja, as áreas que assistirão à automatização de um maior número de operações – são o reconhecimento do cliente, segmentação de mercado, previsão de vendas e comunicação programática. Por outro lado, os temas que mais controvérsia geraram entre os especialistas – sendo pouco seguro retirar ilações – referem-se à operação autónoma de ajustes e desenvolvimentos de websites, bem como à adoção de sistemas inteligentes para servirem de apoio à tomada de decisões estratégicas e de planeamento

A Study of Accomodation of Prosodic and Temporal Features in Spoken Dialogues in View of Speech Technology Applications

Inter-speaker accommodation is a well-known property of human speech and human interaction in general. Broadly it refers to the behavioural patterns of two (or more) interactants and the effect of the (verbal and non-verbal) behaviour of each to that of the other(s). Implementation of thisbehavior in spoken dialogue systems is desirable as an improvement on the naturalness of humanmachine interaction. However, traditional qualitative descriptions of accommodation phenomena do not provide sufficient information for such an implementation. Therefore, a quantitativedescription of inter-speaker accommodation is required. This thesis proposes a methodology of monitoring accommodation during a human or humancomputer dialogue, which utilizes a moving average filter over sequential frames for each speaker. These frames are time-aligned across the speakers, hence the name Time Aligned Moving Average (TAMA). Analysis of spontaneous human dialogue recordings by means of the TAMA methodology reveals ubiquitous accommodation of prosodic features (pitch, intensity and speech rate) across interlocutors, and allows for statistical (time series) modeling of the behaviour, in a way which is meaningful for implementation in spoken dialogue system (SDS) environments.In addition, a novel dialogue representation is proposed that provides an additional point of view to that of TAMA in monitoring accommodation of temporal features (inter-speaker pause length and overlap frequency). This representation is a percentage turn distribution of individual speakercontributions in a dialogue frame which circumvents strict attribution of speaker-turns, by considering both interlocutors as synchronously active. Both TAMA and turn distribution metrics indicate that correlation of average pause length and overlap frequency between speakers can be attributed to accommodation (a debated issue), and point to possible improvements in SDS “turntaking” behaviour. Although the findings of the prosodic and temporal analyses can directly inform SDS implementations, further work is required in order to describe inter-speaker accommodation sufficiently, as well as to develop an adequate testing platform for evaluating the magnitude ofperceived improvement in human-machine interaction. Therefore, this thesis constitutes a first step towards a convincingly useful implementation of accommodation in spoken dialogue systems

