Relevant bibliographies by topics / AI automation

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'AI automation'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 June 2025

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Journal articles on the topic "AI automation"

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Kumar, Sandeep, and Mohammed Abdul Qadeer. "Application of AI in Home Automation." International Journal of Engineering and Technology 4, no. 6 (2012): 803–7. http://dx.doi.org/10.7763/ijet.2012.v4.488.

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Undey, Cenk. "AI in Process Automation." SLAS TECHNOLOGY: Translating Life Sciences Innovation 26, no. 1 (2021): 1–2. http://dx.doi.org/10.1177/2472630320982260.

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Auth, Gunnar, Oliver Jokisch, and Christian Dürk. "Revisiting automated project management in the digital age – a survey of AI approaches." Online Journal of Applied Knowledge Management 7, no. 1 (2019): 27–39. http://dx.doi.org/10.36965/ojakm.2019.7(1)27-39.

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In this decade, remarkable progress has been made in the field of artificial intelligence (AI). Inspired by well-known services of cognitive assistance systems such as IBM Watson, Apple's Siri or Google Duplex, AI concepts and algorithms are widely discussed regarding their automation potentials in business, politics and society. At first glance, project management (PM) seems to be less suitable for automation due to the inherent uniqueness of projects by definition. However, AI is also creating new application possibilities in the PM area, which will be explored in this contribution by involving an extensive literature review as well as real-world examples. The objective of this article is to provide a current overview of AI approaches and available tools that can be used for automating tasks in business project management.
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Badet, Jaures. "AI, Automation and New Jobs." Open Journal of Business and Management 09, no. 05 (2021): 2452–63. http://dx.doi.org/10.4236/ojbm.2021.95132.

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Boavida, Nuno, and Marta Candeias. "Recent Automation Trends in Portugal: Implications on Industrial Productivity and Employment in Automotive Sector." Societies 11, no. 3 (2021): 101. http://dx.doi.org/10.3390/soc11030101.

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Recent developments in automation and artificial intelligence (AI) are leading to a wave of innovation in organizational design and changes in the workplace. Techno-optimists even named it the “second machine age,” arguing that it now involves the substitution of the human brain. Other authors see this as just a continuation of previous ICT developments. Potentially, automation and AI can have significant technical, economic, and social implications in firms. This paper will answer the following question: What are the implications on industrial productivity and employment in the automotive sector with the recent automation trends, including AI, in Portugal? Our approach used mixed methods to conduct statistical analyses of relevant databases and interviews with experts on R&D projects related to automation and AI implementation. Results suggest that automation can have widespread adoption in the short term in the automotive sector, but AI technologies will take more time to be adopted. The findings show that adoption of automation and AI increases productivity in firms and is dephased in time with employment implications. Investments in automation are not substituting operators but rather changing work organization. Thus, negative effects of technology and unemployment were not substantiated by our results.
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Jarrahi, Mohammad Hossein. "In the age of the smart artificial intelligence: AI’s dual capacities for automating and informating work." Business Information Review 36, no. 4 (2019): 178–87. http://dx.doi.org/10.1177/0266382119883999.

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Recent developments in artificial intelligence (AI) have generated tidal waves, which are shaking the foundation of organizations and businesses. Even though AI is considered an unprecedented disruptive force for work automation, much can be learned from current research on the computerization of work. Drawing on the seminal work of Shoshana Zuboff, this article provides a balanced perspective on the dual affordances of AI systems for automating and informating work. Whereas AI offers unique capacities for automating cognitive work that once required high-skill workers, it may be a source of unintended consequences such as cognitive complacency or de-skilling workers. To overcome such effects, the informating capacities of AI systems can be invoked to augment work, generate a more comprehensive perspective on organization, and finally equip workers with new sets of intellectual skills.
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Nakatani, Ichiro. "AI, Robotics and Automation in Space." Journal of Robotics and Mechatronics 12, no. 4 (2000): 443–45. http://dx.doi.org/10.20965/jrm.2000.p0443.

