Task Description Research Articles

Subject Olympiads as an effective tool of management personnel training in universities in modern conditions

The present article studies the issue of using subject Olympiads in higher education institutions for effective training of managerial personnel on the example of the intra-university Olympiad in English language, held in November 2023 at the State University of Management. The quantitative and qualitative composition of participants by training areas and educational programs has been studied, sociological and didactic and methodological aspects of the Olympiad have been highlighted, individual tasks descriptions have been given, demonstrating their high practice-orientedness and relevance. The article concludes that participation in subject university Olympiads stimulates the interest of students of management specialties to the subject of study and increases their competence level. The competitive nature of the Olympiads contributes to a more successful subsequent adaptation of students to a highly competitive professional environment, creating conditions of healthy competition. Conducting subject competitions in universities solves the problems facing the system of higher education in terms of forming a creative educational environment that meets the challenges of the time, and training management specialists capable of effectively solving professional problems against the background of constantly changing economic and political situation in a tough external and internal environment.

Articulatory precision from connected speech as a marker of cognitive decline in Alzheimer's disease risk-enriched cohorts.

Mild cognitive impairment (MCI) has been recognized as a possible precursor to Alzheimer's disease (AD). Recent research focusing on connected speech has uncovered various features strongly correlated with MCI due to AD and related dementias. Despite these advancements, the impact of early cognitive decline on articulatory precision has not been thoroughly investigated. This study introduced the phoneme log-likelihood ratio (PLLR) to assess the articulatory precision of speakers across different cognitive status levels and compared its effectiveness to existing well-studied acoustic features. The study utilized speech recordings from a picture description task, which included data from 324 cognitively unimpaired participants with low amyloid-β burden (CU, Aβ()), 47 cognitively unimpaired participants with high amyloid-β burden (CU, Aβ()), 69 participants with MCI, and 20 participants with dementia. Nine acoustic features were extracted from the speech recordings, covering three categories: speech fluency, speech pace, and articulatory precision. Welch's -test and Hedge's were adopted to assess their discriminative ability. A reduction in speech fluency and pace was observed among participants in the MCI and dementia groups. The PLLR shows large effect sizes in distinguishing between cognitively unimpaired participants with low Aβ burden and those diagnosed with MCI or dementia. Additionally, the test-retest reliability experiment indicated moderate repeatability of the features under study. The study reveals PLLR as a sensitive indicator capable of detecting subtle articulatory variations across groups, while also providing further support for the association between reduced articulatory precision and early cognitive decline.

An audiovisual cognitive optimization strategy guided by salient object ranking for intelligent visual prothesis systems

Objective.Visual prostheses are effective tools for restoring vision, yet real-world complexities pose ongoing challenges. The progress in AI has led to the emergence of the concept of intelligent visual prosthetics with auditory support, leveraging deep learning to create practical artificial vision perception beyond merely restoring natural sight for the blind.Approach.This study introduces an object-based attention mechanism that simulates human gaze points when observing the external world to descriptions of physical regions. By transforming this mechanism into a ranking problem of salient entity regions, we introduce prior visual attention cues to build a new salient object ranking (SaOR) dataset, and propose a SaOR network aimed at providing depth perception for prosthetic vision. Furthermore, we propose a SaOR-guided image description method to align with human observation patterns, toward providing additional visual information by auditory feedback. Finally, the integration of the two aforementioned algorithms constitutes an audiovisual cognitive optimization strategy for prosthetic vision.Main results.Through conducting psychophysical experiments based on scene description tasks under simulated prosthetic vision, we verify that the SaOR method improves the subjects' performance in terms of object identification and understanding the correlation among objects. Additionally, the cognitive optimization strategy incorporating image description further enhances their prosthetic visual cognition.Significance.This offers valuable technical insights for designing next-generation intelligent visual prostheses and establishes a theoretical groundwork for developing their visual information processing strategies. Code will be made publicly available.

