augmented Intelligence

This chapter explores the role of artificial intelligence in epidemiology, focusing on its application in early outbreak detection and disease spread prediction. It examines how machine learning, natural language processing, and predictive analytics are applied to real-time data from clinical, environmental, and digital sources. The chapter demonstrates the integration of AI into public health surveillance systems and discusses the ethical, technical, and infrastructural challenges involved. It highlights the potential of AI to process large, heterogeneous datasets at speed and scale, enabling earlier intervention and resource allocation during emerging health crises. By analyzing historical trends and identifying hidden patterns, AI models offer critical support to decision-makers in managing uncertainty and enhancing response strategies. This chapter argues for an interdisciplinary and transparent approach to algorithm design, data governance, and stakeholder collaboration.

The COVID-19 epidemic commenced in December 2019 in Wuhan, China. In contrast to the Spanish flu pandemic of 1918, the death rate from COVID-19 is merely 5%. The condition is caused by the Severe Acute Respiratory Syndrome Coronavirus 2 strain. In reaction to this catastrophe, several nations have implemented quarantines to mitigate the worldwide dissemination of the COVID-19 virus. COVID-19 vaccines will not be accessible until December 2020. A dependable identification method is essential for the early detection of COVID-19 to preserve humanity. Recent studies indicate that Chest X-ray (CXR) imaging is the most expedient way for diagnosing and classifying COVID-19, and it is also more reliable than Reverse Transcription Polymerase Chain Reaction (RT-PCR) as it yields critical information regarding the coronavirus. Clinical practitioners may initiate treatment at the preliminary stage based on this CXR diagnosis. This article proposes methods for predicting the spread of the COVID-19 pandemic utilizing GLCM-CNN, a deep learning system.

This study investigates the transformative impact of artificial intelligence on business decision-making. It demonstrates how technologies such as machine learning, natural language processing, and big data analytics revolutionize operations by enhancing data analysis, forecasting market trends, and automating routine processes—ultimately boosting efficiency and tailoring customer interactions. At the same time, the paper discusses several challenges associated with AI integration, including issues of data security and privacy, inherent biases in data, high costs of implementation, and the risks of overdependence on automation. The findings offer strategic insights into how organizations can leverage AI to gain a competitive advantage while effectively addressing ethical and operational hurdles.

The integration of Artificial Intelligence (AI) into livestock management in Sub-Saharan Africa (SSA) offers a promising solution for improving food security amid climate change challenges. AI technologies have the potential to optimize agricultural

Language awareness in TESOL encompasses an educator's understanding of the historical evolution of English, phonological principles, and accent variation. Part 1 explores how English has been shaped by socio-political forces and linguistic influences, providing educators with a deeper insight into its dynamic development. By addressing place and manner of articulation, rhythm, stress, intonation, and regional English varieties, TESOL practitioners can enhance students' pronunciation skills and cultural awareness. Additionally, recognising English's linguistic commonalities with other Germanic and Romance languages facilitates vocabulary acquisition, helping learners draw connections between English and their native languages.

Artificial intelligence (AI) appears to have altered the entire process of processing and disseminating the information to larger audiences. The present study is intended to investigate the similitude, variance, and inferences of AI-driven communication in relation to human communication. This study explores AI-driven vs. human communication through a literature review, emphasizing human communication's unique ability to foster connections, empathy, trust, and authenticity. AI offers speed but lacks depth. Ethical concerns include privacy and misinformation. It highlights the importance of an ethical approach when using AI in communication.

Artificial Intelligence (AI) is reshaping the landscape of organizational behaviour,
driving unprecedented transformation in decision-making, human resource management, employee training, and workforce planning. This essay delves into the
multifaceted applications of AI within organizations, highlighting its pivotal role in
enhancing decision-making capabilities through rapid data analysis and predictive
modelling. In human resources, AI revolutionizes recruitment, employee engagement,
and performance management, exemplified by global leaders like Unilever and IBM.
The integration of AI-driven personalized learning platforms ensures continuous skill
development, fostering a motivated and adept workforce.

