Agentic AI
Agentic AI refers to systems that can independently plan, decide, and act in pursuit of defined goals. These systems are capable of adapting their actions based on feedback and environment, often with minimal human oversight.
Algorithm
An algorithm is a finite set of instructions used to solve problems or perform computations. In AI, algorithms power everything from learning patterns in data to making predictions and recommendations.
API (Application Programming Interface)
An API is a set of protocols that allows different software applications to communicate. APIs are commonly used to integrate AI services into existing platforms or workflows.
Artificial General Intelligence (AGI)
AGI is a theoretical form of AI capable of performing any cognitive task that a human can, across all domains. It remains a long-term goal in artificial intelligence research.
Artificial Intelligence (AI)
AI refers to technologies that mimic human cognitive processes like reasoning, learning, and problem-solving. It includes subfields such as machine learning, computer vision, and natural language processing.
Automation
Automation is the use of technology to perform tasks with limited or no human input. AI enhances automation by enabling systems to learn, adapt, and make decisions based on data.
AutoML (Automated Machine Learning)
AutoML tools automate the development of machine learning models, handling tasks such as feature selection and hyperparameter tuning, making AI more accessible to non-experts.
Bias (in AI)
Bias in AI refers to systematic errors in model outputs caused by imbalanced or flawed training data. Bias can result in unfair or inaccurate decisions and must be actively mitigated.
Chatbot
A chatbot is a software application that simulates human conversation via text or voice. Chatbots often use natural language processing to interpret queries and provide relevant responses.
ChatGPT
ChatGPT is a conversational AI developed by OpenAI using GPT (Generative Pre-trained Transformer) architecture. It generates human-like responses and is widely used for writing, coding, and customer support.
Classification
Classification is a supervised learning task where input data is categorized into predefined classes. It’s commonly used in email filtering, image tagging, and medical diagnosis.
Claude
Claude is a large language model developed by Anthropic, focused on safe and aligned behavior. It uses “constitutional AI” to prioritize helpful, honest, and harmless outputs.
Clustering
Clustering is an unsupervised learning technique that groups similar data points based on patterns. It’s often used for segmentation in marketing and exploratory analysis.
Cognitive Services
Cognitive services are prebuilt AI models offered via cloud platforms that handle tasks such as language translation, image recognition, and sentiment analysis through APIs.
Computer Vision
Computer vision enables machines to interpret visual information such as photos or video. It powers applications like facial recognition, object detection, and medical imaging.
Copilot (Microsoft Copilot)
Microsoft Copilot integrates generative AI into Microsoft 365 apps, enhancing productivity in Word, Excel, and Outlook through automated writing, analysis, and summarization.
Data Augmentation
Data augmentation involves modifying training data to increase its diversity. Techniques like flipping, cropping, or adding noise help models generalize better and avoid overfitting.
Data Labeling
Data labeling is the process of tagging raw data with categories to make it usable for supervised learning. Accurate labels are essential for model performance and reliability.
Data Pipeline
A data pipeline automates the flow of data through collection, cleaning, transformation, and storage. It’s essential for preparing data before it’s used in machine learning or analytics.
Deep Learning
Deep learning is a subset of machine learning that uses multi-layered neural networks to learn complex patterns. It excels at processing unstructured data like images, audio, and text.
Embedding
An embedding converts data—like text or images—into numerical vectors that preserve semantic relationships. Embeddings are used in search, recommendation engines, and natural language processing.
Explainability
Explainability refers to the ability to understand and interpret how an AI system reaches its decisions. It’s vital for trust, auditing, and regulatory compliance.
Feature Engineering
Feature engineering involves selecting, modifying, or creating input variables to improve a model’s accuracy. It’s a key part of the machine learning pipeline.
Fine-tuning
Fine-tuning is the process of adapting a pre-trained model to a specific task using a smaller dataset. It improves performance on domain-specific applications.
Foundation Model
A foundation model is a large model trained on broad data that can be adapted for many downstream tasks. GPT-4 and Claude are examples of foundation models used across industries.
Generative AI
Generative AI refers to systems that produce new content—text, images, audio, or code—by learning from patterns in training data. It powers tools like ChatGPT and image generators.
Gemini (Google)
Gemini is Google’s next-generation AI model, integrated into products like Gmail and Google Docs. It supports text, images, and code generation, and succeeds Google Bard.
Groq
Groq is a company that builds ultra-fast AI inference chips and infrastructure. It enables real-time response from large language models with extremely low latency for enterprise applications.
Hallucination (AI)
Hallucination occurs when AI generates plausible but false or fabricated information. It’s a known limitation of generative models and impacts trustworthiness.
