Artificial intelligence
36024272
Explanation in Artificial Intelligence: Insights from the Social Sciences
There has been a recent resurgence in the area of explainable artificial intelligence as researchers and practitioners seek to make their algorithms more understandable. Much of this research is focused on explicitly explaining decisions or actions to a human observer, and it should not be controversial to say that looking at how humans explain to each other can serve as a useful starting point for explanation in artificial intelligence. However, it is fair to say that most work in explainable artificial intelligence uses only the researchers' intuition of what constitutes a `good' explanation. There exists vast and valuable bodies of research in philosophy, psychology, and cognitive science of how people define, generate, select, evaluate, and present explanations, which argues that people employ certain cognitive biases and social expectations towards the explanation process. This paper argues that the field of explainable artificial intelligence should build on this existing research, and reviews relevant papers from philosophy, cognitive psychology/science, and social psychology, which study these topics. It draws out some important findings, and discusses ways that these can be infused with work on explainable artificial intelligence.
Artif. Intell.
Tim Miller
204824113
Explainable Artificial Intelligence (XAI) Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI
In the last years, Artificial Intelligence (AI) has achieved a notable momentum that may deliver the best of expectations over many application sectors across the field. For this to occur, the entire community stands in front of the barrier of explainability, an inherent problem of AI techniques brought by sub symbolism (e.g. ensembles or Deep Neural Networks) that were not present in the last hype of AI. Paradigms underlying this problem fall within the so called eXplainable AI (XAI) field, which is acknowledged as a crucial feature for the practical deployment of AI models. This overview examines the existing literature in the field of XAI, including a prospect toward what is yet to be reached. We summarize previous efforts to define explainability in Machine Learning, establishing a novel definition that covers prior conceptual propositions with a major focus on the audience for which explainability is sought. We then propose and discuss about a taxonomy of recent contributions related to the explainability of different Machine Learning models, including those aimed at Deep Learning methods for which a second taxonomy is built. This literature analysis serves as the background for a series of challenges faced by XAI, such as the crossroads between data fusion and explainability. Our prospects lead toward the concept of Responsible Artificial Intelligence, namely, a methodology for the large scale implementation of AI methods in real organizations with fairness, model explainability and accountability at its core. Our ultimate goal is to provide newcomers to XAI with a reference material in order to stimulate future research advances, but also to encourage experts and professionals from other disciplines to embrace the benefits of AI in their activity sectors, without any prior bias for its lack of interpretability.
Inf. Fusion
Alejandro Barredo Arrieta Javier Del Ser Adrien Bennetot Siham Tabik A Barbado Daniel Molina Rick Benjamins Raja Chatila Francisco Herrera
53142908
Artificial Intelligence: A Modern Approach
The long anticipated revision of this #1 selling book offers the most comprehensive, state of the art introduction to the theory and practice of artificial intelligence for modern applications. Intelligent Agents. Solving Problems by Searching. Informed Search Methods. Game Playing. Agents that Reason Logically. First order Logic. Building a Knowledge Base. Inference in First Order Logic. Logical Reasoning Systems. Practical Planning. Planning and Acting. Uncertainty. Probabilistic Reasoning Systems. Making Simple Decisions. Making Complex Decisions. Learning from Observations. Learning with Neural Networks. Reinforcement Learning. Knowledge in Learning. Agents that Communicate. Practical Communication in English. Perception. Robotics. For computer professionals, linguists, and cognitive scientists interested in artificial intelligence.
Stuart J Russell Peter Norvig
57574615
High performance medicine: the convergence of human and artificial intelligence
The use of artificial intelligence, and the deep learning subtype in particular, has been enabled by the use of labeled big data, along with markedly enhanced computing power and cloud storage, across all sectors. In medicine, this is beginning to have an impact at three levels: for clinicians, predominantly via rapid, accurate image interpretation; for health systems, by improving workflow and the potential for reducing medical errors; and for patients, by enabling them to process their own data to promote health. The current limitations, including bias, privacy and security, and lack of transparency, along with the future directions of these applications will be discussed in this article. Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient doctor relationship or facilitate its erosion remains to be seen.Artificial intelligence is beginning to be applied in the medical setting and has potential to improve workflows and errors, impacting patients and clinicians alike.
Nature Medicine
Eric J Topol
211133166
Artificial Intelligence Forecasting of Covid 19 in China
BACKGROUND An alternative to epidemiological models for transmission dynamics of Covid 19 in China, we propose the artificial intelligence (AI) inspired methods for real time forecasting of Covid 19 to estimate the size, lengths and ending time of Covid 19 across China. METHODS We developed a modified stacked auto encoder for modeling the transmission dynamics of the epidemics. We applied this model to real time forecasting the confirmed cases of Covid 19 across China. The data were collected from January 11 to February 27, 2020 by WHO. We used the latent variables in the auto encoder and clustering algorithms to group the provinces/cities for investigating the transmission structure. RESULTS We forecasted curves of cumulative confirmed cases of Covid 19 across China from Jan 20, 2020 to April 20, 2020. Using the multiple step forecasting, the estimated average errors of 6 step, 7 step, 8 step, 9 step and 10 step forecasting were 1.64% 2.27% 2.14% 2.08% 0.73% respectively. We predicted that the time points of the provinces/cities entering the plateau of the forecasted transmission dynamic curves varied, ranging from Jan 21 to April 19, 2020. The 34 provinces/cities were grouped into 9 clusters. CONCLUSIONS The accuracy of the AI based methods for forecasting the trajectory of Covid 19 was high. We predicted that the epidemics of Covid 19 will be over by the middle of April. If the data are reliable and there are no second transmissions, we can accurately forecast the transmission dynamics of the Covid 19 across the provinces/cities in China. The AI inspired methods are a powerful tool for helping public health planning and policymaking.
