Hasil untuk "Automation"

T. Sheridan

P. Leitão, A. Colombo, S. Karnouskos

Mohd Bahrin, M. Othman, N. Azli et al.

Julia Kokina, T. Davenport

David H. Autor, A. Salomons

Is automation a labor-displacing force? This possibility is both an age-old concern and at the heart of a new theoretical literature considering how labor immiseration may result from a wave of ‘brilliant machines,’ which is in part motivated by declining labor shares in many developed countries. Comprehensive evidence on this labor-displacing channel is at present limited. Using the recent model of Acemoglu and Restrepo (2018b) as an analytical frame, we first outline the various channels through which automation impacts labor ́s share of output. We then turn to empirically estimating the employment and labor share impacts of productivity growth—an omnibus measure of technological change—using data on 28 industries for 18 OECD countries since 1970. Our main findings are that although automation—whether measured by Total Factor Productivity growth or instrumented by foreign patent flows or robot adoption—has not been employment-displacing, it has reduced labor’s share in value-added. We disentangle the channels through which these impacts occur, including: own-industry effects, cross-industry input-output linkages, and final demand effects accruing through the contribution of each industry’s productivity growth to aggregate incomes. Our estimates indicate that the labor share-displacing effects of productivity growth, which were essentially absent in the 1970s, have become more pronounced over time, and are most substantial in the 2000s. This finding is consistent with automation having become in recent decades less labor-augmenting and more labor-displacing. 1 Autor: MIT Department of Economics, Cambridge, MA 02142 (dautor@mit.edu); Salomons: Utrecht University School of Economics, 3584 EC Utrecht, The Netherlands; & Technology & Policy Research Initiative, Boston University (a.m.salomons@uu.nl). Paper prepared for the Brookings Papers on Economic Activity conference, March 2018. We thank Daron Acemoglu, Uwe Blien, Janice Eberly, Maarten Goos, John Haltiwanger, Richard Rogerson, James Stock, and Xianjia Ye for valuable input that improved the paper. We thank Daron Acemoglu, Georg Graetz, Guy Michaels, and Pascual Restrepo for sharing harmonized code on penetration of industrial robotics. And we think Pian Shu for sharing data on approved patent applications by industry and year of filing. Autor acknowledges funding from IBM Higher Education, Schmidt Sciences, and the Smith-Richardson Foundation. Salomons acknowledges funding from the Netherlands Organisation for Scientific Research.

Moritz Körber, E. Baseler, K. Bengler

Moritz Körber

S. Höfer, Kostas E. Bekris, Ankur Handa et al.

D. Acemoglu, P. Restrepo

We present a task-based model in which high- and low-skill workers compete against machines in the production of tasks. Low-skill (high-skill) automation corresponds to tasks performed by low-skill (high-skill) labor being taken over by capital. Automation displaces the type of labor it directly affects, depressing its wage. Through ripple effects, automation also affects the real wage of other workers. Counteracting these forces, automation creates a positive productivity effect, pushing up the price of all factors. Because capital adjusts to keep the interest rate constant, the productivity effect dominates in the long run. Finally, low-skill (high-skill) automation increases (reduces) wage inequality.

Brayden Hamilton, Tim Cashmore, Peter Driscoll et al.

Marine biofouling on vessel hulls poses major ecological, economic, and biosecurity risks. Traditional survey methods rely on diver inspections, which are hazardous and limited in scalability. This work investigates automated classification of biofouling severity on the Level of Fouling (LoF) scale using both custom computer vision models and large multimodal language models (LLMs). Convolutional neural networks, transformer-based segmentation, and zero-shot LLMs were evaluated on an expert-labelled dataset from the New Zealand Ministry for Primary Industries. Computer vision models showed high accuracy at extreme LoF categories but struggled with intermediate levels due to dataset imbalance and image framing. LLMs, guided by structured prompts and retrieval, achieved competitive performance without training and provided interpretable outputs. The results demonstrate complementary strengths across approaches and suggest that hybrid methods integrating segmentation coverage with LLM reasoning offer a promising pathway toward scalable and interpretable biofouling assessment.

