Figure 2 - uploaded by Enrique Fernandez Macias
Content may be subject to copyright.
Cattell-Horn-Carroll's three stratum model. The broad abilities are Crystallised Intelligence (Gc), Fluid Intelligence (Gf), Quantitative Reasoning (Gq), Reading and Writing Ability (Grw), Short-Term Memory (Gsm), LongTerm Storage and Retrieval (Glr), Visual Processing (Gv), Auditory Processing (Ga), Processing Speed (Gs) and Decision/Reaction Time/Speed (Gt).

Cattell-Horn-Carroll's three stratum model. The broad abilities are Crystallised Intelligence (Gc), Fluid Intelligence (Gf), Quantitative Reasoning (Gq), Reading and Writing Ability (Grw), Short-Term Memory (Gsm), LongTerm Storage and Retrieval (Glr), Visual Processing (Gv), Auditory Processing (Ga), Processing Speed (Gs) and Decision/Reaction Time/Speed (Gt).

Source publication
Article
Full-text available
In this paper we develop a framework for analysing the impact of AI on occupations. Leaving aside the debates on robotisation, digitalisation and online platforms as well as workplace automation, we focus on the occupational impact of AI that is driven by rapid progress in machine learning. In our framework we map 59 generic tasks from several work...

Context in source publication

Context 1
... the intermediate level, we aim at a number and breadth similar to the "broad abilities" of the CattellHorn-Carroll hierarchical model (see Figure 2) (Carroll et al., 1993). However, some of them are very anthropocentric and are not really categorical, but orthogonal (such as processing speed or the distinction between short-term and long-term memory). ...

Similar publications

Article
Full-text available
Non-economic damages (pain and suffering) are the most significant and variable components of liability. Our survey of 51 U.S. jurisdictions shows wide heterogeneity in whether attorneys may quantify damages as time-units of suffering (“per diem”) or demand a specific amount (“lump sum”). Either sort of large number could exploit an irrational anch...

