Hasil untuk "artificial intelligence"

Miles Brundage, S. Avin, Jack Clark et al.

The following organisations are named on the report: Future of Humanity Institute, University of Oxford, Centre for the Study of Existential Risk, University of Cambridge, Center for a New American Security, Electronic Frontier Foundation, OpenAI. The Future of Life Institute is acknowledged as a funder.

B. Wirtz, Jan C. Weyerer, Carolin Geyer

ABSTRACT Advances in artificial intelligence (AI) have attracted great attention from researchers and practitioners and have opened up a broad range of beneficial opportunities for AI usage in the public sector. Against this background, there is an emerging need for a holistic understanding of the range and impact of AI-based applications and associated challenges. However, previous research considers AI applications and challenges only in isolation and fragmentarily. Given the lack of a comprehensive overview of AI-based applications and challenges for the public sector, our conceptual approach analyzes and compiles relevant insights from scientific literature to provide an integrative overview of AI applications and related challenges. Our results suggest 10 AI application areas, describing their value creation and functioning as well as specific public use cases. In addition, we identify four major dimensions of AI challenges. We finally discuss our findings, deriving implications for theory and practice and providing suggestions for future research.

Ravi B. Parikh, Stephanie Teeple, A. Navathe

G. Collins, K. Moons

www.thelancet.com Vol 393 April 20, 2019 1577 shortcut, investing in magic bullets such as vaccines, antiretrovirals, and bednets that could be distributed without a well functioning health­care system, and even in war zones. These programmes saved millions of lives, but failed to launch the anticipated virtuous cycle of development, poverty reduction, and improved health systems. Many African countries remain deeply impoverished, and although child mortality has plummeted, weak health systems remain ill­ equipped to address soaring numbers of deaths and disability from heart disease, cancer, and other non­ communicable diseases. Bollyky and colleagues’ findings suggest a possible reason for this dispiriting reality: many of the countries with the worst health systems are governed by corrupt autocrats who retain power by force and can ignore the welfare of their people without repercussions. To date, the global development community has skirted the complications of politics, instead emphasising medical programmes and the empower ment of women, the LGBT+ (lesbian, gay, bisexual, transgender, and all other identities) community, and people with disabilities—ie, people who do not directly challenge government power. This emphasis is sometimes justified by the longstanding assumption that in poor countries, especially, dictators are better at getting things done because they can ignore the demands of petty, competing constituencies. However, Bollyky’s findings support the theory that dictators might themselves be a cause of poverty and illness, and that democrats, however befuddled and disorganised, better serve their people. Unfortunately, the rights of pro­democracy activists are routinely violated with impunity, especially in those African countries most beset by poverty and ill health. Recent elections in Kenya, Uganda, Ethiopia, Rwanda, the Democratic Republic of the Congo, Gabon, Cameroon, and Zimbabwe were all marred by credible rigging allegations, which donors, in most cases, dismissed. When two opposition members of parliament in donor darling Uganda were beaten and crippled by security forces inside parliament in September, 2017, not one international donor or human rights organisation spoke out. Global health advocacy groups need to do more than clamour for more funding and occasionally bemoan corruption. They need to call on Washington (USA), Brussels (Belgium), London (UK), and other donors to impose sanctions on dictators, including those who cooperate with western military aims. As Rudolf Vichow, one of the pioneers of modern public health, wrote after witnessing the ravages of typhus on the oppressed peasants of Silesia, “politics is nothing but medicine at a larger scale”.

David Riaño, S. Wilk, A. T. Teije

Mei Chen, Michel Décary

Artificial Intelligence (AI) is evolving rapidly in healthcare, and various AI applications have been developed to solve some of the most pressing problems that health organizations currently face. It is crucial for health leaders to understand the state of AI technologies and the ways that such technologies can be used to improve the efficiency, safety, and access of health services, achieving value-based care. This article provides a guide to understand the fundamentals of AI technologies (ie, machine learning, natural language processing, and AI voice assistants) as well as their proper use in healthcare. It also provides practical recommendations to help decision-makers develop an AI strategy that can support their digital healthcare transformation.

Hao Zhu

Due to the massive data sets available for drug candidates, modern drug discovery has advanced to the big data era. Central to this shift is the development of artificial intelligence approaches to implementing innovative modeling based on the dynamic, heterogeneous, and large nature of drug data sets. As a result, recently developed artificial intelligence approaches such as deep learning and relevant modeling studies provide new solutions to efficacy and safety evaluations of drug candidates based on big data modeling and analysis. The resulting models provided deep insights into the continuum from chemical structure to in vitro, in vivo, and clinical outcomes. The relevant novel data mining, curation, and management techniques provided critical support to recent modeling studies. In summary, the new advancement of artificial intelligence in the big data era has paved the road to future rational drug development and optimization, which will have a significant impact on drug discovery procedures and, eventually, public health. Expected final online publication date for the Annual Review of Psychology, Volume 71 is January 4, 2020. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Xiaofeng Jin, Conghui Liu, Tailin Xu et al.