An analysis of descriptions by Catalan-speaking individuals with aphasia

ABSTRACT Background Description tasks are used to evaluate and investigate the production abilities of people with language impairments such as aphasia. Aims The goal of the present study is to investigate the production abilities of Catalan-speaking individuals with aphasia (IWA), as well as a smaller sample of individuals with cognitive impairment (IWCI), in comparison with those of healthy controls. A second goal is to evaluate whether the scoring system of the Catalan version of the CAT (CAT-CAT) is helpful to discriminate different patterns of language impairment. Methods and procedure In the study, 109 control subjects, 20 IWA and 4 IWCI were asked to orally describe a picture from the CAT-CAT. The scoring method consisted in a closed-rating system that evaluates productivity, discourse efficiency, fluency, grammatical complexity and grammaticality. Results The results show that the overall scores of the control subjects were significantly higher than those of the experimental groups; the difference between the two experimental groups was not significant. Yet, the group of IWA showed greater intersubject variability than the group of IWCI. Also, the IWA were consistently worse in fluency, grammaticality and grammatical complexity than the IWCI, which indicated a different pattern of performance between the two groups. Conclusions Our findings therefore show that the oral picture description of the CAT-CAT is sensitive to the language impairment of subjects with aphasia and cognitive impairment. Moreover, the rating system put forward allows us to uncover different patterns of language impairment, since it includes variables to evaluate separately content and discourse efficiency on the one hand, and grammatical features on the other hand.

Using Natural Language to Improve Hierarchical Reinforcement Learning in Games

This work investigates how natural language task descriptions can accelerate reinforcement learning in games. Recognizing that human descriptions often imply a hierarchical task structure, we propose a method to extract this hierarchy and convert it into "options" – policies for solving subtasks. These options are generated by grounding natural language descriptions into environment states, which are then used as task boundaries to learn option policies either by leveraging prior successful traces or from human created walkthroughs. We evaluate our approach in both a simpler grid-world environment and the more complex text-based game Zork, comparing option-based agents against standard Q-learning and random agents. Our results demonstrate the effectiveness of incorporating natural language task knowledge for faster and more efficient reinforcement learning across different environments and Q-learning algorithms, including tabular Q-learning and Deep Q-Networks.

Towards Holistic Functional Task Analysis

ABSTRACTTask analysis (TA) can contribute to work systems design, accident investigation, risk assessment, human error identification, planning, and training. Despite the advantages of existing sequential and hierarchical methods, they decompose tasks into their structure and focus on the order in which tasks are accomplished. They do not trace all interactions among elements/subtasks/operations at different levels. As the complexity of tasks increases, not keeping track of all interactions may result in poor, unwanted outcomes. This research introduces a different approach to TA that decomposes tasks into their constituent functions, describes the functionality of the overall work system, traces (dynamic nonlinear) interactions among functions, and highlights the role of functional variability in forming emergent outcomes. This approach to TA is called functional task analysis (FTA). A case study on nursing work was used to demonstrate the suitability of the FTA approach. The findings of this study show that the FTA approach contributes to task modeling by building a nonsequential, nonhierarchical functional model of a complex task considering dynamic, nonlinear interactions among functions. The FTA also contributes to task description by explaining different ways a task can be accomplished. It also increases the understanding, interpretation, and analysis of how changes in work conditions shape good/acceptable and poor/unacceptable outcomes. The FTA can complement the TA by adding some aspects, including functionality, nonlinearity, dynamics, and emergence, that the TA does not normally consider. The findings highlight how the functional approach to TA can be deployed as an alternative (or complement) to other task analysis methods.

Aptitude, Anxiety, and Success in L2 Speech Development: A Longitudinal Study of Chinese EFL College-Level Learners.

This study examined the second language (L2) speech development of a group of Chinese English as a Foreign Language (EFL) college-level learners (N = 83) and the association between their aptitude, anxiety, and L2 speech development. The performance of the participants' L2 speech, including speech comprehensibility and accentedness, was evaluated using a sentence reading task and a pair of picture description tasks (pre- and post-test). In addition, students completed the LLAMA tests (language learning and aptitude measurement in adults) and the Second Language Speaking Anxiety Scale (SLSAS). The t-test revealed that after one semester of English speaking and pronunciation instruction, the EFL students in the study demonstrated significant gains in their L2 speech performance in terms of accentedness and comprehensibility in both controlled and impromptu tasks. Regression analyses revealed that phonemic coding ability positively predicted gains in comprehensibility but negatively predicted gains in accentedness during sentence reading. In addition, associative memory was positively associated with comprehensibility gains in the picture description task. Anxiety appeared to be a strong predictor of participants' gains in speech comprehensibility and accentedness during the picture description task.

Learning by Visualize a Nurse-Led CCOS Using the Functional Resonance Analysis Method.