Citations

... e digital reproduction of three primary cognitive abilities: training, thinking, and self-correction, is known as artificial intelligence (AI). Digital learning is a set of principles applied as a predictive algorithm that transforms realworld historical data into useful information [30]. e purpose of digital reasoning is to select the best rules for achieving a specific goal. ...
Full-text available
Article
In recent years, the Internet of Things (IoT) has been industrializing in various real-world applications, including smart industry and smart grids, to make human existence more reliable. An overwhelming volume of sensing data is produced from numerous sensor devices as the Industrial IoT (IIoT) becomes more industrialized. Artificial Intelligence (AI) plays a vital part in big data analyses as a powerful analytic tool that provides flexible and reliable information insights in real-time. However, there are some difficulties in designing and developing a useful big data analysis tool using machine learning, such as a centralized approach, security, privacy, resource limitations, and a lack of sufficient training data. On the other hand, Blockchain promotes a decentralized architecture for IIoT applications. It encourages the secure data exchange and resources among the various nodes of the IoT network, removing centralized control and overcoming the industry’s current challenges. Our proposed approach goal is to design and implement a consensus mechanism that incorporates Blockchain and AI to allow successful big data analysis. This work presents an improved Delegated Proof of Stake (DPoS) algorithm-based IIoT network that combines Blockchain and AI for real-time data transmission. To accelerate IIoT block generation, nodes use an improved DPoS to reach a consensus for selecting delegates and store block information in the trading node. The proposed approach is evaluated regarding energy consumption and transaction efficiency compared with the exciting consensus mechanism. The evaluation results reveal that the proposed consensus algorithm reduces energy consumption and addresses current security issues.
... Introducing complex features in probabilistic contextfree grammars, Tolan et al. [19] stipulated that all rule preterms have only two nodes, that is, the rules in the binary form. Each nonterminal node is represented by a set of "attribute-value" pairs, allowing the representation of lexical unification relationships, as well as long-range dependencies. ...
Full-text available
Article
The translation recognition of English long and short sentence information is an important issue to obtain the focus and core of English articles. Based on the deep GLR model, this paper constructs a method framework for English long and short sentence translation and recognition, using different embedding layer parameter initialization methods and using multi-layer computing methods in the sentence decoder. The initial corpus text is segmented and tagged with part-of-speech, then, the part-of-speech tag is appropriately corrected to reduce ambiguity, and then it is manually syntactically tagged. In the simulation process, the English long and short sentence summary and translation components are designed and developed, which can accurately and efficiently obtain the key information of English long and short sentences. The experimental results show that the English long and short sentence translation and recognition method of the deep GLR model improves the accuracy of the model parameters. In terms of model structure, the deep GLR value can be improved by 70.77% by reproducing the multi-layer representation fusion of semantic translation; in terms of data enhancement, the deep GLR value can be increased by 70.35% by means of “back translation,” and the improved model is effective. It promotes the translation and recognition generalization ability of English long and short sentences.
... Regarding automation, rapid advances in robotization (Fernández-Macías, Klenert and Antón, 2021) and AI are increasingly able to substitute job tasks not just at the bottom of the occupational structure but also at the middle-top (Tolan et al., 2021). These new technologies replace routine tasks and codify and store knowledge more quickly and cheaply. ...
Full-text available
Preprint
EGP (Erikson-Goldthorpe-Portocarero)-based occupational class schemas, rooted in industrial-age employment relations, are the standard measure of socioeconomic position in social stratification. Previous research highlighted EGP-based schemas’ difficulties to keep up with changing labour markets, but few tested alternative explanations. This article explores how job tasks linked to technological change and economic inequality might confound the links between employment relations, classes, and life chances. Using the European Working Conditions Survey covering the EU-27, this article analyses over time and gender (1) the task distribution between social classes; and (2) whether tasks are predictive of class membership and life chances. Decomposition analyses suggest that tasks explain class membership and wage inequality better than employment relations. However, intellectual/routine tasks and digital tools driving income inequality are well-stratified by occupational classes. Therefore, this article does not argue for a class (schema) revolution but for fine-tuning the old instrument to portray market inequalities in the digital age.
... While innovation on robots and software has mainly affected low skill and low wage occupations in the past, he finds that AI is increasingly predicted to disrupt high-skill occupations. Building on this work, Tolan et al. [16] link research intensity in AI to abilities required for specific job tasks using European survey data, O � NET data and AI benchmarking platforms. They find that jobs that were originally classified as non-automatable are increasingly affected by automation such as medical doctors. ...
... To capture the abilities and skills required by occupations at the three-digit occupation level we draw on O � NET version 15. O � NET is an occupational database by the US Department of Labor that narrowly defines occupations with respect to the tasks and activities and the skills and abilities required on the job. This database has been used frequently in the automation literature both in the US and the UK [2,3,5,16]. The difference in task content of occupations in the US versus the UK has been shown to be small, further justifying using this resource to classify occupation attributes using UK data [3,21]. ...
Full-text available
Article