Zixin Hu Qiyang Ge Shudi Li Li Jin Momiao Xiong
204871987
How artificial intelligence will change the future of marketing
In the future, artificial intelligence (AI) is likely to substantially change both marketing strategies and customer behaviors. Building from not only extant research but also extensive interactions with practice, the authors propose a multidimensional framework for understanding the impact of AI involving intelligence levels, task types, and whether AI is embedded in a robot. Prior research typically addresses a subset of these dimensions; this paper integrates all three into a single framework. Next, the authors propose a research agenda that addresses not only how marketing strategies and customer behaviors will change in the future, but also highlights important policy questions relating to privacy, bias and ethics. Finally, the authors suggest AI will be more effective if it augments (rather than replaces) human managers.
Journal of the Academy of Marketing Science
Thomas H Davenport Abhijit Guha Dhruv Grewal Timna Bressgott
215819243
Artificial Intelligence (AI) applications for COVID 19 pandemic
Abstract Background and aims Healthcare delivery requires the support of new technologies like Artificial Intelligence (AI) Internet of Things (IoT) Big Data and Machine Learning to fight and look ahead against the new diseases. We aim to review the role of AI as a decisive technology to analyze, prepare us for prevention and fight with COVID 19 (Coronavirus) and other pandemics. Methods The rapid review of the literature is done on the database of Pubmed, Scopus and Google Scholar using the keyword of COVID 19 or Coronavirus and Artificial Intelligence or AI. Collected the latest information regarding AI for COVID 19, then analyzed the same to identify its possible application for this disease. Results We have identified seven significant applications of AI for COVID 19 pandemic. This technology plays an important role to detect the cluster of cases and to predict where this virus will affect in future by collecting and analyzing all previous data. Conclusions Healthcare organizations are in an urgent need for decision making technologies to handle this virus and help them in getting proper suggestions in real time to avoid its spread. AI works in a proficient way to mimic like human intelligence. It may also play a vital role in understanding and suggesting the development of a vaccine for COVID 19. This result driven technology is used for proper screening, analyzing, prediction and tracking of current patients and likely future patients. The significant applications are applied to tracks data of confirmed, recovered and death cases.
Diabetes Metabolic Syndrome: Clinical Research Reviews
Raju Vaishya Mohd Javaid Ibrahim Haleem Khan Abid Haleem
218682148
Artificial intelligence enabled rapid diagnosis of patients with COVID 19
For diagnosis of coronavirus disease 2019 (COVID 19) a SARS CoV 2 virus specific reverse transcriptase polymerase chain reaction (RT PCR) test is routinely used. However, this test can take up to 2 d to complete, serial testing may be required to rule out the possibility of false negative results and there is currently a shortage of RT PCR test kits, underscoring the urgent need for alternative methods for rapid and accurate diagnosis of patients with COVID 19. Chest computed tomography (CT) is a valuable component in the evaluation of patients with suspected SARS CoV 2 infection. Nevertheless, CT alone may have limited negative predictive value for ruling out SARS CoV 2 infection, as some patients may have normal radiological findings at early stages of the disease. In this study, we used artificial intelligence (AI) algorithms to integrate chest CT findings with clinical symptoms, exposure history and laboratory testing to rapidly diagnose patients who are positive for COVID 19. Among a total of 905 patients tested by real time RT PCR assay and next generation sequencing RT PCR, 419 (46.3% tested positive for SARS CoV 2. In a test set of 279 patients, the AI system achieved an area under the curve of 0.92 and had equal sensitivity as compared to a senior thoracic radiologist. The AI system also improved the detection of patients who were positive for COVID 19 via RT PCR who presented with normal CT scans, correctly identifying 17 of 25 (68% patients, whereas radiologists classified all of these patients as COVID 19 negative. When CT scans and associated clinical history are available, the proposed AI system can help to rapidly diagnose COVID 19 patients. Artificial intelligence algorithms integrating chest computed tomography scans and clinical information can diagnose COVID 19 with similar accuracy as compared to a senior radiologist.
Nature Medicine
Xueyan Mei Hao-Chih Lee Kaiyue Diao Mingqian Huang Bin Lin Chenyu Liu Zongyu Xie Yixuan Ma Philip M Robson Michael S Chung Adam Bernheim Venkatesh Mani Claudia Calcagno Kunwei Li Shaolin Li Hong Shan Jian Lv Tongtong Zhao Junli Xia Qihua Long Sharon Steinberger Adam Jacobi Timothy Deyer Marta Luksza Fang Liu Brent P Little Zahi A Fayad Yang Yang
143425657
The role of artificial intelligence in achieving the Sustainable Development Goals
The emergence of artificial intelligence (AI) and its progressively wider impact on many sectors requires an assessment of its effect on the achievement of the Sustainable Development Goals. Using a consensus based expert elicitation process, we find that AI can enable the accomplishment of 134 targets across all the goals, but it may also inhibit 59 targets. However, current research foci overlook important aspects. The fast development of AI needs to be supported by the necessary regulatory insight and oversight for AI based technologies to enable sustainable development. Failure to do so could result in gaps in transparency, safety, and ethical standards. Artificial intelligence (AI) is becoming more and more common in people's lives. Here, the authors use an expert elicitation method to understand how AI may affect the achievement of the Sustainable Development Goals.
Nature Communications
Ricardo Vinuesa Hossein Azizpour Iolanda Leite Madeline Balaam Virginia Dignum Sami Domisch Anna Fellander Simone Daniela Langhans Max Tegmark Francesco Fuso Nerini
214679246
Artificial intelligence and machine learning to fight COVID 19
Physiological genomics
Ahmad Alimadadi Sachin Aryal Ishan Manandhar Patricia B Munroe Bina Joe Xi Cheng