Quality improvements (QIs) in dynamic and complex health care contexts require resilience and take variability into account in quality improvement. The Functional Resonance Analysis Method (FRAM) helps us understand resilience and gain insight into (un)desirable variability in the complex system of daily practice. We explored how using FRAM in the Deming cycle of a QI project can help professionals and researchers learn from, reflect upon, and improve complex processes. We used FRAM in a Dutch hospital to study a QI: Critical Care Outreach Service (CCOS). The aim was to use FRAM before and after implementation to create a FRAM model and reflect to health care professionals the mismatch between Work As Imagined (WAI) and Work As Done (WAD). The WAI FRAM model was co-created with professionals before the implementation of CCOS. We used descriptions of tasks and processes for ICU nurses and verified them in 30-minute semistructured interviews (N = 2). WAD was created by input of semistructured interviews with key professionals in CCOS (N = 21) and 3 nonparticipant observations of trained CCOS nurses. We validated WAD in 2 dialogue sessions with key professionals (N = 11). Data collection continued until saturation. Juxtaposing the WAI and WAD models showed that WAD contained additional functions and highlighted unexpectedly complex functions. Reflecting on the application of FRAM with health care professionals revealed opportunities and challenges, especially time investment. FRAM helps professionals outline processes and tasks (WAI), learn from, and reflect upon their daily practice (WAD). FRAM models help professionals identify variability proactively to improve practices that enhance resilient performance.

Hostile behavior of the manager

The article examines the problem of hostile behavior of the manager, which is especially relevant for small businesses. The main attention is paid to the reasons for such behavior, its consequences and ways to minimize it. Hostile behavior is often associated with improper management and ineff ective communication, which is what we discuss in this article. To solve this problem, the following measures are proposed: a detailed description of processes and tasks, demonstration of task completion, control and adjustment of employee work.

Cross-Linguistic Analysis of Speech Markers: Insights from English, Chinese, and Italian Speakers.

Cross-linguistic studies with healthy individuals are vital, as they can reveal typologically common and different patterns while providing tailored benchmarks for patient studies. Nevertheless, cross-linguistic differences in narrative speech production, particularly among speakers of languages belonging to distinct language families, have been inadequately investigated. Using a picture description task, we analyze cross-linguistic variations in connected speech production across three linguistically diverse groups of cognitively normal participants-English, Chinese (Mandarin and Cantonese), and Italian speakers. We extracted 28 linguistic features, encompassing phonological, lexico-semantic, morpho-syntactic, and discourse/pragmatic domains. We utilized a semi-automated approach with Computerized Language ANalysis (CLAN) to compare the frequency of production of various linguistic features across the three language groups. Our findings revealed distinct proportional differences in linguistic feature usage among English, Chinese, and Italian speakers. Specifically, we found a reduced production of prepositions, conjunctions, and pronouns, and increased adverb use in the Chinese-speakers compared to the other two languages. Furthermore, English participants produced a higher proportion of prepositions, while Italian speakers produced significantly more conjunctions and empty pauses than the other groups. These findings demonstrate that the frequency of specific linguistic phenomena varies across languages, even when using the same harmonized task. This underscores the critical need to develop linguistically tailored language assessment tools and to identify speech markers that are appropriate for aphasia patients across different languages.

AI-assisted marking: Functionality and limitations of ChatGPT in written assessment evaluation

Generative artificial intelligence technologies, such as ChatGPT, bring an unprecedented change in education by leveraging the power of natural language processing and machine learning. Employing ChatGPT to assist with marking written assessment presents multiple advantages including scalability, improved consistency, eliminating biases associated with human subjectivity. This work aimed to evaluate the usefulness, reliability and accuracy of ChatGPT in marking written assessments of varied types and to identify its limitations and challenges. ChatGPT was instructed using a set of prompts to mark the assessment based on a rubric. ChatGPT was able to evaluate and assess both coding and reflective assessments and to distinguish between assignments of different quality, demonstrating high consistency and accuracy for higher quality assessments, comparable to a human marking. ChatGPT was also able to generate textual detailed justifications based on the rubric and assessment task description. There was a significant difference in the outcomes generated by different prompts. These preliminary findings suggest that utilising ChatGPT as a marking assistant can increase written assessment marking efficiency, reduce cost and potentially decrease the unfairness and bias by providing a moderating perspective. Implications for practice or policy: Assessment designers could reconsider the design, purpose and objectives of written assessments and leverage ChatGPT effectively for teaching and learning. Assessors might consider adapting the technology as a grading aid, to support a human-in-the-loop grading process, providing additional resources and time, moderating and refining individual feedback, to increase consistency and quality. Curriculum and programme leaders could develop guidelines around the ethical use of generative AI-assisted assessment practice, monitor and regulate the ongoing evaluation and refinement.