This study identifies the job attributes, and in particular skills and abilities, which predict the likelihood a job is recently automatable drawing on the Josten and Lordan (2020) classification of automatability, EU labour force survey data and a machine learning regression approach. We find that skills and abilities which relate to non-linear abstract thinking are those that are the safest from automation. We also find that jobs that require ‘people’ engagement interacted with ‘brains’ are also less likely to be automated. The skills that are required for these jobs include soft skills. Finally, we find that jobs that require physically making objects or physicality more generally are most likely to be automated unless they involve interaction with ‘brains’ and/or ‘people’.
... Current research deals partly with conceptual boundaries and the ways that AI can be operationalized for empirical research (Ernst et al., 2019;Acemoglu and Restrepo, 2020b;Tolan et al., 2021). Building on or parallel to this, empirical work has also been conducted on the quantitative effects of AI on employment, wages, hires, and fluctuation (Felten et al., 2019;Webb, 2020;Georgieff and Hyee, 2021;Fossen and Sorgner, 2022). ...
Full-text available
Article
Artificial intelligence (AI) has a high application potential in many areas of the economy, and its use is expected to accelerate strongly in the coming years. This is linked with changes in working conditions that may be substantial and entail serious health risks for employees. With our paper we are the first to conduct an empirical analysis of employers' increasing flexibility requirements in the course of advancing digitalization, based on a representative business survey, the IAB Job Vacancy Survey. We combine establishment-level data from the survey and occupation-specific characteristics from other sources and apply non-linear random effects estimations. According to employers' assessments, office and secretarial occupations are undergoing the largest changes in terms of flexibility requirements, followed by other occupations that are highly relevant in the context of AI: occupations in company organization and strategy, vehicle/aerospace/shipbuilding technicians and occupations in insurance and financial services. The increasing requirements we observe most frequently are those concerning demands on employees' self-organization, although short-term working-time flexibility and workplace flexibility also play an important role. The estimation results show that the occupational characteristics, independently of the individual employer, play a major role for increasing flexibility requirements. For example, occupations with a larger share of routine cognitive activities (which in the literature are usually more closely associated with artificial intelligence than others) reveal a significantly higher probability of increasing flexibility demands, specifically with regard to the employees' self-organization. This supports the argument that AI changes above all work content and work processes. For the average age of the workforce and the unemployment rate in an occupation we find significantly negative effects. At the establishment level the share of female employees plays a significant negative role. Our findings provide clear indications for targeted action in labor market and education policy in order to minimize the risks and to strengthen the chances of an increasing application of AI technologies.
... This could be a potential weakness of this indicator. In contrast (Tolan et al., 2021) rely on expert assessments for the link between AI applications and worker abilities (Tolan et al., 2021). 15 At the six digit SOC 2010 occupational level, this can be aggregated across sectors and geographical regions, see Felten et al. (2021). ...
... This could be a potential weakness of this indicator. In contrast (Tolan et al., 2021) rely on expert assessments for the link between AI applications and worker abilities (Tolan et al., 2021). 15 At the six digit SOC 2010 occupational level, this can be aggregated across sectors and geographical regions, see Felten et al. (2021). ...
... This indicator is available for the US for the year 2016/2017. Tolan et al. (2021) introduce a layer of cognitive abilities to connect AI applications (that they call benchmarks) to tasks. The authors define 14 cognitive abilities (e.g., visual processing, planning and sequential decision-making and acting, communication, etc.) from the psychometrics, comparative psychology, cognitive science, and AI literature 16 . ...
Full-text available
Article
Recent years have seen impressive advances in artificial intelligence (AI) and this has stoked renewed concern about the impact of technological progress on the labor market, including on worker displacement. This paper looks at the possible links between AI and employment in a cross-country context. It adapts the AI occupational impact measure developed by Felten, Raj and Seamans—an indicator measuring the degree to which occupations rely on abilities in which AI has made the most progress—and extends it to 23 OECD countries. Overall, there appears to be no clear relationship between AI exposure and employment growth. However, in occupations where computer use is high, greater exposure to AI is linked to higher employment growth. The paper also finds suggestive evidence of a negative relationship between AI exposure and growth in average hours worked among occupations where computer use is low. One possible explanation is that partial automation by AI increases productivity directly as well as by shifting the task composition of occupations toward higher value-added tasks. This increase in labor productivity and output counteracts the direct displacement effect of automation through AI for workers with good digital skills, who may find it easier to use AI effectively and shift to non-automatable, higher-value added tasks within their occupations. The opposite could be true for workers with poor digital skills, who may not be able to interact efficiently with AI and thus reap all potential benefits of the technology1.
... At present, no automation technology has the sufficient level of generality to perform all of the task content associated with any existing occupation, and thus trying to assess the potential impact on employment of technological change at the level of full occupations is bound to fail. The effect of automation technologies on work is better understood at the level of specific tasks (Tolan et al., 2020). In this respect, the high level of granularity in the classification of task contents of our proposed taxonomy can be used to identify on what types of work a given technology is Fig. 3 Correlations between task indicators (EU15) and wage occupational indices (EU10). ...
