"Generate" the Future of Work through AI: Empirical Evidence from Online Labor Markets
- URL: http://arxiv.org/abs/2308.05201v2
- Date: Thu, 6 Jun 2024 22:23:57 GMT
- Title: "Generate" the Future of Work through AI: Empirical Evidence from Online Labor Markets
- Authors: Jin Liu, Xingchen Xu, Xi Nan, Yongjun Li, Yong Tan,
- Abstract summary: Large Language Model (LLM) based generative AI, such as ChatGPT, is considered the first generation of Artificial General Intelligence (AGI)
Our paper offers crucial insights into AI's influence on labor markets and individuals' reactions.
- Score: 4.955822723273599
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Large Language Model (LLM) based generative AI, such as ChatGPT, is considered the first generation of Artificial General Intelligence (AGI), exhibiting zero-shot learning abilities for a wide variety of downstream tasks. Due to its general-purpose and emergent nature, its impact on labor dynamics becomes complex and difficult to anticipate. Leveraging an extensive dataset from a prominent online labor market, we uncover a post-ChatGPT decline in labor demand, supply, and transactions for submarkets pertaining to text-related and programming-related jobs, in comparison to those not directly exposed to ChatGPT's core functionalities. Meanwhile, these affected submarkets exhibit a discernible increase in the complexity of the remaining jobs and a heightened level of competition among freelancers. Intriguingly, our findings indicate that the diminution in the labor supply pertaining to programming is comparatively less pronounced, a phenomenon ascribed to the transition of freelancers previously engaged in text-related tasks now bidding for programming-related opportunities. Although the per-period job diversity freelancers apply for tends to be more limited, those who successfully navigate skill transitions from text to programming demonstrate greater resilience to ChatGPT's overall market contraction impact. As AI becomes increasingly versatile and potent, our paper offers crucial insights into AI's influence on labor markets and individuals' reactions, underscoring the necessity for proactive interventions to address the challenges and opportunities presented by this transformative technology.
Related papers
- From Occupations to Tasks: A New Perspective on Automatability Prediction Using BERT [19.75055647648098]
We propose a BERT-based classifier to predict the automatability of tasks in the forthcoming decade.
Our findings indicate that approximately 25.1% of occupations within the O*NET database are at substantial risk of automation.
arXiv Detail & Related papers (2025-02-13T07:18:57Z) - Exploring the Implementation of AI in Early Onset Interviews to Help Mitigate Bias [0.0]
This paper investigates the application of artificial intelligence (AI) in early-stage recruitment interviews.
Results indicate that AI effectively minimizes sentiment-driven biases by 41.2%.
arXiv Detail & Related papers (2025-01-17T00:40:35Z) - TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks [52.46737975742287]
We build a self-contained environment with data that mimics a small software company environment.
We find that with the most competitive agent, 24% of the tasks can be completed autonomously.
This paints a nuanced picture on task automation with LM agents.
arXiv Detail & Related papers (2024-12-18T18:55:40Z) - Generative AI Impact on Labor Market: Analyzing ChatGPT's Demand in Job Advertisements [0.9886108751871759]
This study examines the demand for ChatGPT-related skills in the U.S. labor market.
Using text mining and topic modeling techniques, we extracted and analyzed the Gen AI-related skills that employers are hiring for.
arXiv Detail & Related papers (2024-12-09T23:03:20Z) - Follow the money: a startup-based measure of AI exposure across occupations, industries and regions [0.0]
Existing measures of AI occupational exposure focus on AI's theoretical potential to substitute or complement human labour on the basis of technical feasibility.
We introduce the AI Startup Exposure (AISE) index-a novel metric based on occupational descriptions from O*NET and AI applications developed by startups.
Our findings suggest that AI adoption will be gradual and shaped by social factors as much as by the technical feasibility of AI applications.
arXiv Detail & Related papers (2024-12-06T10:25:05Z) - Robotic Control via Embodied Chain-of-Thought Reasoning [86.6680905262442]
Key limitation of learned robot control policies is their inability to generalize outside their training data.
Recent works on vision-language-action models (VLAs) have shown that the use of large, internet pre-trained vision-language models can substantially improve their robustness and generalization ability.
We introduce Embodied Chain-of-Thought Reasoning (ECoT) for VLAs, in which we train VLAs to perform multiple steps of reasoning about plans, sub-tasks, motions, and visually grounded features before predicting the robot action.
arXiv Detail & Related papers (2024-07-11T17:31:01Z) - WESE: Weak Exploration to Strong Exploitation for LLM Agents [95.6720931773781]
This paper proposes a novel approach, Weak Exploration to Strong Exploitation (WESE) to enhance LLM agents in solving open-world interactive tasks.
WESE involves decoupling the exploration and exploitation process, employing a cost-effective weak agent to perform exploration tasks for global knowledge.
A knowledge graph-based strategy is then introduced to store the acquired knowledge and extract task-relevant knowledge, enhancing the stronger agent in success rate and efficiency for the exploitation task.
arXiv Detail & Related papers (2024-04-11T03:31:54Z) - Combatting Human Trafficking in the Cyberspace: A Natural Language
Processing-Based Methodology to Analyze the Language in Online Advertisements [55.2480439325792]
This project tackles the pressing issue of human trafficking in online C2C marketplaces through advanced Natural Language Processing (NLP) techniques.
We introduce a novel methodology for generating pseudo-labeled datasets with minimal supervision, serving as a rich resource for training state-of-the-art NLP models.
A key contribution is the implementation of an interpretability framework using Integrated Gradients, providing explainable insights crucial for law enforcement.
arXiv Detail & Related papers (2023-11-22T02:45:01Z) - AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities
and Challenges [60.56413461109281]
Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes.
We discuss in depth the key types of data emitted by IT Operations activities, the scale and challenges in analyzing them, and where they can be helpful.
We categorize the key AIOps tasks as - incident detection, failure prediction, root cause analysis and automated actions.
arXiv Detail & Related papers (2023-04-10T15:38:12Z) - Hierarchical Few-Shot Imitation with Skill Transition Models [66.81252581083199]
Few-shot Imitation with Skill Transition Models (FIST) is an algorithm that extracts skills from offline data and utilizes them to generalize to unseen tasks.
We show that FIST is capable of generalizing to new tasks and substantially outperforms prior baselines in navigation experiments.
arXiv Detail & Related papers (2021-07-19T15:56:01Z) - Learning Occupational Task-Shares Dynamics for the Future of Work [5.487438649316376]
Big data and AI have risen significantly among high wage occupations since 2012 and 2016.
We build an ARIMA model to predict future occupational task demands.
arXiv Detail & Related papers (2020-01-28T21:20:33Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.