Machine learning and deep learning

Electronic Markets, Journal Year: 2021, Volume and Issue: 31(3), P. 685 - 695

Published: April 8, 2021

Abstract Today, intelligent systems that offer artificial intelligence capabilities often rely on machine learning. Machine learning describes the capacity of to learn from problem-specific training data automate process analytical model building and solve associated tasks. Deep is a concept based neural networks. For many applications, deep models outperform shallow traditional analysis approaches. In this article, we summarize fundamentals generate broader understanding methodical underpinning current systems. particular, provide conceptual distinction between relevant terms concepts, explain automated through learning, discuss challenges arise when implementing such in field electronic markets networked business. These naturally go beyond technological aspects highlight issues human-machine interaction servitization.

Language: Английский

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Artificial intelligence, machine learning and deep learning in advanced robotics, a review

Cognitive Robotics, Journal Year: 2023, Volume and Issue: 3, P. 54 - 70

Published: Jan. 1, 2023

Artificial Intelligence (AI), Machine Learning (ML), and Deep (DL) have revolutionized the field of advanced robotics in recent years. AI, ML, DL are transforming robotics, making robots more intelligent, efficient, adaptable to complex tasks environments. Some applications include autonomous navigation, object recognition manipulation, natural language processing, predictive maintenance. These technologies also being used development collaborative (cobots) that can work alongside humans adapt changing environments tasks. The be transportation systems order provide safety, efficiency, convenience passengers companies . Also, playing a critical role advancement manufacturing assembly robots, enabling them efficiently, safely, intelligently. Furthermore, they wide range aviation management, helping airlines improve reduce costs, customer satisfaction. Moreover, help taxi better, safer services customers. research presents an overview current developments discusses various robot modification. Further works regarding suggested fill gaps between existing studies published papers. By reviewing systems, it is possible investigate modify performances enhance productivity robotic industries.

Language: Английский

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Investigating the Influence of Artificial Intelligence on Business Value in the Digital Era of Strategy: A Literature Review

Information, Journal Year: 2023, Volume and Issue: 14(2), P. 85 - 85

Published: Feb. 2, 2023

For organizations, the development of new business models and competitive advantages through integration artificial intelligence (AI) in IT strategies holds considerable promise. The majority businesses are finding it difficult to take advantage opportunities for value creation while other pioneers successfully utilizing AI. On basis research methodology Webster Watson (2020), 139 peer-reviewed articles were discussed. According literature, performance advantages, success criteria, difficulties adopting AI have been emphasized prior research. results this review revealed open issues topics that call further research/examination order develop capabilities integrate them into business/IT enhance various streams. Organizations will only succeed digital transformation alignment present era by precisely implementing these new, cutting-edge technologies. Despite revolutionary potential may promote, resource orchestration, along with governance dynamic environment, is still complex enough early stages regarding strategic implementation which issue aims address and, as a result, assist future organizations effectively outcomes.

Language: Английский

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Worker and workplace Artificial Intelligence (AI) coexistence: Emerging themes and research agenda

Technovation, Journal Year: 2023, Volume and Issue: 124, P. 102747 - 102747

Published: March 15, 2023

Workplace Artificial Intelligence (AI) helps organisations increase operational efficiency, enable faster-informed decisions, and innovate products services. While there is a plethora of information about how AI may provide value to workplaces, research on workers can coexist in workplaces evolving. It critical explore emerging themes agendas understand the trajectory scholarly this area. This study's overarching question will with workplaces. A search protocol was employed find relevant articles Scopus, ProQuest, Web Science databases based appropriate specific keywords article inclusion exclusion criteria. We identified four themes: (1) Workers' distrust workplace stems from perceiving it as job threat, (2) entices worker-AI interactions by offering augment worker abilities, (3) coexistence require workers' technical, human, conceptual skills, (4) Workers need ongoing reskilling upskilling contribute symbiotic relationship AI. then developed propositions questions for future research. review makes contributions: argues that an existential argument better explains AI, gathers required skills groups them into suggests technical benefit but cannot outweigh human offers 20 evidence-informed guide inquiries.