... In a recent paper, Tolan et al. (2020) use our proposed taxonomy to assess the potential impact of recent advances in AI on employment. The basic idea behind this approach is that each type of task in our taxonomy (identified at the most detailed level) requires one or more human cognitive abilities, 5 and the same list of human cognitive abilities can be also used to classify recent progress in the different domains of AI research. ...
... Both abilities and skills are relevant to task performance, but from a human perspective abilities are innate (and lower level) while skills are acquired through a combination of abilities, experience and knowledge(Tolan et al., 2020).6 InTolan et al. (2020), progress in AI research is proxied by the amount of research output across 328 AI Benchmarks (standardized contests linked to specific problems to be solved using AI algorithms). ...
Full-text available
Article
In recent years, the increasing concern about the labour market implications of technological change has led economists to look in more detail at the structure of work content and job tasks.Incorporating insights from other traditions of task analysis, in particular from the labour process approach), as well as from recent research on skills, work organisation and occupational change, in this paper we propose a new comprehensive and detailed taxonomy of tasks. Going beyond existing broad classifications, our taxonomy aims at connecting the substantive content of work with its organisational context by answering two key questions: what do people do at work and how do they do their work? For illustrative purposes, we show how our approach allows a better understanding of the impact of new technologies on work, by accounting for relevant ongoing transformations such as the diffusion of artificial intelligence and the unfolding of digital labour platforms.
... The introduction of digital economy tools and AI (artificial intelligence) in society have been increasingly focusing discussions on the issues of replacing people with machines, distorting ethics, degradation of natural intelligence, malicious use of AI, and even a drop in the level of spirituality [1], [2]. The main ways in which AI can negatively distort ethics do not come from some futurological uncontrollable super-intelligence or ''killer drones'' [3], but from the risks associated with people's trust in the work results of AI [4]. ...
Full-text available
Article
The goal of the paper is to find means for the unification of human-machine duality in collective behavior of people and machines, by conciliating approaches that proceed in opposite directions. The first approach proceeds top-down from non-formalizable, cognitive, uncaused, and chaotic human consciousness towards purposeful and sustainable human-machine interaction. The second approach proceeds bottom-up from intelligent machines towards high-end computing and is based on formalizable models leveraging multi-agent architectures. The resulting work reviews the extent, the merging points, and the potential of hybrid artificial intelligence frameworks that accept the idea of strong artificial intelligence. These models concern the pairing of connectionist and cognitive architectures, conscious and unconscious actions, symbolic and conceptual realizations, emergent and brain-based computing, and automata and subjects. The special authors’ convergent methodology is considered, which is based on the integration of inverse problem-solving on topological spaces, cognitive modelling, quantum field theory, category theory methods, and holonic approaches. It aims to a more purposeful and sustainable human-machine interaction in form of algorithms or requirements, rules of strategic conversations or network brainstorming, and cognitive semantics. The paper addresses the reduction of the impact of AI development on ethics violation. The findings delivered are used to provide perspectives on the shaping of societal, ethical, and normative aspects in the symbiosis between humans and machines. Implementations in real practice are represented.
... Deep Learning Methoden haben in den letzten Jahren enorme Erfolge in einer Vielzahl von Anwendungen verzeichnet, beispielsweise bei der Bilderkennung (Computer Vision) oder Sprachverarbeitung (Natural Language Processing). Um das Maß an Einfluss vorherzusagen, das KI auf verschiedene Tätigkeitsfelder und Professionen nehmen wird, existieren verschiedene Modelle (Brynjolfsson et al., 2018;Tolan et al., 2021;Webb, 2019). ...
Full-text available
Book
Der zunehmende Einsatz Künstlicher Intelligenz (KI) in der Medizin wird tiefgreifende Veränderungen hervorrufen. Angesichts dessen benötigen (zukünftige) Mediziner*innen Kompetenzen, die sie zur nutzbringenden Anwendung von KI-Systemen in Praxis und Forschung befähigen. Wir erheben den Bestand an Lernangeboten zum Thema KI in der Medizin für Medizinstudierende und Ärzt*innen in Deutschland, definieren relevante KI-Kompetenzen und Formate und identifizieren Bedarfslücken und Herausforderungen der Implementierung von Lernangeboten in der medizinischen Aus-, Weiter- und Fortbildung. Schließlich legen wir Lösungsstrategien bzw. Empfehlungen für die erfolgreiche KI-Qualifizierung (zukünftiger) Mediziner*innen vor.
... On the potential impact of AI and machine learning on employment see the discussions inPratt (2015),Brynjolfsson and Mitchell (2017) andTolan et al. (2020). In this paper we restrict the analysis to one technologyindustrial robots. ...
Full-text available
Article
This paper analyses data on industrial robots in European manufacturing sectors. In particular, we focus on the applications and characteristics of industrial robots, their distribution over countries and sectors and the main factors that are correlated with robot adoption such as wage levels and robot prices. We argue that, contrary to popular belief, the types of robots widely used in manufacturing today do not imply a discontinuity in terms of automation and labour replacement possibilities. Instead, current robotic technology is better understood as the most recent iteration of industrial automation technologies that have existed for a very long time. These technologies arguably had their biggest impact generations ago, partially explaining changes in employment structures in agricultural and manufacturing sectors that go back to the Industrial Revolution. Thus, the potential employment effects of current robot technology are a priori limited.