Hyperparameter Tuning
Hyperparameter tuning is the process of selecting the best configuration settings for a model to maximize its performance, often using techniques like grid search or Bayesian optimization.
Inference
Inference in AI refers to using a trained model to generate predictions or outputs based on new data. It’s the operational phase of machine learning deployment.
Label (in ML)
A label is a known output or category assigned to a data point during supervised learning. Labels help models learn the correct relationship between inputs and desired outcomes.
Large Language Model (LLM)
A large language model is a deep learning model trained on massive text datasets to understand and generate human language. LLMs like GPT-4 or Claude are used for writing, coding, and reasoning tasks.
LLaMA (Meta)
LLaMA is a family of open-weight language models developed by Meta. Designed for transparency and efficiency, it allows researchers and developers to customize and deploy powerful AI models with fewer restrictions.
Machine Learning (ML)
Machine learning is a subfield of AI focused on algorithms that learn from data to improve performance over time. It includes supervised, unsupervised, and reinforcement learning techniques.
Mistral
Mistral is an AI company developing lightweight, open-weight language models optimized for performance and customization. Its models are gaining traction among developers seeking flexible alternatives to proprietary LLMs.
Model (AI Model)
An AI model is a mathematical representation of relationships in data, trained to perform tasks like classification or prediction. It’s the core of most machine learning systems.
Model Drift
Model drift occurs when the real-world data that an AI model encounters changes over time, reducing its accuracy. Drift requires ongoing monitoring and periodic retraining.
Natural Language Processing (NLP)
NLP is a field of AI focused on the interaction between computers and human language. It enables tasks like translation, summarization, and sentiment analysis.
Neural Network
A neural network is a machine learning model inspired by the human brain. It consists of layers of connected nodes and is particularly effective at detecting patterns in complex data.
Overfitting
Overfitting happens when a model learns the training data too well, including noise, and performs poorly on new data. It is a common issue that affects generalization.
Perplexity AI
Perplexity AI is an AI-powered search engine that combines language models with real-time web data to answer questions with citations. It bridges traditional search with conversational AI.
Pi (Inflection AI)
Pi is a conversational AI assistant created by Inflection AI. It emphasizes emotional intelligence and dialogue over utility, aiming to provide personal support and empathetic interaction.
Predictive Analytics
Predictive analytics uses historical and real-time data along with machine learning models to anticipate future outcomes. It helps in planning, forecasting, and decision-making.
Pretraining
Pretraining is the process of training a model on a general dataset before adapting it to a specific task. This gives the model foundational knowledge that enhances fine-tuning.
Prompt Engineering
Prompt engineering is the practice of designing inputs that guide language models toward useful or accurate outputs. It’s key for getting consistent results from tools like ChatGPT or Claude.
Recommendation Engine
A recommendation engine is an AI system that suggests products or content to users based on behavior, preferences, or similarities. It powers personalization in platforms like Netflix and Amazon.
Reinforcement Learning
Reinforcement learning is a type of machine learning where an agent learns by taking actions in an environment and receiving rewards or penalties based on performance.
Retrieval-Augmented Generation (RAG)
RAG combines generative AI with external data sources. Before responding, the model retrieves relevant documents to improve accuracy and reduce hallucinations.
Robotic Process Automation (RPA)
RPA involves software bots that automate repetitive digital tasks by mimicking user actions. When combined with AI, it enables more intelligent and dynamic workflows.
Sentiment Analysis
Sentiment analysis uses natural language processing to detect emotional tone in text. It’s commonly used to analyze customer feedback, reviews, or social media.
Supervised Learning
Supervised learning trains a model on labeled data so it can learn the relationship between inputs and outputs. It’s widely used in classification and regression tasks.
Token
A token is a unit of language (word, subword, or character) processed by AI models. Tokenization helps break down text for more efficient model input and analysis.
Training Data
Training data is the labeled or structured data used to teach an AI model. High-quality training data is critical to building accurate and unbiased models.
Transfer Learning
Transfer learning reuses a model trained on one task for another related task. It accelerates AI development by reducing the need for large task-specific datasets.
Unsupervised Learning
Unsupervised learning trains models on data without labeled outputs. It allows systems to find patterns or groupings, often used in clustering and anomaly detection.
Vector Database
A vector database stores and retrieves data in high-dimensional vector form, typically used for semantic search, recommendations, and working with embeddings.
Workflow Automation
Workflow automation uses AI or scripting tools to streamline multistep business processes. It reduces manual labor and increases efficiency across operations.