Nipun D. Nath, A. Behzadan, S. Paal

Feiqi Huang, M. Vasarhelyi

Robotic process automation (RPA) has been widely adopted in many industries, including the accounting industry, to automate well-defined and repetitive tasks; however, its application to auditing has lagged behind because of the unique nature of this industry. This study applies RPA in the auditing area. An RPA framework is proposed that frees auditors from doing repetitive and low-judgment audit tasks and enables them to focus on tasks that require professional judgment. This paper also demonstrates the feasibility of RPA by implementing a pilot project that applies RPA to the confirmation process.

J. Lowenberg‐DeBoer, I. Y. Huang, Vasileios Grigoriadis et al.

This study reviewed research published after 1990 on the economics of agricultural mechatronic automation and robotics, and identified research gaps. A systematic search was conducted from the following databases: ScienceDirect, Business Source Complete, Wiley, Emerald, CAB Abstract, Greenfile, Food Science Source and AgEcon Search. This identified 4817 documents. The screening of abstracts narrowed the range to a dataset of 119 full text documents. After eligibility assessment, 18 studies were subjected to a qualitative analysis, with ten focused on automation of specific horticultural operations and eight related to autonomous agricultural equipment. All of the studies found some scenarios in which automation and robotic technologies were profitable. Most studies employed partial budgeting considering only costs and revenues directly changed by the introduction of automation or robotics and assuming everything else constant. None examined cropping system changes, or regional and national impacts on markets, trade and labour demand. The review identified a need for in-depth research on the economic implications of the technology. Most of the studies reviewed estimated economic implications assuming that technology design parameters were achieved and/or based on data from prototypes. Data are needed on the benefits and problems with using automation and robotics on farm. All of the studies reviewed were in the context of agriculture in developed countries, but many of the world’s most pressing agricultural problems are in the developing world. Economic and social research is needed to understand those developing country problems, and guide the engineers and scientists creating automation and robotic solutions.

Lauren A. Cooper, D. K. Holderness, Trevor L. Sorensen et al.

We investigate the implementation of Robotic Process Automation (RPA) software in public accounting by interviewing RPA leaders at Big 4 firms. RPA software automates the input, processing, and output of data to streamline repetitive, mundane tasks. Many of our findings are unique to accounting. For instance, participants report tax services are furthest along in RPA adoption, followed by advisory and assurance services. Furthermore, RPA has not impacted fees, but there is concern that clients may desire fee reductions due to decreased employee hours. Finally, unlike other technology implementations, RPA adoption is driven primarily by lower-level employees. Similar to other domains, our results indicate massive efficiency and effectiveness gains from RPA implementation. Also, interviewees do not expect reduced head count to result from RPA use. This study is the first to discuss the benefits, opportunities, and challenges to implementing RPA in accounting and serves as a catalyst for future research.

I. Holland, J. Davies

Protocols in the academic life science laboratory are heavily reliant on the manual manipulation of tools, reagents and instruments by a host of research staff and students. In contrast to industrial and clinical laboratory environments, the usage of automation to augment or replace manual tasks is limited. Causes of this ‘automation gap’ are unique to academic research, with rigid short-term funding structures, high levels of protocol variability and a benevolent culture of investment in people over equipment. Automation, however, can bestow multiple benefits through improvements in reproducibility, researcher efficiency, clinical translation, and safety. Less immediately obvious are the accompanying limitations, including obsolescence and an inhibitory effect on the freedom to innovate. Growing the range of automation options suitable for research laboratories will require more flexible, modular and cheaper designs. Academic and commercial developers of automation will increasingly need to design with an environmental awareness and an understanding that large high-tech robotic solutions may not be appropriate for laboratories with constrained financial and spatial resources. To fully exploit the potential of laboratory automation, future generations of scientists will require both engineering and biology skills. Automation in the research laboratory is likely to be an increasingly critical component of future research programs and will continue the trend of combining engineering and science expertise together to answer novel research questions.

Angelos Alexopoulos, Agorakis Bompotas, Nikitas Rigas Kalogeropoulos et al.

Modern manufacturing demands high flexibility and reconfigurability to adapt to dynamic production needs. Model-based Engineering (MBE) supports rapid production line design, but final reconfiguration requires simulations and validation. Digital Twins (DTs) streamline this process by enabling real-time monitoring, simulation, and reconfiguration. This paper presents a novel platform that automates DT generation and deployment using AutomationML-based factory plans. The platform closes the loop with a GAI-powered simulation scenario generator and automatic physical line reconfiguration, enhancing efficiency and adaptability in manufacturing.