sketch, collage

excerpt:
This is the system which makes the object. [0] Among philosophers such as Deleuze or Derrida, it was stupidity, rather than intelligence, that ultimately acquired the status of “the object of a properly transcendental question.” Stupidity is the deconstructive ferment that inhabits the heart of intelligence. [9] The object is to be entered at one's own peril, it seeks to trap one's self in itself, and gardens selves and plucks them all in all its fiery temper. [0] The image that Descartes and tradition have left us of their order is that of a chain, which concretises for the imagination the unity of progression and the connection of reasons [5] Enchanted by its rigor, humanity has forgotten, [6] the perfect completion… is reached when Primeval Unity is finally restored, putting an end to an ever-increasing process towards Multiplicity. [3] The end of the maze holds death. [1]

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"I’m gone to keep trying to get smart so I can have that feeling agen. Its good to no things and be smart and I wish I new evrything in the hole world. I wish I coud be smart agen rite now. If I coud I woud sit down and reed all the time." [Flowers for Algernon, Daniel Keyes]

The modern workplace is undergoing a profound transformation, shifting from a focus on human replacement to a collaborative synergy between humans and AI. This article explores how AI, acting as a conductor, orchestrates a symphony of innovation, where human creativity and machine precision elevate each other. It examines AI's role as a creative partner, amplifying human ingenuity and driving performance levels beyond what either could achieve alone. The article also discusses the evolving skills required for an AI-enhanced environment, emphasizing critical thinking, problem-solving, and collaboration. It underscores the importance of ethical considerations in AI development and deployment, ensuring fairness, transparency, and inclusivity. Finally, it examines the evolving role of leadership in an AI-powered workplace, emphasizing the need for fostering a culture of collaboration, adaptability, and continuous learning. The article concludes by advocating for a future where humans and AI work together harmoniously, creating a dynamic and innovative workplace where progress is driven by a symphony of human and machine capabilities.

This paper aims to evaluate recent neurotechnological innovations and Algorithmic Culture from the philosophical and socio-cultural perspectives of technology. As innovations in neurotechnology and generative artificial intelligence begin to shape institutions and working conditions through new paradigms such as algorithmic governmentality, theoretical arguments are needed to characterize these chaotic situations adequately. Existing descriptions of society and culture, such as risk society, postmodern society, burnout society, disciplinary society, palliative society, or digitalized society, no longer capture all aspects of the current situation. Inevitably, new arguments for designing and managing this new world system have brought unprecedented crises, such as new forms of data surveillance and the invasion of the human mind as the subject. This paper focuses on the challenges to the authenticity of the human mind and memory posed by new neurotechnological developments such as neurochips. The significance of this work, therefore, lies in its hybrid theoretical approach that places both society and the subject at the center of debates, with distinctive concepts such as Neurolink and interrelated terminologies such as Algorithmic Governmentality, Algorithmic Society, Algorithmic Culture, and Augmented Intelligence. The study's methodology examines these terminologies alongside the interrelated arguments of Heidegger, Bertrand Gilles, Rouvroy, and Kasparov.

Neural rendering (NR) has emerged as a novel technology for the generation and animation of realistic digital human faces. NR is based on machine learning techniques such as generative adversarial networks and is used to infer human face features and their animation from large amounts of (video) training data. NR shot to prominence with the deep fake phenomenon, the malicious and unwanted use of someone's face for deception or satire. In this paper we demonstrate that the potential uses of NR far outstrip its use for deep fakes. We contrast NR approaches with traditional computer graphics approaches, discuss typical types of NR applications in digital face generation, and derive a conceptual framework for both guiding the design of digital characters, and for classifying existing NR use cases. We conclude with research ideas for studying the potential applications and implications of NR-based digital characters.

Augmented intelligence is a paradigm that integrates machine capabilities with human cognition, amplifying cognitive abilities and optimizing decision-making processes. Integrating augmented intelligence into knowledge management can significantly improve experiential learning and training at lower costs while maintaining higher standards. Before the epidemic, AI-based transformation in knowledge acquisition, dissemination, and responsiveness began, which expanded rapidly during the global COVID-19 pandemic. Consequently, virtual knowledge management, online learning, hybrid training, and similar approaches have become familiar, while quality and real-time human participation have declined due to monotonous static features. Hence, this chapter discusses how to redesign knowledge management and green education systems with augmented intelligence/reality to enhance higher standards in virtual learning and training through advanced hybrid platforms such as holographic labs, 3D holographic training systems, bionic lenses, and electroencephalography for future generations.

