Hasil untuk "artificial intelligence"

B. Lake, T. Ullman, J. Tenenbaum et al.

Abstract Lake et al. offer a timely critique on the recent accomplishments in artificial intelligence from the vantage point of human intelligence and provide insightful suggestions about research directions for building more human-like intelligence. Because we agree with most of the points they raised, here we offer a few points that are complementary.

Ido Roll, Ruth Wylie

Julia Kokina, T. Davenport

Prashansa Agrawal

A. Winfield, M. Jirotka

This paper explores the question of ethical governance for robotics and artificial intelligence (AI) systems. We outline a roadmap—which links a number of elements, including ethics, standards, regulation, responsible research and innovation, and public engagement—as a framework to guide ethical governance in robotics and AI. We argue that ethical governance is essential to building public trust in robotics and AI, and conclude by proposing five pillars of good ethical governance. This article is part of the theme issue ‘Governing artificial intelligence: ethical, legal, and technical opportunities and challenges’.

A. Fogel, J. Kvedar

Artificial intelligence (AI) has recently surpassed human performance in several domains, and there is great hope that in healthcare, AI may allow for better prevention, detection, diagnosis, and treatment of disease. While many fear that AI will disrupt jobs and the physician–patient relationship, we believe that AI can eliminate many repetitive tasks to clear the way for human-to-human bonding and the application of emotional intelligence and judgment. We review several recent studies of AI applications in healthcare that provide a view of a future where healthcare delivery is a more unified, human experience.

Virginia Dignum

J. A. Nichols, Hsien W. Herbert Chan, M. Baker

Odd Erik Gundersen, Sigbjørn Kjensmo

Background: Research results in artificial intelligence (AI) are criticized for not being reproducible. Objective: To quantify the state of reproducibility of empirical AI research using six reproducibility metrics measuring three different degrees of reproducibility. Hypotheses: 1) AI research is not documented well enough to reproduce the reported results. 2) Documentation practices have improved over time. Method: The literature is reviewed and a set of variables that should be documented to enable reproducibility are grouped into three factors: Experiment, Data and Method. The metrics describe how well the factors have been documented for a paper. A total of 400 research papers from the conference series IJCAI and AAAI have been surveyed using the metrics. Findings: None of the papers document all of the variables. The metrics show that between 20% and 30% of the variables for each factor are documented. One of the metrics show statistically significant increase over time while the others show no change. Interpretation: The reproducibility scores decrease with in- creased documentation requirements. Improvement over time is found. Conclusion: Both hypotheses are supported.

Ashish Ghosh, Debasrita Chakraborty, Anwesha Law

Functioning of the Internet is persistently transforming from the Internet of computers (IoC) to the ‘Internet of things (IoT)’. Furthermore, massively interconnected systems, also known as cyber-physical systems (CPSs), are emerging from the assimilation of many facets like infrastructure, embedded devices, smart objects, humans, and physical environments. What the authors are heading to is a huge ‘Internet of Everything in a Smart Cyber Physical Earth’. IoT and CPS conjugated with ‘data science’ may emerge as the next ‘smart revolution’. The concern that arises then is to handle the huge data generated with the much weaker existing computation power. The research in data science and artificial intelligence (AI) has been striving to give an answer to this problem. Thus, IoT with AI can become a huge breakthrough. This is not just about saving money, smart things, reducing human effort, or any trending hype. This is much more than that – easing human life. There are, however, some serious issues like the security concerns and ethical issues which will go on plaguing IoT. The big picture is not how fascinating IoT with AI seems, but how the common people perceive it – a boon, a burden, or a threat.

Leily Bakhtiari

The Metaverse, as a hypothetical virtual environment utilizing advanced technologies such as Artificial Intelligence and the Internet of Things, has the potential to contribute to the sustainability of future smart cities, enhancing urban efficiency and quality of life. In this regard, the present study aims to analyze the framework of immersive Metaverse spaces for the sustainability of future smart cities in the metropolitan area of Tehran. The research strategy is application-oriented, and the methodology is descriptive-analytical, based on exploratory futures research methods. The theoretical data were collected using a documentary method, and the empirical data were gathered through a survey method based on the Delphi technique. The target population of this study included urban experts, and a purposive sample of 70 individuals was selected. To analyze the data, the Delphi method, structural analysis in the MICMAC environment, and Scenario Wizard software were employed. The findings revealed that indicators such as transparency, demand reduction, urban simulation, and digital education play significant roles in improving smart urban sustainability. Additionally, the study demonstrated that the Metaverse can serve as an effective tool for optimizing resource consumption, increasing citizen participation, and mitigating urban risks. The final results suggest that to fully harness the potential of the Metaverse, it is necessary to strengthen digital infrastructures, enhance technology-related education, and improve cybersecurity. Overall, it is recommended that policymakers in Tehran focus on developing Metaverse technologies and integrating them with urban systems to leverage all the capabilities of this technology.