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Recently, AI, robotics and automation in space, referred to in a general term as ""space robotics"" in this paper, are playing increasingly more important roles for ground support, LEO satellites and planetary probes. In deep space missions, however, space robotics is a ""must"" due to the radio propagation delay and a poor communication link between the spacecraft and Earth. A typical example for robotics for planetary exploration is an autonomous rover that moves around on the surface of planetary bodies and conducts scientific investigations. A new infrastructure called ROBUST is proposed, which stands for ROBotized Unmanned Station. ROBUST is the space station that will be constructed, maintained and expanded by robotic technology completely without human presence in orbit.
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Strauß, Stefan. "Deep Automation Bias: How to Tackle a Wicked Problem of AI?" Big Data and Cognitive Computing 5, no. 2 (2021): 18. http://dx.doi.org/10.3390/bdcc5020018.

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The increasing use of AI in different societal contexts intensified the debate on risks, ethical problems and bias. Accordingly, promising research activities focus on debiasing to strengthen fairness, accountability and transparency in machine learning. There is, though, a tendency to fix societal and ethical issues with technical solutions that may cause additional, wicked problems. Alternative analytical approaches are thus needed to avoid this and to comprehend how societal and ethical issues occur in AI systems. Despite various forms of bias, ultimately, risks result from eventual rule conflicts between the AI system behavior due to feature complexity and user practices with limited options for scrutiny. Hence, although different forms of bias can occur, automation is their common ground. The paper highlights the role of automation and explains why deep automation bias (DAB) is a metarisk of AI. Based on former work it elaborates the main influencing factors and develops a heuristic model for assessing DAB-related risks in AI systems. This model aims at raising problem awareness and training on the sociotechnical risks resulting from AI-based automation and contributes to improving the general explicability of AI systems beyond technical issues.
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Eckroth, Joshua. "Evolution of a Robust AI System: A Case Study of AAAI’s AI-Alert." AI Magazine 41, no. 4 (2020): 17–38. http://dx.doi.org/10.1609/aimag.v41i4.5309.

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Since mid-2018, we have used a suite of artificial intelligence (AI) technologies to automatically generate the Association for the Advancement of Artificial Intelligence’s AI-Alert, a weekly email sent to all Association for the Advancement of Artificial Intelligence members and thousands of other subscribers. This alert contains ten news stories from around the web that focus on some aspect of AI, such as new AI inventions, AI’s use in various industries, and AI’s impacts in our daily lives. This alert was curated by-hand for a decade before we developed AI technology for automation, which we call “NewsFinder.” Recently, we redesigned this automation and ran a six-month experiment on user engagement to ensure the new approach was successful. This article documents our design considerations and requirements, our implementation (which involves web crawling, document classification, and a genetic algorithm for story selection), and our reflections after a year and a half since deploying this technology.
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Altavilla, S., and E. Blanco. "ARE AI TOOLS GOING TO BE THE NEW DESIGNERS? A TAXONOMY FOR MEASURING THE LEVEL OF AUTOMATION OF DESIGN ACTIVITIES." Proceedings of the Design Society: DESIGN Conference 1 (May 2020): 81–90. http://dx.doi.org/10.1017/dsd.2020.286.

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AbstractThe digitalisation of the industry offers new opportunities to discuss design activities and support tools. Advancement in AI allows thinking about new Designer-AI tools interaction in the design process. The paper aims to initiate a characterisation of tools issued from researches in the application of AI in Design to rethink the division of work between Designer-AI tools. The paper is based on the literature on the concept of Levels of Automation in cognitive engineering, manufacturing and robotics, and proposes a grid of characterisation of the Level of Automation for the design process.
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Dissertations / Theses on the topic "AI automation"

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Schinner, Charles Edward 1957. "Electronic manufacturing test cell automation and configuration using AI techniques." Thesis, The University of Arizona, 1990. http://hdl.handle.net/10150/278327.

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This thesis utilizes artificial intelligence techniques and problem specific knowledge to assist in the design of an manufacturing test cell for electronic products. The electronic printed circuit board (PCB) is subjected to one or more functional evaluation(s) during the manufacturing process. The purpose of these evaluations is to assure product quality. This thesis is focused on, with historical knowledge, the configuration of this testing environment and associated fault isolation processes. By using such knowledge, an improvement in the testing efficiency will be realized which will allow the overall product cost to be minimized.
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Packer, Jonathan M. B. A. Sloan School of Management. "The ethics of labor automation : AI, technological disenfranchisement, and libertarian paternalism." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122399.