Language: Английский

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Multi-center federated learning: clients clustering for better personalization

World Wide Web, Journal Year: 2022, Volume and Issue: 26(1), P. 481 - 500

Published: June 9, 2022

Personalized decision-making can be implemented in a Federated learning (FL) framework that collaboratively train decision model by extracting knowledge across intelligent clients, e.g. smartphones or enterprises. FL mitigate the data privacy risk of collaborative training since it merely collects local gradients from users without access to their data. However, is fragile presence statistical heterogeneity commonly encountered personalized decision-making, e.g., non-IID over different clients. Existing approaches usually update single global capture shared all aggregating gradients, regardless discrepancy between distributions. By comparison, mixture multiple models could various clients if assigning client (i.e., centers) FL. To this end, we propose novel multi-center aggregation mechanism cluster using models' parameters. It learns as centers, and simultaneously derives optimal matching centers. We then formulate an optimization problem efficiently solved stochastic expectation maximization (EM) algorithm. Experiments on benchmark datasets show our method outperforms several popular baseline methods. The experimental source codes are publicly available Github repository https://github.com/mingxuts/multi-center-fed-learning .

Language: Английский

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Machine Learning (ML) in Medicine: Review, Applications, and Challenges

Mathematics, Journal Year: 2021, Volume and Issue: 9(22), P. 2970 - 2970

Published: Nov. 21, 2021

Today, artificial intelligence (AI) and machine learning (ML) have dramatically advanced in various industries, especially medicine. AI describes computational programs that mimic simulate human intelligence, for example, a person’s behavior solving problems or his ability learning. Furthermore, ML is subset of intelligence. It extracts patterns from raw data automatically. The purpose this paper to help researchers gain proper understanding its applications healthcare. In paper, we first present classification learning-based schemes According our proposed taxonomy, healthcare are categorized based on pre-processing methods (data cleaning methods, reduction methods), (unsupervised learning, supervised semi-supervised reinforcement learning), evaluation (simulation-based practical implementation-based real environment) (diagnosis, treatment). classification, review some studies presented We believe helps familiarize themselves with the newest research medicine, recognize their challenges limitations area, identify future directions.

Language: Английский

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Recognizing and Utilizing Novel Research Opportunities with Artificial Intelligence

Academy of Management Journal, Journal Year: 2023, Volume and Issue: 66(2), P. 367 - 373

Published: April 1, 2023

Language: Английский

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Artificial Intelligence in Business Communication: The Changing Landscape of Research and Teaching

Business and Professional Communication Quarterly, Journal Year: 2022, Volume and Issue: 85(1), P. 7 - 33

Published: Feb. 3, 2022

The rapid, widespread implementation of artificial intelligence technologies in workplaces has implications for business communication. In this article, the authors describe current capabilities, challenges, and concepts related to adoption use (AI) Understanding abilities inabilities AI is critical using these ethically. offer a proposed research agenda researchers communication concerning topics implementation, lexicography grammar, collaboration, design, trust, bias, managerial concerns, tool assessment, demographics. conclude with some ideas regarding how teach about classroom.

Language: Английский

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Artificial intelligence focus and firm performance

Journal of the Academy of Marketing Science, Journal Year: 2022, Volume and Issue: 50(6), P. 1176 - 1197

Published: June 8, 2022

Abstract Artificial Intelligence is poised to transform all facets of marketing. In this study, we examine the link between firms’ focus on AI in their 10-K reports and gross net operating efficiency. are a salient source insight into an array issues accounting finance research, yet remain relatively overlooked Drawing upon economic marketing theory, develop guiding framework show how could be related We then use system simultaneous equations empirically test relationship Our findings confirm that US-listed firms state impending transformation with regards AI. associated improvements profitability, efficiency return marketing-related investment while reducing adspend creating jobs.

Language: Английский

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Combining Human and Artificial Intelligence: Hybrid Problem-Solving in Organizations

Academy of Management Review, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 5, 2024

Organizations increasingly use artificial intelligence (AI) to solve previously unexplored problems. While routine tasks can be automated, the intricate nature of exploratory tasks, such as solving new problems, demands a hybrid approach that integrates human with AI. We argue outcomes this human–AI collaboration are contingent on processes employed combine and Our model unpacks three problem-solving their outcomes: Compared problem-solving, autonomous search generates more distant solutions, sequential enables local interactive promotes recombinative ones. Collectively, these broaden range organizational outcomes. enrich behavioral theory firm technology-conscious perspective complements its traditional human-centric perspective. Additionally, we contribute literature AI in management by extending scope from using predictive for generative applications tasks.

Language: Английский

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