Maissam Barkeshli, Michael R. Douglas, Michael H. Freedman

Recent progress in artificial intelligence (AI) is unlocking transformative capabilities for mathematics. There is great hope that AI will help solve major open problems and autonomously discover new mathematical concepts. In this essay, we further consider how AI may open a grand perspective on mathematics by forging a new route, complementary to mathematical\textbf{ logic,} to understanding the global structure of formal \textbf{proof}\textbf{s}. We begin by providing a sketch of the formal structure of mathematics in terms of universal proof and structural hypergraphs and discuss questions this raises about the foundational structure of mathematics. We then outline the main ingredients and provide a set of criteria to be satisfied for AI models capable of automated mathematical discovery. As we send AI agents to traverse Platonic mathematical worlds, we expect they will teach us about the nature of mathematics: both as a whole, and the small ribbons conducive to human understanding. Perhaps they will shed light on the old question: "Is mathematics discovered or invented?" Can we grok the terrain of these \textbf{Platonic worlds}?

Philipp Zech, Istvan David

The convergence of modeling & simulation (M&S) and artificial intelligence (AI) is leaving its marks on advanced digital technology. Pertinent examples are digital twins (DTs) - high-fidelity, live representations of physical assets, and frequent enablers of corporate digital maturation and transformation. Often seen as technological platforms that integrate an array of services, DTs have the potential to bring AI-enabled M&S closer to end-users. It is, therefore, paramount to understand the role of M&S in DTs, and the role of digital twins in enabling the convergence of AI and M&S. To this end, this chapter provides a comprehensive exploration of the complementary relationship between these three. We begin by establishing a foundational understanding of DTs by detailing their key components, architectural layers, and their various roles across business, development, and operations. We then examine the central role of M&S in DTs and provide an overview of key modeling techniques from physics-based and discrete-event simulation to hybrid approaches. Subsequently, we investigate the bidirectional role of AI: first, how AI enhances DTs through advanced analytics, predictive capabilities, and autonomous decision-making, and second, how DTs serve as valuable platforms for training, validating, and deploying AI models. The chapter concludes by identifying key challenges and future research directions for creating more integrated and intelligent systems.

Ajay Agrawal, J. Gans, Avi Goldfarb

Ajay Agrawal, J. Gans, Avi Goldfarb

Recent progress in artificial intelligence (AI)—a general purpose technology affecting many industries—has been focused on advances in machine learning, which we recast as a quality-adjusted drop in the price of prediction. How will this sharp drop in price impact society? Policy will influence the impact on two key dimensions: diffusion and consequences. First, in addition to subsidies and intellectual property (IP) policy that will influence the diffusion of AI in ways similar to their effect on other technologies, three policy categories—privacy, trade, and liability—may be uniquely salient in their influence on the diffusion patterns of AI. Second, labor and antitrust policies will influence the consequences of AI in terms of employment, inequality, and competition.

Peter Hahn

Shuling Wang, Feng Yang, Suixue Wang et al.

Pulmonary diseases, such as pneumonia and lung abscess, can trigger sepsis, while sepsis-induced immune dysfunction exacerbates Pulmonary tissue damage, creating a vicious cycle. Therefore, designing a safe and effective clinical treatment planning method for sepsis is critically significant. In recent years, deep reinforcement learning (DRL), as one of the artificial intelligence technologies, has achieved remarkable results in the field of sepsis treatment. However, DRL models may be attacked due to their sensitive training data and their high commercial value, especially with the increasing number of DRL models being released on the Internet. Consequently, protecting the “privacy” of DRL models and training data has become an urgent problem. To address this issue, we propose a differential privacy-based DRL model for sepsis treatment. Furthermore, we investigate the impact of differential privacy mechanisms on the performance of the DRL model. Experimental results demonstrate that integrating differential privacy into DRL models enables clinicians to design sepsis treatment plans while protecting patient privacy, thereby mitigating lung tissue damage and dysfunction caused by sepsis.