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Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2019<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 77-83).<br>The capitalist economic landscape constantly reinvents itself to capture new value opportunities, in an endless innovative process of what Joseph Schumpeter called "creative destruction"'. Its next major event, hastened by the development of Artificial Intelligence (AI) and the emergence of highly accurate predictive behavioral data, is likely to assert itself in the labor economy. Labor costs often constitute the majority of a firm's operating expenses; indeed, the average wage share in highly developed nations currently hovers around 60% of GDP 2. Labor-automating technology is, for this reason, both anticipated and feared - the productivity enhancements of its adoption are likely to re-allocate trillions of dollars of revenue globally. Nevertheless, even in labor markets most susceptible to automation, Al is unlikely to completely replace any roles.<br>Instead, its application will happen selectively in a process of human-machine teaming: fragmenting a single workflow into automation-feasible functions executed by machine intelligence, and a remainder of technically infeasible functions performed by a human worker. This technology also promises to radically transform organizations, replacing limited, reactive, human managerial insights with behavioral analytics that anticipate and directly modify worker experience. This paper will explore the ethical and democratic tensions between disenfranchising automation, invasive behavioral analytics, and economic growth. It will survey the evolution of contemporary labor from early narratives, to the vanquished, neoclassical notion of rational homo economicus, the development of modern organization theory, and the organizational psychology of workflow automation.<br>Ultimately, it will consider the ethics of this transformation, its ramifications on corporate responsibility, and its threat to the worker and citizen's ever-shrinking creative franchise. The paper hypothesizes that the fragmentation of labor's creative discretion constitutes the total technological disenfranchisement of the worker, and its organizational automation ruptures the normal, reactive function of private, social, and democratic institutions. Through the lens of libertarian paternalism, or "choice architecture," it proposes a Third Way to correctively adapt to automation's inevitability. Drawing from this proposal and its survey of related theory, the paper suggests that former institutionalized labor norms have permanently deteriorated, and attempts to synthesize their replacement.<br>by Jonathan Packer.<br>M.B.A.<br>M.B.A. Massachusetts Institute of Technology, Sloan School of Management
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Swift, K. G. "A computer-based methodology for advising the designer regarding assembly automation." Thesis, University of Hull, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.377412.

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Panda, Rames C., and J. Kanagaraj. "Automation in Leather Making - 271." Verein für Gerberei-Chemie und -Technik e. V, 2019. https://slub.qucosa.de/id/qucosa%3A34327.

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Content: In most of the tanneries, water and chemicals are added manually in the tanning drum and pH of the float / leather is adjusted. Addition of correct amount of process recipe are necessary for better processing of the hides and minimizing wastage of utility, thereby controlling pollution load in effluent. However, fugitive-emission from process and drains accumulate ammonia, hydrogen-sulphide and volatile organic compounds which contribute bad odor in tannery as well as in wastewater-treatment premises causing problems to occupational health & safety of workers. Both local and supervisory control stations are employed to monitor and accurately manage the unit operations. The objective of this work is to produce consistent quality of leathers and to provide a healthy environment through automatic dosing and odor abatement system. Therefore the entire process control operation is integrated to operate through PLCs with following five modules: i) Water addition module ii) Chemical preparation and dosing system iii) pH monitoring and float recycle system iv) Drum rotation module v) Odor reduction module. In the integrated system, critical and bulk chemicals are stored in bulk storage tanks and are drawn into the load cell (LC) as per process sequence or recipe for feeding into the drums through auxiliary tanks. The float-recycle system helps to remix & heat the float where a pH electrode is housed to monitor pH online. The pH monitoring system adjusts addition of critical chemicals that indicates automatic end point. The contaminated air inside the tannery is sucked and passed through blower and then through bio-filter. The filtration process is based on the principle that VOCs (in the order of 50-200 ppm) and odors can be biologically treated by naturally occurring microbes. The control parameters monitored are: moisture in the bed and uniformity of media (contaminated air or process liquor). The humidity and temperature of inlet media is controlled and contact time with microbes is 10-30 secs. Moisture is controlled to maintain microbial population. A lead in laboratory scale has been developed to measure process variables (PV) considering their spatial distribution in two dimensions. Spatial distribution of process variables inside hides (across cross section) may provide accurate measurement of through reconstruction of image and data driven models using artificial intelligence tools. Computational intelligence is developed for updation of model parameters as that can be used for direct estimation of PV Take-Away: 1. Cleaner production is provided through automation of dosing & pH monitoring using PLC in indegeneous way 2. Pollution Load in exit stream and odor-gas emmision are minimized 3. Artificial Intelligence and data analytics techniques are used in Leather making
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Santiago, Dionny. "A Model-Based AI-Driven Test Generation System." FIU Digital Commons, 2018. https://digitalcommons.fiu.edu/etd/3878.

