Artificial intelligence (AI) shows promise to support the prevention, diagnosis, and treatment of diseases in medicine and therapy. However, ethics is a priority concern in the development and implementation of AI across sectors. Common ethical themes in the healthcare literature of the last 5 years surround algorithmic bias, accountability, privacy, transparency and trust issues. The question arises how these challenges apply to speech and language therapy (SLT). Stakeholder attitudes towards the use of AI in healthcare have been investigated for the populations of physicians, medical students, and patients. However, no study has yet addressed the specific perspective of speech and language therapists on this technology. Therefore, the aim was to gather insights on the attitudes, hopes and concerns towards the (future) use of artificial intelligence from speech and language therapists working in clinical practice or research. An online survey with 11 closed and three open-ended questions was conducted in four German speaking countries. The quantitative analysis of the results involved correlating demographic factors, such as age, with the responses. The qualitative analysis compared the responses to this survey with the findings of healthcare literature and studies addressing other healthcare stakeholders. Five hundred eighty-seven professionals from Germany, Switzerland, Austria and Liechtenstein answered the questionnaire. In the results all but 3% of the participants expect that AI will be applied at least to some extend in SLT in the future. The majority of the participants (65%) are open-minded towards the application of AI in SLT. Perceived potential benefits show a larger overlap than identified challenges with the existing literature. The possible loss of the 'human-factor' in assessment and therapy is by far the most frequent concern (41%) the participating speech and language therapists have towards the use of AI. Results further reflect the current level of knowledge about this technology in our profession. The use of AI in SLT can have a positive impact, but many factors need to be considered to prepare our profession for this type of technology. These include the expansion of education, the development of guidelines and the establishment of interdisciplinary collaborations all aiming to develop, implement and enable the use of truly beneficial AI-tools for assessment and intervention in SLT. What is already known on this subject Stakeholder involvement is important in the development and implication of artificial intelligence in health care. Stakeholder attitudes towards the use of AI in healthcare have been investigated for the populations of physicians, medical students and patients. However, no study has yet addressed the specific perspective of speech and language therapists on this technology. What this paper adds to existing knowledge The majority of the participants are open-minded towards the application of AI in SLT and think that it will be used in our profession in the future. Perceived potential benefits and challenges align with the literature to some degree. One aspect that is especially emphasised by the participants is the potential loss of 'human-factor' in SLT. Results reflect participants' knowledge on AI as well as a specific therapeutic view on healthcare and intervention. What are the potential or actual clinical implications of this work? AI application in SLT has great potential, but also comes with challenges. Speech and language therapists need to expand their knowledge on this technology, prepare specific guidelines and engage in interdisciplinary collaborations to specify their perspective and needs in developing and implementing AI-software. Only then will it become truly useful for clinicians and they will be able to use it in a responsible and informed way.

Games have been the perfect test-beds for artificial intelligence research for the characteristics that widely exist in real-world scenarios. Learning and optimisation, decision making in dynamic and uncertain environments, game theory, planning and scheduling, design and education are common research areas shared between games and real-world problems. Numerous open-source games or game-based environments have been implemented for studying artificial intelligence. In addition to single- or multi-player, collaborative or adversarial games, there has also been growing interest in implementing platforms for creative design in recent years. Those platforms provide ideal benchmarks for exploring and comparing artificial intelligence ideas and techniques. This paper reviews the games and game-based platforms for artificial intelligence research, provides guidance on matching particular types of artificial intelligence with suitable games for testing and matching particular needs in games with suitable artificial intelligence techniques, discusses the research trend induced by the evolution of those games and platforms, and gives an outlook.

Artificial intelligence (AI) models are increasingly finding applications in the field of medicine. Concerns have been raised about the explainability of the decisions that are made by these AI models. In this article, we give a systematic analysis of explainable artificial intelligence (XAI), with a primary focus on models that are currently being used in the field of healthcare. The literature search is conducted following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) standards for relevant work published from 1 January 2012 to 02 February 2022. The review analyzes the prevailing trends in XAI and lays out the major directions in which research is headed. We investigate the why, how, and when of the uses of these XAI models and their implications. We present a comprehensive examination of XAI methodologies as well as an explanation of how a trustworthy AI can be derived from describing AI models for healthcare fields. The discussion of this work will contribute to the formalization of the XAI field.

This article reviews the "Once learning" mechanism that was proposed 23 years ago and the subsequent successes of "One-shot learning" in image classification and "You Only Look Once - YOLO" in objective detection. Analyzing the current development of Artificial Intelligence (AI), the proposal is that AI should be clearly divided into the following categories: Artificial Human Intelligence (AHI), Artificial Machine Intelligence (AMI), and Artificial Biological Intelligence (ABI), which will also be the main directions of theory and application development for AI. As a watershed for the branches of AI, some classification standards and methods are discussed: 1) Human-oriented, machine-oriented, and biological-oriented AI R&D; 2) Information input processed by Dimensionality-up or Dimensionality-reduction; 3) The use of one/few or large samples for knowledge learning.