Variability in Vowel Space in Parkinson's Disease: Associations With Cognitive and Motor Impairment.

People with Parkinson's disease (PwP) typically experience impairments in vowel articulation; however, less is known about how this measure varies with speech task type and clinical characteristics such as cognitive impairment. We characterized vowel space in PwP with and without mild cognitive impairment (MCI) comparing performance across phonation, reading, and picture description tasks. We evaluated associations between vowel space and cognitive impairment, as well as motor symptom severity to elucidate the factors contributing to variability in this acoustic measure. PwP (n = 48) and age-matched controls (n = 15) performed sustained phonation of corner vowels, a reading passage, and a picture description task (Cookie Theft picture). PwP participants were classified as with normal cognition (PD-NC) or MCI (PD-MCI), and motor symptoms were assessed using the Movement Disorders Society Unified Parkingson's Disease Rating Scale Part III (MDS-UPDRS Part III). Vowel articulation index (VAI) for each task and mean difference in VAI between tasks was compared between the groups using linear mixed models adjusted for age, sex, and education. The impact of motor severity was assessed by additionally adjusting the model for MDS-UPDRS Part III score. In the adjusted mixed model, mean VAI was significantly lower in the PD-MCI group compared to the PD-NC group for all tasks. Within participants, adjusted mean differences demonstrated that all groups declined in VAI when sustained phonation was compared to either reading or picture description tasks. Adjustment for MDS-UPDRS Part III did not alter the results, suggesting no major association of motor impairment with vowel space variability within or between individuals or groups. Variability in vowel space is impacted by cognitive impairment and speech task in PwP. These findings are relevant to the further development of speech markers in PwP and other neurogenerative diseases that impact both cognitive and motor functions.

A Conceptual Model for Automation of Small Ports

Abstract For large ports and terminals, the loading and discharge operations have to a great extent been automated already. However, for the vast number of small ports, for instance all Norwegian ports, this large-scale automation is not possible due to the small size of the ports compared to the cost of introducing full automation. Still, these small ports are important logistics hubs in the transport system. They facilitate the shift from congested roads to the sea by allowing the goods to arrive closer to the final destination by using smaller, possibly autonomous, vessels. However, this puts pressure on the ports to facilitate reduced laytime in the port, to increase the port efficiency and to improve interoperability towards hinterland systems and terminals. For small ports, this means that manual and automated work processes need to exist side by side, and that it will be important to get an overview of which processes to automate and which will remain manual, in addition to the handling of the hand-over between humans and automation. This paper describes a conceptual model that can be used by small ports as a starting point on their way to introduce automation of their services supporting the cargo flow through the port. The model includes a description of port functions, tasks and responsibilities and use the concept of operational envelope to define the overall capabilities of the system, including both automation and human. This will be the starting point for how to structure the description of an operation centre that monitor and control the maritime terminal operations. The starting point for this work is a use case analysis performed in some small ports in Norway, and their need to optimize the utilization of their assets. The work is done as part of the AutoPort project which is a research project funded by the Norwegian Research Council (project number 337211). The goal of this work is to describe a methodology on how to identify the interface between automation and human operators when it comes to small ports and the movement of cargo through its terminals, and by this give a description on how different levels of automation can be implemented. The methodology consists of several steps, including definition of the context (big picture of the use case, actors and their roles, and communication), the scenario (port activities and processes), and the use cases including the control tasks performed by the automated systems.

