Where can AI be used? Insights from a deep ontology of work activities
arXiv:2603.20619v1 Announce Type: new Abstract: Artificial intelligence (AI) is poised to profoundly reshape how work is executed and organized, but we do not yet have deep frameworks for understanding where AI can be used. Here we provide a comprehensive ontology of work activities that can help systematically analyze and predict uses of AI. To do this, we disaggregate and then substantially reorganize the approximately 20K activities in the US Department of Labor's widely used O*NET occupational database. Next, we use this framework to classify descriptions of 13,275 AI software applications and a worldwide tally of 20.8 million robotic systems. Finally, we use the data about both these kinds of AI to generate graphical displays of how the estimated units and market values of all worldwide AI systems used today are distributed across the work activities that these systems help perform. We find a highly uneven distribution of AI market value across activities, with the top 1.6% of ac
arXiv:2603.20619v1 Announce Type: new Abstract: Artificial intelligence (AI) is poised to profoundly reshape how work is executed and organized, but we do not yet have deep frameworks for understanding where AI can be used. Here we provide a comprehensive ontology of work activities that can help systematically analyze and predict uses of AI. To do this, we disaggregate and then substantially reorganize the approximately 20K activities in the US Department of Labor's widely used O*NET occupational database. Next, we use this framework to classify descriptions of 13,275 AI software applications and a worldwide tally of 20.8 million robotic systems. Finally, we use the data about both these kinds of AI to generate graphical displays of how the estimated units and market values of all worldwide AI systems used today are distributed across the work activities that these systems help perform. We find a highly uneven distribution of AI market value across activities, with the top 1.6% of activities accounting for over 60% of AI market value. Most of the market value is used in information-based activities (72%), especially creating information (36%), and only 12% is used in physical activities. Interactive activities include both information-based and physical activities and account for 48% of AI market value, much of which (26%) involves transferring information. These results can be viewed as rough predictions of the AI applicability for all the different work activities down to very low levels of detail. Thus, we believe this systematic framework can help predict at a detailed level where today's AI systems can and cannot be used and how future AI capabilities may change this.
Executive Summary
This study provides a comprehensive ontology of work activities to systematically analyze and predict the uses of artificial intelligence (AI) in various sectors. By reorganizing and classifying 20K activities from the O*NET database, the researchers applied their framework to 13,275 AI software applications and 20.8 million robotic systems, revealing a highly uneven distribution of AI market value across activities. The study found that information-based activities account for 72% of AI market value, with a significant portion used in creating information. The results offer rough predictions of AI applicability for work activities, enabling the identification of areas where AI can be effectively utilized and where its limitations lie. This framework can aid in predicting the impact of future AI capabilities on various sectors.
Key Points
- ▸ The study provides a comprehensive ontology of work activities to analyze and predict the uses of AI.
- ▸ The framework classifies 20K activities from the O*NET database and applies it to 13,275 AI software applications and 20.8 million robotic systems.
- ▸ The study reveals a highly uneven distribution of AI market value across activities, with information-based activities accounting for 72% of market value.
Merits
Strength in framework development
The study's framework for analyzing and predicting AI uses is comprehensive and systematic, allowing for the identification of areas where AI can be effectively utilized and where its limitations lie.
Insights into AI applicability
The study provides rough predictions of AI applicability for work activities, enabling the identification of areas where AI can be used and where its limitations lie.
Demerits
Limitation in scope
The study's scope is limited to the O*NET database and may not account for emerging or niche AI applications.
Assumption of current market conditions
The study assumes current market conditions and may not account for future changes in the AI market or technological advancements.
Expert Commentary
This study makes a significant contribution to the field of AI research by providing a comprehensive framework for analyzing and predicting AI uses. The study's findings on AI applicability and market value distribution offer valuable insights for businesses, organizations, and policymakers. However, the study's limitations in scope and assumption of current market conditions must be acknowledged. Future research should aim to expand the scope of the study to include emerging or niche AI applications and account for future changes in the AI market or technological advancements. Additionally, policymakers should consider the study's implications for addressing the digital divide and promoting equal access to AI.
Recommendations
- ✓ Research institutions and organizations should collaborate to develop and refine the study's framework, ensuring its applicability to diverse sectors and industries.
- ✓ Policymakers should develop targeted interventions to address the digital divide and promote equal access to AI, leveraging the study's insights on AI applicability and market value distribution.
Sources
Original: arXiv - cs.AI
