Short for "Artificial Intelligence," the field of computer science that focuses on developing machines that can perform tasks that typically require human intelligence.
B - Big Data
The massive amounts of data that are generated every day, which can be analyzed and used to train AI systems.
C - Computer Vision
The field of AI that focuses on enabling machines to interpret and understand visual information from the world around them.
D - Deep Learning
A type of machine learning that uses neural networks with multiple layers to learn increasingly complex patterns and representations.
E - Expert Systems
AI systems that mimic the decision-making abilities of a human expert in a particular field.
F - Fuzzy Logic
A type of logic that allows for imprecise or uncertain reasoning, often used in AI systems that deal with uncertain or incomplete information.
G - Genetic Algorithms
A type of AI algorithm inspired by natural selection, which can be used to optimize complex systems.
H - Heuristics
A problem-solving approach used in AI systems that involves using rules of thumb or educated guesses to make decisions.
I - Intelligent Agents
AI systems that are capable of autonomous decision-making based on their environment.
J - Jupyter Notebook
A popular tool used in the development and training of AI models, which allows for interactive coding and data analysis.
K - Knowledge Representation
The process of encoding knowledge in a format that can be used by AI systems.
L - Logic Programming
A type of programming that uses formal logic to represent knowledge and solve problems.
M - Machine Learning
A type of AI that involves training machines to learn from data, rather than being explicitly programmed.
N - Natural Language Processing
A type of AI that involves training machines to learn from data, rather than being explicitly programmed.
O - Ontology
A formal representation of knowledge that can be used to enable intelligent reasoning and decision-making.
P - Planning and Scheduling
AI techniques used to generate plans or schedules that can be executed by machines.
Q - Quality Control
The use of AI systems to improve the quality of products or services.
R - Robotics
The field of AI that focuses on the development of robots and other physical machines that can interact with the world around them.
S - Speech Recognition
AI systems that can interpret and understand human speech.
T - Turing Test
A test designed to evaluate a machine's ability to exhibit intelligent behavior that is indistinguishable from that of a human.
U - Uncertainty
The inherent uncertainty and ambiguity in many real-world problems, which can make them challenging for AI systems to solve.
V - Virtual Assistants
AI systems that can interact with humans to answer questions, perform tasks, or provide other forms of assistance.
W - Weak AI
AI systems that are designed to perform specific tasks or solve specific problems, rather than exhibiting general intelligence.
X - Explainability
The ability of an AI system to explain its decision-making process in a way that is understandable to humans.
Y - Yield Management
The use of AI systems to optimize pricing and inventory management in industries such as transportation and hospitality.
Z - Zero-shot Learning
A type of machine learning in which a model is trained to recognize new categories of objects without being explicitly trained on them.