The Relationship of Word-Finding Behaviors and Naming in Mild Cognitive Impairment and Dementia of Alzheimer’s Type

Objectives: Anomia, a fundamental language difficulty, presents in various forms of wordfinding behaviors in individuals with Mild Cognitive Impairment (MCI) and Dementia of Alzheimer’s Type (DAT). This study aims to compare the rates of word-finding behavior among these groups and determine the correlation between these rates and the results of naming tests. Methods: This study included 101 participants, consisting of forty-one healthy elderly adults, thirty individuals with MCI, and thirty individuals with DAT. Spontaneous speech data were collected using a picture description task. For the naming tests, we used the confrontation naming test (K-BNT) and the generative naming test (KCOWAT). Results: First, significant differences were observed in the rates of word-finding behavior among the three groups, especially in the global index, which indicates the overall rate of word-finding behaviors. Second, the correlation analysis results showed a significant negative correlation between the ratios of the global index, word reformulations, empty words, and performance on the confrontation naming test. Finally, there was a significant negative correlation between the ratios of the global index, word reformulations, repetitions, delays, insertions, and performance on the generative naming tests. Conclusion: These results suggest the necessity of developing a naming evaluation scale based on spontaneous speech to effectively assess anomia in individuals with MCI and DAT. The study found significant differences in the rates of word-finding behavior among healthy elderly adults, individuals with MCI, and individuals with DAT, and a correlation between the rates of word-finding behavior and naming tests.

Linguacodus: a synergistic framework for transformative code generation in machine learning pipelines.

In the ever-evolving landscape of machine learning, seamless translation of natural language descriptions into executable code remains a formidable challenge. This article introduces Linguacodus, an innovative framework designed to tackle this challenge by deploying a dynamic pipeline that iteratively transforms natural language task descriptions into code through high-level data-shaping instructions. The core of Linguacodus is a fine-tuned large language model, empowered to evaluate diverse solutions for various problems and select the most fitting one for a given task. This article details the fine-tuning process and sheds light on how natural language descriptions can be translated into functional code. Linguacodus represents a substantial leap towards automated code generation, effectively bridging the gap between task descriptions and executable code. It holds great promise for advancing machine learning applications across diverse domains. Additionally, we propose an algorithm capable of transforming a natural description of an ML task into code with minimal human interaction. In extensive experiments on a vast machine learning code dataset originating from Kaggle, we showcase the effectiveness of Linguacodus. The investigations highlight its potential applications across diverse domains, emphasizing its impact on applied machine learning in various scientific fields.

Screening for early Alzheimer's disease: enhancing diagnosis with linguistic features and biomarkers.

Research has shown that speech analysis demonstrates sensitivity in detecting early Alzheimer's disease (AD), but the relation between linguistic features and cognitive tests or biomarkers remains unclear. This study aimed to investigate how linguistic features help identify cognitive impairments in patients in the early stages of AD. This study analyzed connected speech from 80 participants and categorized the participants into early-AD and normal control (NC) groups. The participants underwent amyloid-β positron emission tomography scans, brain magnetic resonance imaging, and comprehensive neuropsychological testing. Participants' speech data from a picture description task were examined. A total of 15 linguistic features were analyzed to classify groups and predict cognitive performance. We found notable linguistic differences between the early-AD and NC groups in lexical diversity, syntactic complexity, and language disfluency. Using machine learning classifiers (SVM, KNN, and RF), we achieved up to 88% accuracy in distinguishing early-AD patients from normal controls, with mean length of utterance (MLU) and long pauses ratio (LPR) serving as core linguistic indicators. Moreover, the integration of linguistic indicators with biomarkers significantly improved predictive accuracy for AD. Regression analysis also highlighted crucial linguistic features, such as MLU, LPR, Type-to-Token ratio (TTR), and passive construction ratio (PCR), which were sensitive to changes in cognitive function. Findings support the efficacy of linguistic analysis as a screening tool for the early detection of AD and the assessment of subtle cognitive decline. Integrating linguistic features with biomarkers significantly improved diagnostic accuracy.

An Analysis of Research Trends for Using Artificial Intelligence in Cultural Heritage

Artificial intelligence (AI) techniques have been increasingly applied in assisting various cultural heritage (CH)-related tasks. The aim of this study is to examine the research trends and current applications of AI in this vast domain. After obtaining a dataset from the Web of Science and Scopus databases, a scientometric analysis of research publications from 2019 to 2023 related to the use of AI in CH was conducted. The trending topics based on the author’s keywords were identified by using the ScientoPy v2.1.3 software. Through this approach, five main topics were identified: classification, computer vision, 3D reconstruction, recommender systems, and intangible cultural heritage. The analysis highlights the upward trend in publications in this field since 2019, indicating a growing interest in the application of AI techniques in CH. By analyzing the latest research in the field, it is observed that AI techniques are mostly applied to assist CH in the discovery, description, classification, and preservation tasks. The report gives insights into the main research areas and developing trends in the field of artificial intelligence and machine learning. The study offers important information about the key research areas and emerging trends related to using AI techniques in the CH field. This helps to recognize the potential, development, and increasing influence of these technologies within the CH domain. The findings of this study contribute to the future development of AI applications in CH, enabling professionals to use the advantages of these technologies.