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Achieving high software quality today involves manual analysis, test planning, documentation of testing strategy and test cases, and development of automated test scripts to support regression testing. This thesis is motivated by the opportunity to bridge the gap between current test automation and true test automation by investigating learning-based solutions to software testing. We present an approach that combines a trainable web component classifier, a test case description language, and a trainable test generation and execution system that can learn to generate new test cases. Training data was collected and hand-labeled across 7 systems, 95 web pages, and 17,360 elements. A total of 250 test flows were also manually hand-crafted for training purposes. Various machine learning algorithms were evaluated. Results showed that Random Forest classifiers performed well on several web component classification problems. In addition, Long Short-Term Memory neural networks were able to model and generate new valid test flows.
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Klinga, Peter. "Transforming Corporate Learning using Automation and Artificial Intelligence : An exploratory case study for adopting automation and AI within Corporate Learning at financial services companies." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279570.

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As the emergence of new technologies are continuously disrupting the way in which organizations function and develop, the majority of initiatives within Learning and Development (L&amp;D) are far from fully effective. The purpose of this study was to conduct an exploratory case study to investigate how automation and AI technologies could improve corporate learning within financial services companies. The study was delimited to study three case companies, all primarily operating in the Nordic financial services industry. The exploratory research was carried out through a literature review, several indepth interviews as well as a survey for a selected number of research participants. The research revealed that the current state of training within financial services is characterized by a significant amount of manual and administrative work, lack of intelligence within decision-making as well as a non-existing consideration of employee knowledge. Moreover, the empirical evidence similarly reveled a wide array of opportunities for adopting automation and AI technologies into the respective learning workflows of the L&amp;D organization within the case companies.<br>I takt med att företag kontinuerligt anammar nya teknologier för att förbättra sin verksamhet, befinner sig utbildningsorganisationer i ett märkbart ineffektivt stadie. Syftet med denna studie var att genomföra en explorativ fallstudie gällande hur finansbolag skulle kunna införa AI samt automatisering för att förbättra sin utbildningsorganisation. Studien var begränsat till att undersöka tre företag, alla med verksamhet i den nordiska finansbranschen. Den explorativa delen av studien genomfördes med hjälp av en litteraturstudie, flertal djupgående intervjuer samt en enkät för ett begränsat antal deltagare i forskningsprocessen. Forskning påvisade att den existerade utbildningsorganisationen inom finansbolag är starkt präglat av ett överflöd av manuellt och administrativt arbete, bristande intelligens inom beslutsprocesser samt en bristande hänsyn för existerande kunskapsnivåer bland anställda. Studien påvisade därtill en mängd möjligheter att införa automatisering samt AI för att förbättra utbildningsflödena inom samtliga deltagande bolag i fallstudien.
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Rozic, Luka, and Damon Salehi. "Uppfattning av AI i vår tid : En studie om de bakomliggande faktorerna till människors uppfattning kring AI och hur uppfattningen har förändrats över tid." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-44470.