Artificial intelligence (AI) and wearable sensors are two essential fields to realize the goal of tailoring the best precision medicine treatment for individual patients. Integration of these two fields enables better acquisition of patient data and improved design of wearable sensors for monitoring the wearers' health, fitness and their surroundings. Currently, as the Internet of Things (IoT), big data and big health move from concept to implementation, AI-biosensors with appropriate technical characteristics are facing new opportunities and challenges. In this paper, the most advanced progress made in the key phases for future wearable and implantable technology from biosensing, wearable biosensing to AI-biosensing is summarized. Without a doubt, material innovation, biorecognition element, signal acquisition and transportation, data processing and intelligence decision system are the most important parts, which are the main focus of the discussion. The challenges and opportunities of AI-biosensors moving forward toward future medicine devices are also discussed.

S. Larsson, F. Heintz

This conceptual paper addresses the issues of transparency as linked to artificial intelligence (AI) from socio-legal and computer scientific perspectives. Firstly, we discuss the conceptual distinction between transparency in AI and algorithmic transparency, and argue for the wider concept ‘in AI’, as a partly contested albeit useful notion in relation to transparency. Secondly, we show that transparency as a general concept is multifaceted, and of widespread theoretical use in multiple disciplines over time, particularly since the 1990s. Still, it has had a resurgence in contemporary notions of AI governance, such as in the multitude of recently published ethics guidelines on AI. Thirdly, we discuss and show the relevance of the fact that transparency expresses a conceptual metaphor of more general significance, linked to knowing, bringing positive connotations that may have normative effects to regulatory debates. Finally, we draw a possible categorisation of aspects related to transparency in AI, or what we interchangeably call AI transparency, and argue for the need of developing a multidisciplinary understanding, in order to contribute to the governance of AI as applied on markets and in society. (Less)

Fei Wu, Cewu Lu, Ming-Li Zhu et al.

D. Hashimoto, E. Witkowski, Lei Gao et al.

Artificial intelligence has been advancing in fields including anesthesiology. This scoping review of the intersection of artificial intelligence and anesthesia research identified and summarized six themes of applications of artificial intelligence in anesthesiology: (1) depth of anesthesia monitoring, (2) control of anesthesia, (3) event and risk prediction, (4) ultrasound guidance, (5) pain management, and (6) operating room logistics. Based on papers identified in the review, several topics within artificial intelligence were described and summarized: (1) machine learning (including supervised, unsupervised, and reinforcement learning), (2) techniques in artificial intelligence (e.g., classical machine learning, neural networks and deep learning, Bayesian methods), and (3) major applied fields in artificial intelligence. The implications of artificial intelligence for the practicing anesthesiologist are discussed as are its limitations and the role of clinicians in further developing artificial intelligence for use in clinical care. Artificial intelligence has the potential to impact the practice of anesthesiology in aspects ranging from perioperative support to critical care delivery to outpatient pain management. This scoping review of artificial intelligence in anesthesiology summarizes six areas of research: (1) depth of anesthesia monitoring, (2) control of anesthesia, (3) event/risk prediction, (4) ultrasound guidance, (5) pain management, and (6) operating room logistics. Supplemental Digital Content is available in the text.

Ibrahim Habli, Tom Lawton, Zoe Porter

Abstract The prospect of patient harm caused by the decisions made by an artificial intelligence-based clinical tool is something to which current practices of accountability and safety worldwide have not yet adjusted. We focus on two aspects of clinical artificial intelligence used for decision-making: moral accountability for harm to patients; and safety assurance to protect patients against such harm. Artificial intelligence-based tools are challenging the standard clinical practices of assigning blame and assuring safety. Human clinicians and safety engineers have weaker control over the decisions reached by artificial intelligence systems and less knowledge and understanding of precisely how the artificial intelligence systems reach their decisions. We illustrate this analysis by applying it to an example of an artificial intelligence-based system developed for use in the treatment of sepsis. The paper ends with practical suggestions for ways forward to mitigate these concerns. We argue for a need to include artificial intelligence developers and systems safety engineers in our assessments of moral accountability for patient harm. Meanwhile, none of the actors in the model robustly fulfil the traditional conditions of moral accountability for the decisions of an artificial intelligence system. We should therefore update our conceptions of moral accountability in this context. We also need to move from a static to a dynamic model of assurance, accepting that considerations of safety are not fully resolvable during the design of the artificial intelligence system before the system has been deployed.