In the upcoming future of work, due to the digital transformation of organisations, humans must learn to work alongside artificial intelligent systems and thus the working environment and society will evolve towards augmented intelligence and hybrid teams. This research paper will present both the benefit of working with artificially intelligent systems but also the potential risks that these systems represent for the employees. In order to mitigate these risks education will become the main entry point to a digitally transformed future of work. It will be highly required for humans to be engaged in continuous active learning after the end of their education years. For the digital transformed future both organizations and individuals will have to engage in a lifelong learning process. Even so, in the future there will still be humans and organizations that will produce goods, content, and art in the old ways without the help of artificial intelligence or advanced technologies, and these will be "human-made" products and services.

In the upcoming future of work, due to the digital transformation of organisations, humans must learn to work alongside artificial intelligent systems and thus the working environment and society will evolve towards augmented intelligence and hybrid teams. This research paper will present both the benefit of working with artificially intelligent systems but also the potential risks that these systems represent for the employees. In order to mitigate these risks education will become the main entry point to a digitally transformed future of work. It will be highly required for humans to be engaged in continuous active learning after the end of their education years. For the digital transformed future both organizations and individuals will have to engage in a lifelong learning process. Even so, in the future there will still be humans and organizations that will produce goods, content, and art in the old ways without the help of artificial intelligence or advanced technologies, and these will be "human-made" products and services.

The UAE Government has recently launched a strategy for Artificial Intelligence (AI) that transitions the government to a new phase of becoming an AI-driven government. This strategy aimed to adopt AI-based technologies to boost the overall performance of the government. AI- based technologies have the capability to perform a wide range of human cognitive functions such as voice recognition, image recognition, and prediction. However, to achieve the vision of becoming an AI-driven government, the UAE has to prepare effectively for the transformation by anticipating the various challenges that accompany the adoption of AI-based technologies. Previous research indicated that infusing AI-based technologies will stimulate major shifts in organizations such as reshaping the nature of work, changing how work was previously done, and, more importantly, redefining the measurements of effective leadership. Hence, to particularly address the major shifts triggered by the adoption of AI-based ...

The paper discusses the merits of airborne and ground-based concepts for implementing TBO and their impact on capacity, safety, efficiency, and sustainability.

Progress made in 40 years is illustrated based on the comparison of two samples of traffic inbound to London-Heathrow. Airborne and ground-based automation concepts for facilitating TBO are exposed to the challenges observed. The advantages of Augmented Intelligence (AI) in ground-based, Advanced Interval Management (AIM) become evident.

This paper presents a novel methodology for automatically extracting pragmatic markers from large streams of texts and repositories of documents. Pragmatic markers typically are implications, innuendos, suggestions, contradictions, sarcasms or references that are difficult to define objectively, but that are subjectively evident. Our methodology uses a two-stage augmented learning model applied to a specific use case, extracting from a repository of over 1500 Article IV country reports prepared for government officials by International Monetary Fund (IMF) staff. The model uses principles of evidence theory to train a machine to decipher the textual patterns of suggested actions for government officials and to extract those suggestions from the country reports at scale. We demonstrate the effectiveness of the model with impressive precision and recall metrics that over time outperform even the human trainers..

Le domaine journalistique est fortement influencé par les innovations techniques et technologiques. L’émergence des GAFAM force les médias à revoir leur modèle d’affaires et à innover pour survivre. Les outils liés à l’intelligence artificielle gagnent en popularité dans les salles de nouvelles pour aider les journalistes. Cette recherche aborde les usages faits de la technologie dans les salles de rédaction au Canada. Journalism is strongly influenced by the technological and technical innovations. The increase of GAFAM’s use and popularity force media outlets to review their business model and find ways to survive. The uses of tools related to artificial intelligence are increasing in media outlets around the globe. This paper presents the technology’s usage in Canada’s top newsrooms.