The following briefly discusses possible difficulties in communication with and control of an AGI (artificial general intelligence), building upon an explanation of The Fermi Paradox and preceding work on symbol emergence and artificial general intelligence. The latter suggests that to infer what someone means, an agent constructs a rationale for the observed behaviour of others. Communication then requires two agents labour under similar compulsions and have similar experiences (construct similar solutions to similar tasks). Any non-human intelligence may construct solutions such that any rationale for their behaviour (and thus the meaning of their signals) is outside the scope of what a human is inclined to notice or comprehend. Further, the more compressed a signal, the closer it will appear to random noise. Another intelligence may possess the ability to compress information to the extent that, to us, their signals would appear indistinguishable from noise (an explanation for The Fermi Paradox). To facilitate predictive accuracy an AGI would tend to more compressed representations of the world, making any rationale for their behaviour more difficult to comprehend for the same reason. Communication with and control of an AGI may subsequently necessitate not only human-like compulsions and experiences, but imposed cognitive impairment.

In spite of the rapidly advancing global technological environment, the professional participation of women in technology, big data, analytics, artificial intelligence and information systems related domains remains proportionately low. Furthermore, it is of no less concern that the number of women in leadership in these domains are in even lower proportions. In spite of numerous initiatives to improve the participation of women in technological domains, there is an increasing need to gain additional insights into this phenomenon especially since it occurs in nations and geographies which have seen a sharp rise in overall female education, without such increase translating into a corresponding spurt in information systems and technological roles for women. The present paper presents findings from an exploratory analysis and outlines a framework to gain insights into educational factors in the emerging technology waves influencing the role of, and impact upon, women. We specifically identify ways for learning and self-efficacy as key factors, which together lead us to the Advancement of Women in Technology (AWT) insights framework. Based on the AWT framework, we also proposition principles that can be used to encourage higher professional engagement of women in emerging and advanced technologies. Key Words- Women's Education, Technology, Artificial Intelligence, Knowing, Confidence, Self-Efficacy, Learning.

The advent of artificial intelligence has changed many disciplines such as engineering, social science and economics. Artificial intelligence is a computational technique which is inspired by natural intelligence such as the swarming of birds, the working of the brain and the pathfinding of the ants. These techniques have impact on economic theories. This book studies the impact of artificial intelligence on economic theories, a subject that has not been extensively studied. The theories that are considered are: demand and supply, asymmetrical information, pricing, rational choice, rational expectation, game theory, efficient market hypotheses, mechanism design, prospect, bounded rationality, portfolio theory, rational counterfactual and causality. The benefit of this book is that it evaluates existing theories of economics and update them based on the developments in artificial intelligence field.

<p>What is artificial intelligence?</p> <p>That’s an easy question to ask and a hard one to answer—for two reasons. First, there’s little agreement about what intelligence is. Second, there’s scant reason to believe that machine intelligence bears much relationship to human intelligence, at least so...</p>

<p>Where did the term <italic>artificial intelligence</italic> come from?</p> <p>The first use of “artificial intelligence” can be attributed to a specific individual—John McCarthy, in 1956 an assistant professor of mathematics at Dartmouth College in Hanover, New Hampshire. Along with three other, more senior researchers (Marvin...</p>

In the modern healthcare system, rapidly expanding costs/complexity, the growing myriad of treatment options, and exploding information streams that often do not effectively reach the front lines hinder the ability to choose optimal treatment decisions over time. The goal in this paper is to develop a general purpose (non-disease-specific) computational/artificial intelligence (AI) framework to address these challenges. This serves two potential functions: 1) a simulation environment for exploring various healthcare policies, payment methodologies, etc., and 2) the basis for clinical artificial intelligence - an AI that can think like a doctor. This approach combines Markov decision processes and dynamic decision networks to learn from clinical data and develop complex plans via simulation of alternative sequential decision paths while capturing the sometimes conflicting, sometimes synergistic interactions of various components in the healthcare system. It can operate in partially observable environments (in the case of missing observations or data) by maintaining belief states about patient health status and functions as an online agent that plans and re-plans. This framework was evaluated using real patient data from an electronic health record. Such an AI framework easily outperforms the current treatment-as-usual (TAU) case-rate/fee-for-service models of healthcare (Cost per Unit Change: $189 vs. $497) while obtaining a 30-35% increase in patient outcomes. Tweaking certain model parameters further enhances this advantage, obtaining roughly 50% more improvement for roughly half the costs. Given careful design and problem formulation, an AI simulation framework can approximate optimal decisions even in complex and uncertain environments. Future work is described that outlines potential lines of research and integration of machine learning algorithms for personalized medicine.