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I det här arbetet tittar vi på artificiell intelligens genom sitt ursprung och hur tekniken har utvecklats över tiden. Eftersom begreppet diskuteras flitigt i dagens samhälle och av människors förståelse av det, vill vi klargöra vad människor faktiskt tror att artificiell intelligens är och jämföra det med dess faktiska definitioner. Eftersom artificiell intelligens har många definitioner är vårt syfte att redogöra för dessa definitioner utförligt och gå igenom AI:s historia och hur människor har uppfattat termen i generationer. Målet är att framföra en presentation av en jämförelse med hur människor uppfattar artificiell intelligens, vad uppfattningen grundar sig på i förhållande till AI:s faktiska definitioner, hur dess perspektiv och kunskap har förändrats i allmänhet - associerat med förändringar som händer i vårt samhälle. För att göra denna jämförelse var vi tvungna att genomföra en semistrukturerad intervju med informanter om olika demografi och åldrar för att få en nyanserad bild av ”vanliga” människors uppfattning om AI. Olika teorier som definitionerna av AI, automatisering och robotik användes också för att jämföra hur våra informanter beskrev de olika termerna.De presenterade resultaten visade att i princip alla våra informanter hade väldigt lite kunskap om AI baserat på vår insamlade data. Automatisering och robotik talades ofta om, och det verkade som om dessa termer är allt som människor associerade AI med. Likheter och skillnader i uppfattningar om AI genom tiden uppmärksammades, vilket sträcker sig till uppfattningar om etiskt resonemang, hälsovård, robotik m.m.<br>In this assignment, we look at artificial intelligence through its origin and how the technology has developed over time. Since the term is highly discussed in today's society and by people's understanding of it, we want to clarify what people believe artificial intelligence is and compare it to its true definitions. Since artificial intelligence has many definitions, our purpose is to account for these definitions thoroughly and go through the history of AI and how people have perceived the term through various timelines. The goal is to accredit a presentation of a comparison with how people perceive artificial intelligence, what the acknowledgement is based on in relation to how AI:s true definitions, how its perspectives and knowledge has changed in general - associated with changes happening in our society. In order to make this comparison, we had to perform a semi-structured interview with informants of various demographics and ages to get a nuanced image of “ordinary” people’s perception of AI. Different theories such as the definitions of AI, automatization and robotics were also used to compare how our informants described the different terms. The results presented showed that basically all of our informants had arguably little knowledge of AI based on our collected data. Automation and robotics were frequently spoken about, and it seemed as if these terms are all that people associated AI with. Similarities and differences in perceptions of AI through time were acknowledged, which extends to perceptions of ethical reasoning, health care, robotics etc.
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Chiloiro, Giuseppe Marco. "Virtual coach for super sports cars: a possible AI application." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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The dream of the human being has always been to reach the perfection. This is true for life in general, but also for sports competitions including motorsport. In car racing, the optimum is represented by the perfect lap, performed by the driver in the minimum possible time, following the ideal race line with the appropriate velocity. However, due to the intrinsic nature of human being this is unfeasible and the only way to get as close as possible to the perfection is to rely on technology and artificial intelligence. Focusing on motorsport, the actual knowledge of vehicle dynamics and the development of optimization techniques can help in reaching great improvements in terms of race performance. This is exactly the objective of this thesis in which the implementation of a virtual coach for super sports cars has been carried out, in order to provide useful suggestions to the driver during and after the performance. In particular, the algorithm on which the virtual coach is based, computes the ideal race line and the optimal velocity in every point, giving also an accurate estimation of the best reachable lap time. The virtual coach is able to adapt to each super sports car and circuit, since the vehicle and racetrack models are generated starting from the physical parameters introduced by the user as inputs to the algorithm. The optimization of the race line is performed by solving the minimum lap time problem through the optimal control theory and the optimal velocity is computed for each point of the ideal trajectory. After the first lap, the algorithm associates the driver to a category level and shows which are the sectors of the circuit where a higher improvement is possible, adapting the targets in terms of lap time and velocity to the driver’s capabilities and allowing a step by step improving of the performances. The algorithm has been tested on a large variety of vehicles and circuits, providing accurate results in terms of ideal race lines and velocities.
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Fält, Felix, and Reuterstrand Adrian Torres. "AI och partiskhet vid beslutsfattande i rekryteringsprocesser : Hur artificiell intelligens kan hantera partiskhet i rekryteringsprocessen." Thesis, Linköpings universitet, Informationssystem och digitalisering, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177501.