In this paper I describe a novel application of contemplative techniques to software engineering with the goal of augmenting the intellectual capabilities of knowledge workers within the field in four areas: flexibility, attention, creativity, and trust. The augmentation of software engineers' intellectual capabilities is proposed as a third complement to the traditional focus of methodologies on the process and environmental factors of the software development endeavor. I argue that these capabilities have been shown to be open to improvement through the practices traditionally used in spiritual traditions, but now used increasingly in other fields of knowledge work, such as in the medical profession and the education field. Historically, the intellectual capabilities of software engineers have been treated as a given within any particular software development effort. This is argued to be an aspect ripe for inclusion within software development methodologies.

Unresolved challenges of "dialogue"
Intelligence amplification, Augmented intelligence and Artificial intelligence (AI)
Meeting-relevant resources for AI applications
Participation in an AI-enhanced virtual meeting
Mapping the pattern and flow of dialogue
AI-enhanced approach to innovative meeting organization
Psychosocial anticipation of future interaction with AI in online gaming
Enabling comprehension of "the great game"
AI as an intelligent artifice -- but what is it really for?
AI Dialogue: beyond the Turing Test to the Buber Test?

The term 'Medical Technology' is commonly used to refer to a variety of tools, machines, and processes that allow healthcare professionals to deliver health services that improve quality of life by early diagnoses, reduced complications, systematic treatments and/or reducing patient hospitalizations and invasive procedures. Classical medical devices (prosthetics, stents, and implants) to the revolutionary invention and use of CT Scan and MRI in the 1970s, presented manageable ethical and implementational challenges. However, the rise of digital health technologies (Electronic Health Records, wearable sensors, communication sensors, etc.) and social media has resulted in a barrage of qualitative and quantitative 'big data' on an individual. Moreover, we now have the resources and ability to exploit 'big data' with the help of Artificial Intelligence (AI) and Deep Machine Learning which touts the promise of being transformative to the healthcare sector. Artificial Intelligence and its recognized potential pose a tough challenge for all stakeholders of the healthcare industry, specifically policymakers. To keep abreast of the rapidly changing landscape of medicine it is necessary to develop the infrastructure that inspects, tests and implements new digital health technologies before introducing them into the healthcare system. Artificial Intelligence has had the most impact on the healthcare policy in recent times. There are numerous stakeholders and policymakers involved in the regulation of a technology whose inherent nature is to learn, adapt and change with its use. Below we consider a few of the many areas of healthcare impacted by the presence and integration of AI and how the medical regulatory authorities may adapt to and adopt innovative methods and policies to face a unique cohort of ethical, organizational and implementational challenges. The term Artificial Intelligence is used to describe systems and processes performed by a constellation of technologies to mimic the cognitive functions of humans such as learning,

This paper presents a novel methodology for automatically extracting pragmatic markers from large streams of texts and repositories of documents. Pragmatic markers typically are implications, innuendos, suggestions, contradictions, sarcasms or references that are difficult to define objectively, but that are subjectively evident. Our methodology uses a two-stage augmented learning model applied to a specific use case, extracting from a repository of over 1500 Article IV country reports prepared for government officials by International Monetary Fund (IMF) staff. The model uses principles of evidence theory to train a machine to decipher the textual patterns of suggested actions for government officials and to extract those suggestions from the country reports at scale. We demonstrate the effectiveness of the model with impressive precision and recall metrics that over time outperform even the human trainers..

Current research in robotics is driven by the goal to achieve a high user acceptance of service robots for private households. This implies that robots have to increase their social aptness in order to become a robot companion that is able to interact in a natural way and to carry out tasks for the user.

Dialog modeling in robotics suffers from lack of generalizability, due to the fact that the dialog is heavily influenced by the tasks the robot is able to perform. We introduce interleaving interaction patterns together with a general protocol for task communication ...

