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Research suggests that algorithms—based on artificial intelligence or linear regression models—make better predictions than humans in a wide range of domains. Several studies have examined the degree to which people use algorithms. However, these studies have been mostly cross‐sectional and thus have failed to address the dynamic nature of algorithm use. In the present paper, we examined algorithm use with a novel longitudinal approach outside the lab. Specifically, we conducted two ecological momentary assessment studies in which 401 participants made financial predictions for 18 days in two tasks. Relying on the judge‐advisor system framework, we examined how time interacted with advice source (human vs. algorithm) and advisor accuracy to predict advice taking. Our results showed that when the advice was inaccurate, people tended to use algorithm advice less than human advice across the period studied. Inaccurate algorithms were penalized logarithmically; the effect was initially strong but tended to fade over time. This suggests that first impressions are crucial and produce significant changes in advice taking at the beginning of the interaction, which later tends to stabilize as days go by. Therefore, inaccurate algorithms are more likely to accrue a negative reputation than inaccurate humans, even when having the same level of performance. [ABSTRACT FROM AUTHOR]

Surgical robot systems (SRS) represent an innovative cross‐disciplinary research field using robotic technology to assist surgeons in operations. Current bottlenecks in SRS, such as the limited ability to process complex information and make surgical decisions, have not been effectively solved. Artificial intelligence (AI) is a valuable technique for simulating and extending human intelligence. AI offers a new direction and impetus for SRS by enhancing performance in areas such as perception, navigation, surgical planning, and control strategies. This review introduces the developmental history of AI‐aided SRS, summarizes the basic SRS architecture, and analyzes how AI can improve SRS performance. Classical cases of AI‐aided SRS, the impact of evidence in clinical settings, and associated ethical and legal considerations are explored. Finally, the challenges in AI‐aided SRS are discussed, including algorithm development, surgical data science, human–robot coordination, and trust building between humans and robots. [ABSTRACT FROM AUTHOR]

The wide availability of computer technology and large electronic storage media has led to an enormous proliferation of databases in almost every area of human endeavour. This naturally creates an intense demand for powerful methods and tools for data analysis. Current methods and tools are primarily oriented toward extracting numerical and statistical data characteristics. While such characteristics are very important and useful, they are often insufficient. A decision maker typically needs an interpretation of these findings, and this has to be done by a data analyst. With the growth in the amount and complexity of the data, making such interpretations is an increasingly difficult problem. As a potential solution, this paper advocates the development of methods for conceptual data analysis. Such methods aim at semi-automating the processes of determining high-level data interpretations, and discovering qualitative patterns in data. It is argued that these methods could be built on the basis of algorithms developed in the area of machine learning. An exemplary system utilizing such algorithms, INLEN, is discussed. The system integrates machine learning and statistical analysis techniques with database and expert system technologies. Selected capabilities of the system are illustrated by examples from implemented modules. [ABSTRACT FROM AUTHOR]

The paper presents a novel machine learning algorithm used for training a compound classifier system that consists of a set of area classifiers. Area classifiers recognize objects derived from the respective competence area. Splitting feature space into areas and selecting area classifiers are two key processes of the algorithm; both take place simultaneously in the course of an optimization process aimed at maximizing the system performance. An evolutionary algorithm is used to find the optimal solution. A number of experiments have been carried out to evaluate system performance. The results prove that the proposed method outperforms each elementary classifier as well as simple voting. [ABSTRACT FROM AUTHOR]

Automated decision-making systems have been deployed in many industrial, commercial, and financial applications. The needs for such systems are usually motivated by requirements for variation reduction, capacity increase, cost and cycle time reduction, and end-to-end traceability of the transaction or product. Before we can use any automated decision-making system in a production environment we must develop a strategy to insure high quality throughout its entire lifecycle. We need to guarantee its performance through a rigorous Design for Six Sigma process (DFSS). This process includes validation, tuning, and production testing of the system. Once the system is in production we must monitor and maintain its performance over its lifecycle. In this paper we will outline the Six Sigma process that led to the deployment of an automated decision-making system in one of the General Electric Financial Assurance businesses. Copyright © 2005 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This study explores the impact of artificial intelligence (AI) on user entrepreneurs, driving factors and user entrepreneurship process combined with its characteristics and development trends. Moreover, this study sorts out the logic of user entrepreneurship change in the AI era. This study achieves four findings. First, AI contributes to entrepreneurs by collecting large user data and intelligently analysing them to obtain optimal entrepreneurial judgements and decisions. Second, entrepreneurs may use AI systems to understand users' potential needs and get user demand information (e.g. more accurate and more major shortcomings of the products). Third, AI assists entrepreneurs in obtaining robust data of product users, including leading and ordinary users, for a wider audience. Fourth, AI replaces the original intergenerational product replacement model with intermittent and periodic characteristics for entrepreneurial activities and reforms the three‐stage user entrepreneurial process: product element deconstruction, product verification matching and innovative product commercialization. This study provides a feasible direction for the key research issues. [ABSTRACT FROM AUTHOR]

Policy PointsWith increasing integration of artificial intelligence and machine learning in medicine, there are concerns that algorithm inaccuracy could lead to patient injury and medical liability.While prior work has focused on medical malpractice, the artificial intelligence ecosystem consists of multiple stakeholders beyond clinicians. Current liability frameworks are inadequate to encourage both safe clinical implementation and disruptive innovation of artificial intelligence.Several policy options could ensure a more balanced liability system, including altering the standard of care, insurance, indemnification, special/no‐fault adjudication systems, and regulation. Such liability frameworks could facilitate safe and expedient implementation of artificial intelligence and machine learning in clinical care. [ABSTRACT FROM AUTHOR]

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This study proposes the use of a modified genetic algorithm (MGA), a global search technique, as a training method to improve generalizability and to identify relevant inputs in a neural network (NN) model. Generalizability refers to the NN model's ability to perform well on exemplars (observations) that were not used during training (out-of-sample); improved generalizability enhances NN's acceptability as a valid decision-support tool. The MGA improves generalizability by setting unnecessary weights (or connections) to zero and by eliminating these weights. Because the eliminated weights have no further impact on the training (in-sample or out-of-sample data), the relevant variables can be identified from the model. By eliminating unnecessary weights, the MGA is able to search and find a parsimonious model that generalizes well. Unlike the traditional NN, the MGA identifies the model variables that contribute to an outcome, helping decision makers to rationalize output and accept results with greater confidence. The study uses real-life data to demonstrate the use of MGA. [ABSTRACT FROM AUTHOR]

Medical therapy for cardiac arrhythmias is still to a large extent based on empirical methods. Assessing and evaluating different therapeutical strategies constitutes the starting point for inducing decision methods to select the appropriate regimen for an individual patient. We designed a computer-based system that establishes a set o/heuristic rules linking attributes in a data base of patients with rhythm disturbances. A feasibility analysis conducted on a small set of 23 patients indicated that constraints on the number of attributes and their clinical relevancy together with a representation scheme for temporal changes have to be incorporated to provide for a useful and efficient algorithm. [ABSTRACT FROM AUTHOR]