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Dagens rekryteringsprocessen drivs oftast av en mänsklig rekryterare, men på senare tid har det blivit allt mer populärt att använda sig av AI verktyg för att effektivisera delar av processen, men också för att motverka den inneboende partiskhet som finns hos människor. Detta arbete syftar till att analysera hur rekryterare med erfarenheter kring användning av AI, och utvecklare av sådana system, upplever huruvida AI kan användas som ett verktyg för att möta partiskhet inom rekryteringsprocessen. Genom att genomföra semistrukturerade intervjuer med intressenter som är insatta i både rekryteringsprocessen, men också i utveckling av AI-system med rekrytering som huvudfokus, har vi fått insyn kring hur verksamheter jobbar och utvecklar dessa typer av system. Hur de förhåller sig till etiska frågor har varit avgörande för att kunna utvärdera om AI är lämpligt för denna uppgift. Vår slutsats visar att det finns användningsområden för AI i rekryteringen, men istället som ett komplementerande verktyg för den mänskliga rekryteraren snarare än en ersättare, som AI ofta spekuleras att vara. Fördelar som vi såg var bland annat att AI kan behandla fler kandidater än dess mänskliga motpart och i de flesta fall ta beslut som är kompetensbaserade eftersom AI:n inte påverkas av externa faktorer på samma vis som vi människor gör. Även om AI har sina brister, där den kan efterbilda negativa beteenedemönster från oss människor och att den mänskliga kontakten reduceras, så fann vi att de positiva aspekterna var övervägande, och att det finns en optimistisk inställning kring vidare studier inom området.<br>The recruitment process of today is often driven by human recruiters and lately it has become increasingly popular to use AI-driven tools to streamline parts of this process, but also to try to counteract the inherent bias present in humans. This study aims to analyze how recruiters with experience in the use of AI, and developers of such systems, experience whether AI can be used as a tool to meet bias in the recruitment process. By performing semistructured interviews with relevant parties familiar with the recruitment process, but also with the development of AI-systems with recruitment as the main focus, we have gained insight into how companies work and how they develop these types of systems. How they relate to ethical issues has been useful in being able to evaluate whether AI is appropriate for this task. Our conclusion shows that there are uses for AI in recruitment, but instead as a complementary tool for the human recruiter rather than as a replacement, as AI is often speculated to be. Advantages that we saw included that AI can treat more candidates than its human counterpart and in most cases make decisions that are competency based because AI is not affected by external factors in the same way as we humans do. Although AI has its flaws, where it can mimic negative behavioural patterns from us humans and that human contact is reduced, we found that the positive aspects of AI were predominant, and that there is an optimistic attitude towards further studies in the field.
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Anzén, Elizabeth, and Lukas Ekberg. "Understanding how automatized personalization with AI can drive value in B2B marketing : A case study of a Swedish industrial equipment manufacturer." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279668.

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In the last decade, marketing automation, a tool for automatic personalization, has been gaining significant traction among marketing professionals. In parallel with the growing adoption trend, many marketing automation platform providers have been extending their offers to include AI features. However, there is a lack of research regarding how AI can enhance the process of marketing automation in a way that creates value, which is the studied topic in this thesis. A qualitative and exploratory case study has been conducted in collaboration with the global B2B company Atlas Copco, a manufacturer of industrial equipment. Digital marketing practitioners were presented with two use cases of AI, segmentation and cross-selling, for personalization and asked about the marketing automation process and the expected impact on value. The findings reveal what would be required in the marketing automation process for the use cases in terms of data needs, learning about customer insights, marketing output and evaluation. In our findings value creation strongly revolve around the value types: ‘excellence’, ‘efficiency’ and ‘privacy’. To conclude, AI will enable more advanced personalization and value creation can be substantial if customer sacrifices are addressed in an appropriate way. Depending on relevance, tone of voice, time and use of channel, different feelings of value are perceived, which are factors that AI can help to determine<br>Under det senaste årtiondet har verktyg för automatisk marknadsföring blivit populära bland marknadsförare. Automatiska marknadsföringsplattformar fungerar som ett verktyg för att automtiskt leverera personaliserade marknadsföring. Många leverantörer av automatiska marknadsföringsplattformar har utökat sina erbjudanden till att innefatta AI-tjänster. Den befintliga forskningen kring hur sådana AItjänster ska utnyttjas på ett sätt som skapar värde är begränsad och därav behandlas ämnet i den här uppsatsen. En explorativ och kvalitativ fallstudie har genomförts i samarbete med Atlas Copco som är ett globalt b2b-företag. Vid varje intervju presenterades antingen merförsäljning eller kundsegmentering sedan ställdes frågor om den automatiska marknadsföringsprocess och värde. Resultaten indikerar vad som skulle krävas för de undersökta användningsfallen i den automatiska marknadsföringsprocessen samt att värdeskapande är starkt kopplat till värdetyperna excellens, effektivitet, privatliv och datasäkerhet. Slutsatserna indikerar att AI kommer göra den personalisering som uppstår till följd av automatisk marknadsföring mer avancerad. Värdeskapandet från nya AI lösningar kan vara signifikant om implementeringen tar hänsyn och adresserar uppoffringar kunder behöver göra.
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Books on the topic "AI automation"

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Salzman, Roy M. AI in IA: Artificial intelligence in industrial automation. A.D. Little Decision Resources, 1985.

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IASTED International Symposium: Applied Informatics (1985 Grindelwald, Switzerland). Applied informatics, AI '85. Edited by Hamza M. H and International Association of Science and Technology for Development. Acta Press, 1985.