In the recent past, service robots that are able to interact with humans in a natural way have become increasingly popular. A special kind of service robots that are designed for personal use at home are the so-called robot companions. They are expected to communicate with nonexpert users in natural and intuitive way. For such natural interactions with humans the robot has to detect communication partners and focus its attention on them. Moreover, the companion has to be able to understand speech and gestures of a user and to carry out dialogs in order to get instructed, i.e., introduced to its environment. We address these problems by presenting the current state of our mobile robot BIRON, the Bielefeld Robot Companion.

Grounding is essential in human interaction and crucial for social robots collaborating with humans. Gaze plays versatile roles for establishing, maintaining and repairing the common ground. It is combined with parallel modalities and involved in several processes for behavior generation and recognition. We present a uniform modeling approach focusing on the multi-modal, parallel and bidirectional aspects of gaze and their interleaving with the dialog logic.

We formalize and extend the contribution model of Clark and Schaefer (1987, 1989) so that it can be represented computationally; we then present a method for combining the turns of two individual agents into one incrementally determined, coherent representation of the processes of dialog. This representation is intended to approximate what a participant might represent about the dialog so far,

Dialog systems for mobile robots operating in the real world should enable mixedinitiative dialog style, handle multi-modal information involved in the communication and be relatively independent of the domain knowledge. Most dialog systems developed for mobile robots today, however, are often system-oriented and have limited capabilities. We present an agentbased dialog model that are specially designed for human-robot interaction and provide evidence for its efficiency with our implemented system.

An ambitious goal in modern robotic science is to build mobile robots that are able to interact as companions in real world environments. Especially for caretaking of elderly people a system robustly working at private homes is essential, requiring a very natural and human oriented way of communication. Since home environments are usually very individual a first task for a newly acquired robot is to get familiar with its new environment. This paper gives a short overview on how we integrated a vision based localization using the advantages of a very modular architecture and extending a spoken dialog system for online labeling and interaction about different locations. We present results from the integrated system working in a real, fully furnished home environment where it was able to learn the names of different rooms. This system enables us to perform real user studies in future without the need to fall back to Wizard-of-Oz experiments. Ongoing work aims at enabling the robot to take initiative by asking for unknown locations. A future extension is the ability to generalize over features of known rooms to make predictions when encountering unknown rooms

In research on human-robot interaction the interest is currently shifting from uni-modal dialog systems to multi-modal interaction schemes. We present a system for human-style interaction with a robot that is integrated on our mobile robot BIRON. To model the dialog we adopt an extended grounding concept with a mechanism to handle multi-modal in-and output where object references are resolved by the interaction with an object attention system (OAS). The OAS integrates multiple input from, e.g., the object and gesture recognition systems and provides the information for a common representation. This representation can be accessed by both modules and combines symbolic verbal attributes with sensor-based features. We argue that such a representation is necessary to achieve a robust and efficient information processing.

Dialog systems for mobile robots operating in the real world should enable mixedinitiative dialog style, handle multi-modal information involved in the communication and be relatively independent of the domain knowledge. Most dialog systems developed for mobile robots today, however, are often system-oriented and have limited capabilities. We present an agentbased dialog model that are specially designed for human-robot interaction and provide evidence for its efficiency with our implemented system.

Natural language user interfaces for cognitive robots should attempt to reduce the occurence of miscommunication in order to be perceived as providing a smooth and intuitive interaction to its users. This paper will describe how we integrate miscommunication analysis in the design process. By analysing data from 12 sessions, where subjects interacted with a service robot in a home like environment, we arrived at a set of observations, e.g., that users misunderstand the robot's functionality; and that feedback sometimes is ill-timed with respect to the situation; we also observed that referencing objects is important with respect to lexical choice and deixis. The design implications from our analysis are that we need to equip our robots to provide more and relevant feedback with respect to the system's functionality. Another design implication is to explore strategies that prime the user to respond in a way that can be handled by the robot system.

Dialog systems for mobile robots operating in the real world should enable mixedinitiative dialog style, handle multi-modal information involved in the communication and be relatively independent of the domain knowledge. Most dialog systems developed for mobile robots today, however, are often system-oriented and have limited capabilities. We present an agentbased dialog model that are specially designed for human-robot interaction and provide evidence for its efficiency with our implemented system.