Paul Henman

Artificial intelligence arising from the use of machine learning is rapidly being developed and deployed by governments to enhance operations, public services, and compliance and security activities. This article reviews how artificial intelligence is being used in public sector for automated decision making, for chatbots to provide information and advice, and for public safety and security. It then outlines four public administration challenges to deploying artificial intelligence in public administration: accuracy, bias and discrimination; legality, due process and administrative justice; responsibility, accountability, transparency and explainability; and power, compliance and control. The article outlines technological and governance innovations that are being developed to address these challenges.

S. Ellahham

E. Neri, F. Coppola, V. Miele et al.

Ahmet Gocen, Fatih Aydemir

Abstract With the increase in studies about artificial intelligence (AI) in the educational field, many scholars in the field believe that the role of teachers, school and leaders in education will change. In this regard, the purpose of this study is to examine what possible scenarios are there with the arrival of AI in education and what kind of implications it can reveal for future of schools. The research was designed as a phenomenological study, a qualitative research method, in which the opinions of participants from different sectors were examined. The results show that schools and teachers will have new products, benefits and also face drawbacks with the arrival of AI in education. The findings point out some suggestions for use of AI and prevention of possible problems. While participants generally seem to have positive perceptions towards AI, there are also certain drawbacks, especially highlighted by teachers and academicians, regarding the future of teaching. Lawyers and jurists tend to focus more on legal grounds for AI in education and future problems, while engineers see AI as a tool to bring quality and benefit for all in the education sector.

J. Soun, D. Chow, M. Nagamine et al.

SUMMARY: Artificial intelligence technology is a rapidly expanding field with many applications in acute stroke imaging, including ischemic and hemorrhage subtypes. Early identification of acute stroke is critical for initiating prompt intervention to reduce morbidity and mortality. Artificial intelligence can help with various aspects of the stroke treatment paradigm, including infarct or hemorrhage detection, segmentation, classification, large vessel occlusion detection, Alberta Stroke Program Early CT Score grading, and prognostication. In particular, emerging artificial intelligence techniques such as convolutional neural networks show promise in performing these imaging-based tasks efficiently and accurately. The purpose of this review is twofold: first, to describe AI methods and available public and commercial platforms in stroke imaging, and second, to summarize the literature of current artificial intelligence–driven applications for acute stroke triage, surveillance, and prediction.

Paola Tubaro, A. Casilli, Marion Coville

This paper sheds light on the role of digital platform labour in the development of today’s artificial intelligence, predicated on data-intensive machine learning algorithms. Focus is on the specific ways in which outsourcing of data tasks to myriad ‘micro-workers’, recruited and managed through specialized platforms, powers virtual assistants, self-driving vehicles and connected objects. Using qualitative data from multiple sources, we show that micro-work performs a variety of functions, between three poles that we label, respectively, ‘artificial intelligence preparation’, ‘artificial intelligence verification’ and ‘artificial intelligence impersonation’. Because of the wide scope of application of micro-work, it is a structural component of contemporary artificial intelligence production processes – not an ephemeral form of support that may vanish once the technology reaches maturity stage. Through the lens of micro-work, we prefigure the policy implications of a future in which data technologies do not replace human workforce but imply its marginalization and precariousness.

Aleš Završnik

The automation brought about by big data analytics, machine learning and artificial intelligence systems challenges us to reconsider fundamental questions of criminal justice. The article outlines the automation which has taken place in the criminal justice domain and answers the question of what is being automated and who is being replaced thereby. It then analyses encounters between artificial intelligence systems and the law, by considering case law and by analysing some of the human rights affected. The article concludes by offering some thoughts on proposed solutions for remedying the risks posed by artificial intelligence systems in the criminal justice domain.

Stéphan Vincent‐Lancrin, R. V. D. Vlies

This paper was written to support the G20 artificial intelligence (AI) dialogue. With the rise of artificial intelligence (AI), education faces two challenges: reaping the benefits of AI to improve education processes, both in the classroom and at the system level; and preparing students for new skillsets for increasingly automated economies and societies. AI applications are often still nascent, but there are many examples of promising uses that foreshadow how AI might transform education. With regard to the classroom, this paper highlights how AI can accelerate personalised learning, the support of students with special needs. At the system level, promising uses include predictive analysis to reduce dropout, and assessing new skillsets. A new demand for complex skills that are less easy to automate (e.g. higher cognitive skills like creativity and critical thinking) is also the consequence of AI and digitalisation. Reaching the full potential of AI requires that stakeholders trust not only the technology, but also its use by humans. This raises new policy challenges around “trustworthy AI”, encompassing the privacy and security of data, but also possible wrongful uses of data leading to biases against individuals or groups.