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Stock, Michael. AI in process control. Intertext Publications, 1989.

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AI in process control. Intertext Publications, 1989.

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Conference on AI, Simulation and Planning in High Autonomy Systems (3rd 1992 Perth, W.A.). Proceedings of the Third Annual Conference of AI, Simulation and Planning in High Autonomy Systems: Theme, integrating perception, planning and action, Perth, Australia, July 8-10, 1992. IEEE Computer Society Press, 1992.

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Zhaoxia, Guo, and Leung Yung-sun, eds. Optimizing decision making in the apparel supply chain using artificial intelligence (AI): From production to retail. Woodhead Publishing Ltd, 2013.

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Conference on AI, Simulation and Planning in High Autonomy Systems. (4th 1993 Tucson, Ariz.). Proceedings, Fourth Annual Conference: AI, Simulation and Planning in High Autonomy Systems: Theme, integrating virtual reality and model-based environments : Tucson, Arizona, September 20-22, 1993. IEEE Computer Society Press, 1992.

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Farreny, Henri. AI and expertise: Heuristic search, inference engines, automatic proving. E. Horwood, 1989.

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Ugo, Pagallo, Sartor Giovanni, Ajani Gianmaria, and SpringerLink (Online service), eds. AI Approaches to the Complexity of Legal Systems. Complex Systems, the Semantic Web, Ontologies, Argumentation, and Dialogue: International Workshops AICOL-I/IVR-XXIV Beijing, China, September19, 2009 and AICOL-II/JURIX 2009, Rotterdam,The Netherlands, December 16, 2009 Revised Selected Papers. Springer Berlin Heidelberg, 2010.

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Ruqian, Lu. Automatic generation of computer animation: Using AI for movie animation. Springer, 2002.

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Book chapters on the topic "AI automation"

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Johannessen, Jon-Arild, and Helene Sætersdal. "AI network." In Automation, Innovation and Work. Routledge, 2020. http://dx.doi.org/10.4324/9781003032854-5.

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Lažanský, J., and V. Mařík. "AI for manufacturing: Some practical experience." In Balanced Automation Systems. Springer US, 1995. http://dx.doi.org/10.1007/978-0-387-34910-7_14.

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Mohanty, Soumendra, and Sachin Vyas. "Intelligent Process Automation = RPA + AI." In How to Compete in the Age of Artificial Intelligence. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3808-0_5.

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Ng, Cory, and John Alarcon. "Robotic Process Automation (RPA) and AI." In Artificial Intelligence in Accounting. Routledge, 2020. http://dx.doi.org/10.4324/9781003003342-3.

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Vallor, Shannon. "AI and the Automation of Wisdom." In Philosophical Studies Series. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61043-6_8.

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Magnusson Sjöberg, Cecilia. "Legal Automation: AI and Law Revisited." In Legal Tech, Smart Contracts and Blockchain. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6086-2_7.

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Wang, Yong, Qiang Yang, and Zhong Zhang. "Real-Time Scheduling for Multi-agent Call Center Automation." In Recent Advances in AI Planning. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/10720246_15.

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Hessenkämper, Axel, Falk Howar, and Andreas Rausch. "Digital Transformation Trends: Industry 4.0, Automation, and AI." In Lecture Notes in Computer Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03427-6_34.

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Darshana, Subhashree, Siddharth Swarup Rautaray, and Manjusha Pandey. "AI to Machine Learning: Lifeless Automation and Issues." In Studies in Big Data. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6518-6_7.

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Manchanda, Sanjeev. "Automation of Bid Proposal Preparation Through AI Smart Assistant." In Data Management, Analytics and Innovation. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2934-1_3.

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Conference papers on the topic "AI automation"

1

Liang, A. H. "Biological Optical AI and Bionic Optical AI." In 2020 5th International Conference on Robotics and Automation Engineering (ICRAE). IEEE, 2020. http://dx.doi.org/10.1109/icrae50850.2020.9310880.

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Dessouky, Ghada, Patrick Jauernig, Nele Mentens, Ahmad-Reza Sadeghi, and Emmanuel Stapf. "INVITED: AI Utopia or Dystopia - On Securing AI Platforms." In 2020 57th ACM/IEEE Design Automation Conference (DAC). IEEE, 2020. http://dx.doi.org/10.1109/dac18072.2020.9218490.

