2.3 Which Tasks Are Cost-Effective to Automate with Computer Vision?

A measure of the likelihood that a job can be automated, based on the combination of occupational skills and subjective assessments of automation levels.

An assessment of job automation risk in OECD countries, using a task-based approach.

A measure of automation integrated into the tasks and responsibilities of a specific job or occupation.

Estimates how suitable tasks within different job categories are for machine learning.

Connects AI advancements with occupational abilities, aggregating them at the occupation level.

Quantifies the exposure of occupations to AI technologies by comparing technology patents with occupation tasks.

Quantifies the exposure of occupations to robots by comparing technology patents with occupation tasks.

Quantifies the exposure of occupations to software by comparing technology patents with occupation tasks.

The rate at which a value increases over time, expressed as a percentage.

A software platform that provides tools and services to build and deploy AI applications.

The process of adapting a pre-trained AI model to perform a specific task.

The ability to use computer vision technology effectively in a business environment.

The use of AI systems to automate tasks that were previously performed by humans.

The cost of developing, deploying, and maintaining AI systems.

The ability of AI-as-a-service platforms to handle increasing workloads and user demands.

The impact of technological advancements on the cost of AI systems.

The minimum number of tasks or operations required for an AI-powered solution to be economically more appealing than employing a human worker.

The cost of deploying an AI system decreases as the scale of its deployment increases.

The possibility of developing an AI model to perform a specific task.

The comparison of the cost of using AI to perform a task vs the cost of human labor for the same task.

How AI automation can be made more economically viable by reducing deployment costs or increasing the scale of deployments.

A service that delivers AI capabilities as a subscription, allowing users to access and utilize AI models without needing to build and maintain their own AI infrastructure.

The process of training and using AI models for tasks that involve understanding and interpreting visual information.

The rate at which jobs are created and lost in the economy.

The cost of developing, deploying, and maintaining an AI system is a crucial factor in determining if automating a task is economically feasible.

The ability of an AI system to perform a task to a level comparable to human performance is essential for economic evaluation.

Prioritizing tasks that can be easily isolated from other parts of a process is essential for efficient automation.

The percentage of a worker's tasks that can be automated using AI is not always a reliable indicator of overall economic feasibility.

The economic attractiveness of AI automation depends on factors like task difficulty, AI system cost, and labor cost.

Companies that have limited size or scale may find it less economically viable to automate tasks due to the high cost of AI systems.

The use of computer vision for automation, specifically in the context of food quality inspection in bakeries, highlights the challenges and economic considerations.

The research suggests that a significant portion of worker tasks may have limited economic incentive for automation due to the cost and performance requirements of current AI systems.

Large and general-purpose AI models trained on vast amounts of data, aiming to perform various tasks with a single model.

Models designed for specific tasks, like medical image analysis or object detection in a factory.

The amount of data available for training AI models. Limited data can restrict a model's ability to generalize to new situations.

The rate at which progress is made in improving AI models. Progress can slow down due to factors like limited data and high costs.

The ability of an AI model to apply knowledge learned from one task to a different, but related, task.

The process of training AI models, which can become increasingly expensive as model size and complexity increase.

The idea that AI adoption follows a pattern where initial costs are high, but decrease over time, leading to wider use and more efficient operations.

AI models designed to generate human-like text, translating input text into a different format or creating original content.

The idea that AI automation, while potentially increasing job destruction, might also substitute for traditional automation, leading to a smaller net effect on employment.

The cost associated with adapting (fine-tuning) a pre-existing foundation model to a specific task.

The study of how AI techniques, like language modeling, can be applied to automating tasks beyond the scope of computer vision.

The idea that improvements in foundation models can reduce automation costs by requiring less fine-tuning or even eliminating the need for it entirely.

The potential impact of foundation model improvements on reducing the cost of AI implementation, making it more economically viable.

The potential impact of foundation model improvements on reducing the amount of data needed for fine-tuning, making automation more efficient.