DataTerminology.comNEW
Also known as: AI
Computing field creating systems that perceive, reason, learn, and act to solve tasks requiring human-like intelligence.
DESCRIPTION
Artificial Intelligence encompasses techniques that enable machines to perform tasks such as perception, reasoning, decision-making, and learning. It includes symbolic approaches, statistical machine learning, deep learning, and reinforcement learning. AI powers applications in NLP, computer vision, robotics, recommendation systems, and autonomous agents. Modern AI emphasizes data-driven models, scalability, and ethical considerations like fairness, safety, and transparency. Governance frameworks and regulations are increasingly shaping AI development and deployment. Organizations adopt AI to enhance efficiency, create new products, and gain competitive advantage.
ALTERNATIVE DEFINITIONS
Encyclopaedia Britannica
Artificial intelligence (AI), the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings.
Highlights AI as machine ability to perform tasks of intelligent beings.
NIST
An AI system is an engineered or machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments.
Defines AI systems in the context of objectives and decisions affecting environments.
Wikipedia
Artificial intelligence (AI) is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by animals and humans.
Contrasts machine intelligence with natural intelligence.
RELATED TERMS
Machine Learning Deep Learning Artificial General Intelligence Neural Network Reinforcement Learning Natural Language Processing Computer Vision Generative AI AI Governance Explainability
COMPETING TERMS
Artificial Narrow Intelligence
RELATIONS
- Narrower: Ambient Intelligence Applied AI Artificial General Intelligence Artificial Narrow Intelligence Artificial Super Intelligence Augmented Intelligence
KNOWLEDGE AREAS
TARGET AUDIENCE
AI Engineer Data Scientist MLOps Engineer Chief Data Officer AI Product Manager
EXTERNAL SOURCES
- Wikipedia - Artificial intelligence
- Microsoft - Responsible AI practices
- Stanford University - Foundational AI course
TOP QUESTIONS
What is currently a hot topic around Artificial Intelligence?
Generative AI and foundation models dominate discussions, alongside responsible AI, AI safety, and regulation. Advances in multimodal models and edge deployment are also noteworthy.
Category: Topic
Why is Artificial Intelligence still relevant?
AI drives productivity, personalization, and innovation across industries. It enables automation, advanced analytics, and new customer experiences that were previously infeasible.
Category: Relevancy
How is Artificial Intelligence managed in organizations?
AI initiatives follow governance frameworks, MLOps pipelines, model registries, and continuous monitoring for performance and ethics. Cross-functional collaboration is essential for responsible outcomes.
Category: Management
What is required to implement Artificial Intelligence effectively?
High-quality data, clear objectives, appropriate algorithms, scalable infrastructure, and robust governance are essential. Teams need skills in data engineering, modeling, and model operations.
Category: Requirements
What are common mistakes when adopting Artificial Intelligence?
Overfitting to narrow data, ignoring bias and fairness, weak data governance, unclear ROI, and insufficient monitoring are frequent pitfalls.
Category: Mistakes
What risks arise from Artificial Intelligence deployment?
Risks include biased outcomes, lack of explainability, privacy violations, security vulnerabilities, and regulatory non-compliance. Misuse or misinterpretation can harm users and reputation.
Category: Risk
What is an alternative or competing term for Artificial Intelligence?
Artificial Narrow Intelligence is contrasted with general intelligence, focusing on specific tasks. Symbolic AI contrasts with data-driven connectionist approaches.
Category: Alternatives
How should teams start learning about Artificial Intelligence?
Begin with math foundations and Python, then study machine learning, deep learning, and MLOps. Hands-on projects and reputable courses accelerate learning.
Category: GettingStarted
What is a big misunderstanding about Artificial Intelligence?
Many assume AI is fully autonomous and infallible; in reality, AI relies on data, human oversight, and careful design to avoid harmful outcomes.
Category: Misunderstanding