Explicit information and practice type can affect the L2 acquisition of plural marking

Abstract Previous research has indicated that explicit information (EI) about either the first language (L1) or second language (L2) along with task-essential practice can facilitate L2 learning (e.g., Fernández, 2008; McManus, 2022). However, little research has examined L1–L2 contrastive EI with L1/L2 practice. Targeting plural-marking accuracy, the present study sought to fill this gap by exposing 127 Japanese intermediate learners of English to six online treatment sessions across four conditions: (1) non-contrastive EI (pluralization rule explanation) + L2 practice, (2) contrastive EI + L2 practice, (3) as per Condition 2 + additional L1 practice (application of L2 pluralization to L1 through the Japanese suffix –tachi), and (4) using prepositions as a control, non-contrastive EI (prepositional rule explanation) + L2 practice. Accuracy was tested at pre/post/delayed intervals using an acceptability judgement task (reading) and a picture description task (writing). Statistical analyses demonstrated greater gains from the contrastive variables in the immediate post-test, with L1 practice appearing especially beneficial, despite improvements diminishing in the delayed post-test. Supplementary data from retroactive interviews and questionnaire responses indicated favourable orientations towards the intervention. The study advances understanding of how EI and practice type can be used to maximize L2 learning through web-based practices tailored to the nature of the learning problem.

Adynamic spoken language in corpus callosum dysgenesis

Corpus callosum dysgenesis (CCD) is a congenital brain malformation that occurs when the development of the corpus callosum is disrupted, either partially or completely. The cognitive outcomes in individuals with CCD vary greatly, but generally the neuropsychological profile is characterised by slow processing speed, poor transfer of interhemispheric sensory-motor information, and impaired complex problem solving. Core language skills are often preserved in CCD, but there is some evidence that complex language may be impaired. Thus, the current study sought to examine whether spontaneous speech output was reduced in a cohort of individuals with CCD compared to age-matched controls. We further explored a series of factors that may be contributing to poor spontaneous speech in CCD, such as difficulties generating, selecting, and sequencing ideas for expression, as well as apathy and slowed processing speed. A cohort of 25 individuals with CCD and 39 neurotypical controls were enrolled in this study. Participants completed a picture description task to measure spontaneous speech output, alongside a series of cognitive and language baseline tests. Verbal and nonverbal fluency tasks gauged idea generation and sequencing, and sentence-level selection tasks measured idea selection. We found that, despite having largely intact core language skills, individuals with CCD produced significantly less spontaneous speech on the picture description task than controls. This language profile may be described as “adynamic”. Further, we found that poor spontaneous speech output in CCD was related to problems generating ideas for expression, as individuals with CCD performed below controls on the verbal and nonverbal fluency tasks. Exploratory analyses revealed that apathy and slowed processing speed may be contributing factors. Adynamia in CCD is a novel finding that may be an intervention target for improving communication skills in this population.

The potential impact of AI innovations on US occupations.

An occupation is comprised of interconnected tasks, and it is these tasks, not occupations themselves, that are affected by Artificial Intelligence (AI). To evaluate how tasks may be impacted, previous approaches utilized manual annotations or coarse-grained matching. Leveraging recent advancements in machine learning, we replace coarse-grained matching with more precise deep learning approaches. Introducing the AI Impact measure, we employ Deep Learning Natural Language Processing to automatically identify AI patents that may impact various occupational tasks at scale. Our methodology relies on a comprehensive dataset of 17,879 task descriptions and quantifies AI's potential impact through analysis of 24,758 AI patents filed with the United States Patent and Trademark Office between 2015 and 2022. Our results reveal that some occupations will potentially be impacted, and that impact is intricately linked to specific skills. These include not only routine tasks (codified as a series of steps), as previously thought but also nonroutine ones (e.g. diagnosing health conditions, programming computers, and tracking flight routes). However, AI's impact on labor is limited by the fact that some of the occupations affected are augmented rather than replaced (e.g. neurologists, software engineers, air traffic controllers), and the sectors affected are experiencing labor shortages (e.g. IT, Healthcare, Transport).