Dawid Pawuś, Tomasz Porażko, Szczepan Paszkiel

Focal segmental glomerulosclerosis (FSGS) presents significant challenges in diagnosis, treatment, and management due to its complex etiology and clinical variability. Traditional approaches often rely on clinician judgment and are prone to inconsistencies. This study introduces an advanced expert system integrating Artificial Intelligence (AI) with Machine Learning (ML) to support nephrologists in assessing, treating, and managing FSGS. The proposed system features a modular design comprising diagnostic workflows, risk stratification, treatment guidance, and outcome monitoring modules. By leveraging ML algorithms and clinical data, the system offers personalized, data-driven recommendations, enhancing decision-making and patient care. The evaluation demonstrates the system’s efficacy in reducing diagnostic errors and optimizing treatment pathways. These findings underscore the potential of AI-driven tools in transforming nephrology practice and improving clinical outcomes for FSGS patients.

Omar Elfahim, Kokou Laris Edjinedja, Johan Cossus et al.

<i>Background:</i> The emergency medical services (EMS) sector, as a complex system, presents substantial hurdles in providing excellent treatment while operating within limited resources, prompting greater adoption of artificial intelligence (AI) as a tool for improving operational efficiency. While AI models have proved beneficial in healthcare operations, there is limited explainability and interpretability, as well as a lack of data used in their application and technological advancement. <i>Methods:</i> The scoping review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for scoping reviews, using PubMed, IEEE Xplore, and Web of Science, with a procedure of double screening and extraction. The search included articles published from 2018 to the beginning of 2025. Studies were excluded if they did not explicitly identify an artificial intelligence (AI) component, lacked relevance to emergency department (ED) or prehospital contexts, failed to report measurable outcomes or evaluations, or did not exploit real-world data. We analyzed the data source used, clinical subclasses, AI domains, ML algorithms, their performance, as well as potential roles for large language models (LLMs) in future applications. <i>Results:</i> A comprehensive PRISMA-guided methodology was used to search academic databases, finding 1181 papers on prehospital emergency treatment from 2018 to 2025, with 65 articles identified after an extensive screening procedure. The results reveal a significant increase in AI publications. A notable technological advancement in the application of AI in EMS using different types of data was explored. <i>Conclusions:</i> These findings highlighted that AI and ML have emerged as revolutionary innovations with huge potential in the fields of healthcare and medicine. There are several promising AI interventions that can improve prehospital emergency care, particularly for out-of-hospital cardiac arrest and triage prioritization scenarios. <i>Implications for EMS Practice:</i> Integrating AI methods into prehospital care can optimize the use of available resources, as well as triage and dispatch efficiency. LLMs may have the potential to improve understanding and assist in decision-making under pressure in emergency situations by combining various forms of recorded data. However, there is a need to emphasize continued research and strong collaboration between AI experts and EMS physicians to ensure the safe, ethical, and effective integration of AI into EMS practice.

Jorge Francisco Aguirre-Sala, Jorge Francisco Aguirre-Sala

The primary objective was to build a model that complements the provisions of the recent European Union AI regulation, the National Institute of Standards and Technology (NIST) Artificial Intelligence Risk Management Framework—which operationalizes the US President’s Executive Order 14110—and the first international standard for the Artificial Intelligence Management Systems (ISO/IEC 42001:2023) of the International Standardization Organization (ISO). This objective was a priority because Responsibility Articles 28 and 57 of the recent European Union regulations have set a deadline of August 2025 and August 2026 for designating an authority responsible for evaluations and testing before artificial intelligence (AI) systems are put into service. The previous objective analyzes the above regulations and provisions from the perspective of AI governance (AIG). That is, the approach seeks to balance the empowerment of algorithms in public administration with the citizen aspiration of empowerment. The method analytically reviews the stages and actions of an AI system to infer the design and development of the action and responsibilities of the actors involved in the AIG process from end to end. The results show a general AIG model for public administration that uses Artificial Intelligence before, during, and after its complete life cycle. The conclusions demand a holistic vision that includes both social and technical infrastructure.