Aman Singh, Deepak Kapa, Suryank Joshi et al.

The optimal design of robotic actuators is a critical area of research, yet limited attention has been given to optimizing gearbox parameters and automating actuator CAD. This paper introduces COMPAct: Computational Optimization and Automated Modular Design of Planetary Actuators, a framework that systematically identifies optimal gearbox parameters for a given motor across four gearbox types, single-stage planetary gearbox (SSPG), compound planetary gearbox (CPG), Wolfrom planetary gearbox (WPG), and double-stage planetary gearbox (DSPG). The framework minimizes mass and actuator width while maximizing efficiency, and further automates actuator CAD generation to enable direct 3D printing without manual redesign. Using this framework, optimal gearbox designs are explored across a wide range of gear ratios, providing insights into the suitability of different gearbox types while automatically generating CAD models for all four gearbox types with varying gear ratios and motors. Two actuator types are fabricated and experimentally evaluated through power efficiency, no-load backlash, and transmission stiffness tests. Experimental results indicate that the SSPG actuator achieves a mechanical efficiency of 60-80%, a no-load backlash of 0.59 deg, and a transmission stiffness of 242.7 Nm/rad, while the CPG actuator demonstrates 60% efficiency, 2.6 deg backlash, and a stiffness of 201.6 Nm/rad. CODE: https://github.com/singhaman1750/COMPAct.git VIDEO: https://youtu.be/etK6anjXag8?si=jFK7HgAPSBy-GnDR

Janneke Pieters, Ana Kujundzic, Rulof Burger et al.

Technological change can have profound impacts on the labor market. Decades of research have made it clear that technological change produces winners and losers. Machines can replace some types of work that humans do, while new technologies increase human's productivity in other types of work. For a long time, highly educated workers benefitted from increased demand for their labor due to skill-biased technological change, while the losers were concentrated at the bottom of the wage distribution (Katz and Autor, 1999; Goldin and Katz, 2007, 2010; Kijima, 2006). Currently, however, labor markets seem to be affected by a different type of technological change, the so-called routine-biased technological change (RBTC). This chapter studies the risk of automation in developing country labor markets, with a particular focus on differences between men and women. Given the pervasiveness of gender occupational segregation, there may be important gender differences in the risk of automation. Understanding these differences is important to ensure progress towards equitable development and gender inclusion in the face of new technological advances. Our objective is to describe the gender gap in the routine task intensity of jobs in developing countries and to explore the role of occupational segregation and several worker characteristics in accounting for the gender gap.

Miriam Ugarte, Pablo Valle, José Antonio Parejo et al.

Large Language Models (LLMs) have recently gained attention due to their ability to understand and generate sophisticated human-like content. However, ensuring their safety is paramount as they might provide harmful and unsafe responses. Existing LLM testing frameworks address various safety-related concerns (e.g., drugs, terrorism, animal abuse) but often face challenges due to unbalanced and obsolete datasets. In this paper, we present ASTRAL, a tool that automates the generation and execution of test cases (i.e., prompts) for testing the safety of LLMs. First, we introduce a novel black-box coverage criterion to generate balanced and diverse unsafe test inputs across a diverse set of safety categories as well as linguistic writing characteristics (i.e., different style and persuasive writing techniques). Second, we propose an LLM-based approach that leverages Retrieval Augmented Generation (RAG), few-shot prompting strategies and web browsing to generate up-to-date test inputs. Lastly, similar to current LLM test automation techniques, we leverage LLMs as test oracles to distinguish between safe and unsafe test outputs, allowing a fully automated testing approach. We conduct an extensive evaluation on well-known LLMs, revealing the following key findings: i) GPT3.5 outperforms other LLMs when acting as the test oracle, accurately detecting unsafe responses, and even surpassing more recent LLMs (e.g., GPT-4), as well as LLMs that are specifically tailored to detect unsafe LLM outputs (e.g., LlamaGuard); ii) the results confirm that our approach can uncover nearly twice as many unsafe LLM behaviors with the same number of test inputs compared to currently used static datasets; and iii) our black-box coverage criterion combined with web browsing can effectively guide the LLM on generating up-to-date unsafe test inputs, significantly increasing the number of unsafe LLM behaviors.