- Agentic AI
- AI systems that can work autonomously to achieve goals without direct human input.
- Algorithm
- A set of rules or instructions that a machine follows to complete a task.
- Artificial General Intelligence (AGI)
- A type of AI that can perform any intellectual task a human can.
- Artificial Intelligence (AI)
- The simulation of human intelligence in machines, enabling them to think and learn.
- Autonomous
- A machine that can perform its tasks without human intervention.
- Big Data
- Extremely large and complex datasets that require specialized tools to process.
- Chatbot
- A program designed to simulate human conversation through text or voice.
- Cognitive Computing
- AI systems that simulate human thought processes to solve complex problems, combining machine learning, natural language processing, and other AI technologies.
- Computer Vision
- An AI field that trains computers to interpret and understand visual information from the world, enabling machines to identify objects, analyze images, and process visual data in real-time.
- Data Mining
- The process of analyzing large datasets to find new patterns and insights.
- Deep Learning
- A subfield of machine learning that uses neural networks with many layers to learn from large amounts of data.
- Deep Neural Networks
- Neural networks with multiple hidden layers that can learn complex patterns and representations from data, forming the foundation of deep learning systems.
- Generative AI
- AI models that can create new and original content, such as text, images, or music.
- Hallucination
- A confident response from an AI that is not justified by its training data.
- Hidden Layers
- Intermediate layers in neural networks between input and output layers that process and transform data, enabling the network to learn complex patterns and relationships.
- Human Intelligence
- The cognitive abilities that humans possess, including reasoning, learning, problem-solving, creativity, and emotional understanding, which AI systems aim to replicate or augment.
- Large Language Model (LLM)
- A type of AI model trained on vast amounts of text data to understand and generate human-like language.
- Machine Learning (ML)
- A subset of AI that allows computers to learn from data and improve their performance over time without being explicitly programmed.
- Machine Learning Models
- Mathematical algorithms and statistical models that enable computers to learn from and make predictions or decisions based on data without being explicitly programmed for each task.
- Natural Language Processing (NLP)
- A field of AI that focuses on the interaction between computers and human language.
- Neural Network
- A computer system modeled after the human brain, used for tasks like speech and image recognition.
- Pattern Recognition
- The ability of AI systems to identify regularities, trends, and structures in data, enabling them to classify information and make predictions based on learned patterns.
- Prompt
- The input given to an AI model to guide its output.
- Real-Time Processing
- The ability of AI systems to process and respond to data immediately as it is received, enabling instant analysis and decision-making for time-sensitive applications.
- Specific Tasks
- Particular functions or activities that AI systems are designed to perform, such as image recognition, language translation, or data analysis, often excelling in narrow domains.
- Supervised Learning
- A type of machine learning where the model is trained on labeled data.
- Training Data
- Large amounts of data used to teach AI systems how to perform specific tasks, providing examples and patterns that the AI learns from to improve its performance.
- Unsupervised Learning
- A type of machine learning where the model finds patterns in unlabeled data.