AGI (Artificial General Intelligence)
A hypothetical form of AI that can understand, learn, and apply intelligence across any task a human can do, rather than being limited to specific domains.
AI Concepts
51+ terms defined. From AGI to zero-shot learning, understand the vocabulary of AI job displacement and the future of work.
A hypothetical form of AI that can understand, learn, and apply intelligence across any task a human can do, rather than being limited to specific domains.
Using AI to enhance human capabilities rather than replace them. Workers use AI tools to become more productive while retaining decision-making authority.
The replacement of human workers by AI systems, either through full automation of roles or significant reduction in labor demand for specific tasks.
Use of AI and algorithms to manage, monitor, and evaluate worker performance, often in real-time. Common in gig economy platforms.
The use of technology to perform tasks with minimal human intervention. Includes both physical (robotics) and cognitive (AI) automation.
The technical feasibility of automating specific tasks or jobs using current or near-term technology. A key factor in displacement risk.
An AI program designed to simulate conversation with human users. Used extensively in customer service and support roles.
Automation of tasks requiring thinking, learning, or decision-making, as opposed to purely physical tasks.
AI technology that enables computers to interpret and understand visual information from images and videos.
A subset of machine learning using neural networks with many layers. Powers most modern AI breakthroughs including language models and image recognition.
A virtual replica of a physical system, product, or process used for simulation, testing, and optimization.
A quantitative measure of AI job displacement risk, typically expressed as a percentage. displacement.ai calculates this using a weighted model of multiple factors.
The financial motivation for automation, including labor cost savings, productivity gains, and return on investment.
AI systems designed to provide human-understandable explanations for their decisions and actions.
The process of further training a pre-trained AI model on specific data to adapt it for a particular task or domain.
Large AI models trained on broad data that can be adapted for many downstream tasks. Examples include GPT and BERT.
AI that can create new content including text, images, audio, video, and code. Includes tools like ChatGPT, DALL-E, and Midjourney.
A labor market characterized by short-term, flexible jobs often mediated by digital platforms, heavily shaped by algorithmic management.
When AI models generate plausible-sounding but incorrect or fabricated information. A key limitation of current language models.
Systems where humans actively participate in AI decision-making, providing oversight, corrections, or final approvals.
Work arrangements where humans collaborate with AI systems, each handling tasks best suited to their capabilities.
The hollowing out of middle-skill jobs while high-skill and low-skill jobs grow, often attributed to automation.
Work primarily involving thinking, analyzing, and creating information rather than physical labor. Increasingly automatable by AI.
The degree to which a job or sector is affected by technological change, either through displacement or transformation.
AI models trained on massive text datasets that can understand and generate human-like text. GPT-4, Claude, and Gemini are examples.
AI systems that learn from data to improve performance on tasks without being explicitly programmed.
AI technology enabling computers to understand, interpret, and generate human language.
Computing systems inspired by biological neural networks, forming the foundation of modern AI and deep learning.
The Occupational Information Network, a comprehensive database of occupational requirements and worker characteristics used in labor market research.
The degree to which specific occupations are subject to AI-driven change, measured by task composition and automation potential.
Using data, statistical algorithms, and machine learning to identify the likelihood of future outcomes.
Automating repetitive, rule-based business processes, often using RPA or AI.
The practice of designing effective prompts to get desired outputs from AI models. An emerging skill in AI-augmented work.
Machine learning where AI agents learn optimal behaviors through trial and error, receiving rewards or penalties.
Learning new skills to transition to a different job or career, often necessary due to automation displacing current roles.
A numerical measure (typically 0-100%) indicating the probability that a job will be significantly impacted by AI automation.
Software robots that automate repetitive digital tasks by mimicking human interactions with computer systems.
Predictable, repetitive tasks that follow clear rules or patterns. Most vulnerable to automation.
The barrier preventing frontline and lower-wage workers from accessing AI tools that could augment their productivity.
When skills that were once valuable become less relevant due to technological change.
Automation that combines AI decision-making with physical or digital process execution.
Machine learning using labeled training data where the correct output is known.
Automating individual tasks within a job rather than the entire role. Most jobs are partially automatable.
Breaking jobs down into component tasks to assess automation potential, rather than treating jobs as monolithic units.
Job losses caused by technological change outpacing the creation of new jobs or retraining of displaced workers.
The data used to teach machine learning models. Quality and bias in training data affect AI performance.
Using knowledge from one AI task to improve performance on a related task.
Developing additional skills to enhance performance in current or related roles, often to work alongside AI.
AI systems that understand and generate speech, powering virtual assistants and voice-based interfaces.
The broad shift in how work is organized, performed, and valued due to technological and economic changes.
AI capability to perform tasks without being explicitly trained on examples of that specific task.
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