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Fransson, Claude, Najah Naffah, Gordon Stokes, and George White. "AI in office automation (panel session)." In the 1986 ACM fourteenth annual conference, edited by Bipin C. Desai. ACM Press, 1986. http://dx.doi.org/10.1145/324634.325461.

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Hossain, Alamgir. "AI for innovation, transformation and automation." In 2017 20th International Conference of Computer and Information Technology (ICCIT). IEEE, 2017. http://dx.doi.org/10.1109/iccitechn.2017.8281863.

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Zhou, Congshu, Zheng Zou, Fang Li, et al. "AI Solution for Metrology Recipe Automation." In 2021 32nd Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC). IEEE, 2021. http://dx.doi.org/10.1109/asmc51741.2021.9435689.

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Francis, Anagha, and Yogesh P. Pingle. "Automation in Baking using AI and IoT." In 2020 7th International Conference on Computing for Sustainable Global Development (INDIACom). IEEE, 2020. http://dx.doi.org/10.23919/indiacom49435.2020.9083695.

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Diamanti, Alessio, Jose Manuel Sanchez Vilchez, and Stefano Secci. "The SYRROCA AI-empowered network automation platform." In 2021 24th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN). IEEE, 2021. http://dx.doi.org/10.1109/icin51074.2021.9385535.

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Chen, Tai-Chen, Pei-Yu Lee, and Tung-Chieh Chen. "Automatic Floorplanning for AI SoCs." In 2020 International Symposium on VLSI Design, Automation and Test (VLSI-DAT). IEEE, 2020. http://dx.doi.org/10.1109/vlsi-dat49148.2020.9196464.

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Yin, Jianxiong. "Scalable AI Computing Lifecycle." In 2019 International Symposium on VLSI Design, Automation and Test (VLSI-DAT). IEEE, 2019. http://dx.doi.org/10.1109/vlsi-dat.2019.8741600.

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Ricca, Filippo, Alessandro Marchetto, and Andrea Stocco. "AI-based Test Automation: A Grey Literature Analysis." In 2021 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW). IEEE, 2021. http://dx.doi.org/10.1109/icstw52544.2021.00051.

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Reports on the topic "AI automation"

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Ball, Kenneth, Jay Hineman, Sam Voisin, Gary Koplik, and Paul Bendich. AUTOMATION IS ALL YOU NEED: FASTER EARTH SYSTEMS MODELS WITH AI/ML. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1769679.

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Hardie, Tom, Tim Horton, and Will Warburton. Switched on: How do we get the best out of automation and AI in health care? The Health Foundation, 2021. http://dx.doi.org/10.37829/hf-2021-i03.

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Buchanan, Ben, Andrew Lohn, Micah Musser, and Katerina Sedova. Truth, Lies, and Automation: How Language Models Could Change Disinformation. Center for Security and Emerging Technology, 2021. http://dx.doi.org/10.51593/2021ca003.

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Abstract:
Growing popular and industry interest in high-performing natural language generation models has led to concerns that such models could be used to generate automated disinformation at scale. This report examines the capabilities of GPT-3--a cutting-edge AI system that writes text--to analyze its potential misuse for disinformation. A model like GPT-3 may be able to help disinformation actors substantially reduce the work necessary to write disinformation while expanding its reach and potentially also its effectiveness.
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Fang, Chen. Unsettled Issues in Vehicle Autonomy, Artificial Intelligence, and Human-Machine Interaction. SAE International, 2021. http://dx.doi.org/10.4271/epr2021010.

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Artificial intelligence (AI)-based solutions are slowly making their way into our daily lives, integrating with our processes to enhance our lifestyles. This is major a technological component regarding the development of autonomous vehicles (AVs). However, as of today, no existing, consumer ready AV design has reached SAE Level 5 automation or fully integrates with the driver. Unsettled Issues in Vehicle Autonomy, AI and Human-Machine Interaction discusses vital issues related to AV interface design, diving into speech interaction, emotion detection and regulation, and driver trust. For each of these aspects, the report presents the current state of research and development, challenges, and solutions worth exploring.
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Corcuera, R., C. Ford, S. Ball, and R. Perez. Designing an AI-environment prototype for the automatic startup of EBR-II (Experimental Breeder Reactor-II). Office of Scientific and Technical Information (OSTI), 1990. http://dx.doi.org/10.2172/7176889.

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