Real-time generation and adaptation of social companion robot behaviors

Social robots will be part of our future homes. They will assist us in everyday tasks, entertain us, and provide helpful advice. However, the technology still faces challenges that must be overcome to equip the machine with social competencies and make it a socially intelligent and accepted housemate. An essential skill of every social robot is verbal and non-verbal communication. In contrast to voice assistants, smartphones, and smart home technology, which are already part of many people's lives today, social robots have an embodiment that raises expectations towards the machine. Their anthropomorphic or zoomorphic appearance suggests they can communicate naturally with speech, gestures, or facial expressions and understand corresponding human behaviors. In addition, robots also need to consider individual users' preferences: everybody is shaped by their culture, social norms, and life experiences, resulting in different expectations towards communication with a robot. However, robots do not have human intuition - they must be equipped with the corresponding algorithmic solutions to these problems. This thesis investigates the use of reinforcement learning to adapt the robot's verbal and non-verbal communication to the user's needs and preferences. Such non-functional adaptation of the robot's behaviors primarily aims to improve the user experience and the robot's perceived social intelligence. The literature has not yet provided a holistic view of the overall challenge: real-time adaptation requires control over the robot's multimodal behavior generation, an understanding of human feedback, and an algorithmic basis for machine learning. Thus, this thesis develops a conceptual framework for designing real-time non-functional social robot behavior adaptation with reinforcement learning. It provides a higher-level view from the system designer's perspective and guidance from the start to the end. It illustrates the process of modeling, simulating, and evaluating such adaptation processes. Specifically, it guides the integration of human feedback and social signals to equip the machine with social awareness. The conceptual framework is put into practice for several use cases, resulting in technical proofs of concept and research prototypes. They are evaluated in the lab and in in-situ studies. These approaches address typical activities in domestic environments, focussing on the robot's expression of personality, persona, politeness, and humor. Within this scope, the robot adapts its spoken utterances, prosody, and animations based on human explicit or implicit feedback.Soziale Roboter werden Teil unseres zukünftigen Zuhauses sein. Sie werden uns bei alltäglichen Aufgaben unterstützen, uns unterhalten und uns mit hilfreichen Ratschlägen versorgen. Noch gibt es allerdings technische Herausforderungen, die zunächst überwunden werden müssen, um die Maschine mit sozialen Kompetenzen auszustatten und zu einem sozial intelligenten und akzeptierten Mitbewohner zu machen. Eine wesentliche Fähigkeit eines jeden sozialen Roboters ist die verbale und nonverbale Kommunikation. Im Gegensatz zu Sprachassistenten, Smartphones und Smart-Home-Technologien, die bereits heute Teil des Lebens vieler Menschen sind, haben soziale Roboter eine Verkörperung, die Erwartungen an die Maschine weckt. Ihr anthropomorphes oder zoomorphes Aussehen legt nahe, dass sie in der Lage sind, auf natürliche Weise mit Sprache, Gestik oder Mimik zu kommunizieren, aber auch entsprechende menschliche Kommunikation zu verstehen. Darüber hinaus müssen Roboter auch die individuellen Vorlieben der Benutzer berücksichtigen. So ist jeder Mensch von seiner Kultur, sozialen Normen und eigenen Lebenserfahrungen geprägt, was zu unterschiedlichen Erwartungen an die Kommunikation mit einem Roboter führt. Roboter haben jedoch keine menschliche Intuition - sie müssen mit entsprechenden Algorithmen für diese Probleme ausgestattet werden. In dieser Arbeit wird der Einsatz von bestärkendem Lernen untersucht, um die verbale und nonverbale Kommunikation des Roboters an die Bedürfnisse und Vorlieben des Benutzers anzupassen. Eine solche nicht-funktionale Anpassung des Roboterverhaltens zielt in erster Linie darauf ab, das Benutzererlebnis und die wahrgenommene soziale Intelligenz des Roboters zu verbessern. Die Literatur bietet bisher keine ganzheitliche Sicht auf diese Herausforderung: Echtzeitanpassung erfordert die Kontrolle über die multimodale Verhaltenserzeugung des Roboters, ein Verständnis des menschlichen Feedbacks und eine algorithmische Basis für maschinelles Lernen. Daher wird in dieser Arbeit ein konzeptioneller Rahmen für die Gestaltung von nicht-funktionaler Anpassung der Kommunikation sozialer Roboter mit bestärkendem Lernen entwickelt. Er bietet eine übergeordnete Sichtweise aus der Perspektive des Systemdesigners und eine Anleitung vom Anfang bis zum Ende. Er veranschaulicht den Prozess der Modellierung, Simulation und Evaluierung solcher Anpassungsprozesse. Insbesondere wird auf die Integration von menschlichem Feedback und sozialen Signalen eingegangen, um die Maschine mit sozialem Bewusstsein auszustatten. Der konzeptionelle Rahmen wird für mehrere Anwendungsfälle in die Praxis umgesetzt, was zu technischen Konzeptnachweisen und Forschungsprototypen führt, die in Labor- und In-situ-Studien evaluiert werden. Diese Ansätze befassen sich mit typischen Aktivitäten in häuslichen Umgebungen, wobei der Schwerpunkt auf dem Ausdruck der Persönlichkeit, dem Persona, der Höflichkeit und dem Humor des Roboters liegt. In diesem Rahmen passt der Roboter seine Sprache, Prosodie, und Animationen auf Basis expliziten oder impliziten menschlichen Feedbacks an

Digital Analytics:Modeling for Insights and New Methods

Firms are increasingly turning towards new-age technologies such as artificial intelligence (AI), the internet of things (IoT), blockchain, and drones, among others, to assist in interacting with their customers. Further, with the prominence of personalization and customer engagement as the go-to customer management strategies, it is essential for firms to understand how to integrate the new-age technologies into their existing practices seamlessly to aid in the generation of actionable insights. Towards this end, this study proposes an organizing framework to understand how firms can use digital analytics, within the changing technology landscape, to generate consumer insights. The proposed framework begins by recognizing the forces that are external to the firm that then leads to the generation of specific capabilities by the firm. Further, the firm capabilities can lead to the generation of insights for decision making that can be data-driven and/or analytics-driven. Finally, the proposed framework identifies the creation of value-based outcomes for firms and customers, resulting from the insights generated. Additionally, we identify moderators that influence (a) the impact of external forces on the development of firm capabilities, and (b) the creation of insights and subsequent firm outcomes. This study also identifies questions for future research that combines the inclusion of new-age technologies, generation of strategic insights, and the achievement of established firm outcomes

Engineering derivatives from biological systems for advanced aerospace applications

The present study consisted of a literature survey, a survey of researchers, and a workshop on bionics. These tasks produced an extensive annotated bibliography of bionics research (282 citations), a directory of bionics researchers, and a workshop report on specific bionics research topics applicable to space technology. These deliverables are included as Appendix A, Appendix B, and Section 5.0, respectively. To provide organization to this highly interdisciplinary field and to serve as a guide for interested researchers, we have also prepared a taxonomy or classification of the various subelements of natural engineering systems. Finally, we have synthesized the results of the various components of this study into a discussion of the most promising opportunities for accelerated research, seeking solutions which apply engineering principles from natural systems to advanced aerospace problems. A discussion of opportunities within the areas of materials, structures, sensors, information processing, robotics, autonomous systems, life support systems, and aeronautics is given. Following the conclusions are six discipline summaries that highlight the potential benefits of research in these areas for NASA's space technology programs