AI Glossary

Step into the world of AI terminologies with this glossary. This is your guide through the language of Artificial Intelligence terms.

Term	Description	Explanation
AI Training	The process of teaching an AI model	AI training involves feeding a model with data to learn patterns, enabling it to make accurate predictions or classifications.
Algorithm	A set of rules designed to solve a specific problem	In AI, algorithms are core instructions guiding machine learning models in decision-making or tasks.
Artificial Intelligence (AI)	The simulation of human intelligence in machines	AI enables machines to perform tasks that typically require human intelligence, such as learning, and problem-solving.
Bias in AI	Systematic errors or unfairness in AI models, often reflecting societal biases	Bias in AI can lead to discriminatory outcomes; addressing it is crucial for responsible AI development.
Bioinformatics	Application of AI in biological data analysis	Bioinformatics utilizes AI to analyze biological data, aiding tasks like DNA sequencing/drug discovery in the pharma industry.
Chatbot	Computer program designed for human conversation	Chatbots use natural language processing and machine learning to understand and respond to user queries.
Computer Vision	The field of AI focused on enabling machines to interpret and understand visuals	Applications include image recognition, object detection, and facial recognition.
Data Infrastructure	Systems and tools for managing and processing data	Data infrastructure includes databases, storage systems, and frameworks that handle the storage/processing of data.
Deep Learning	Subset of machine learning with deep neural networks	It involves training neural networks with multiple layers, enabling them to autom. learn hierarchical representations of data.
Edge Computing	Processing data near the source of generation, reducing reliance on the cloud	Edge computing is essential in AI applications requiring real-time processing, minimizing latency and improving efficiency.
Elastic Fabric Adapter (EFA)	AWS networking technology for HPC workloads	EFA provides low-latency comms between Amazon EC2 instances, crucial for HPC tasks.
Exaflops	A speed measure: one quintillion floating-point operations per second	Exaflops represent a high level of computing power, often used to quantify the performance of supercomputers.
GPU (Graphics Processing Unit)	A specialized processor for graphics rendering	GPUs are used in AI for parallel processing, accelerating tasks like training deep neural networks.
Generative AI	AI systems capable of creating new content	Generative AI can produce new data or images, based on patterns and info it has learned from existing data.
High-Performance Computing (HPC)	Computing with high processing power	HPC involves the use of powerful computers to handle complex tasks and process large datasets quickly.
Hyperparameter	Configuration settings external to the model influencing its learning process	Examples include learning rates and regularization factors, impacting a model's performance but not learned from data.
Inference	Applying knowledge gained during training	Inference is when a trained AI model makes predictions or decisions based on new, unseen data.
Natural Language Processing (NLP)	The ability of machines to understand, and generate human language	NLP is crucial for applications like chatbots, language translation, and sentiment analysis.
Neural Network	Computational model inspired by the human brain	Neural networks consist of interconnected nodes (artificial neurons) that process information, mimicking the human brain.
Nitro System	AWS technology for advanced virtualization	This system enhances virtualization performance, providing hardware for critical components like networking/storage.
Omniverse	NVIDIA's platform for 3D simulation and AI	Omniverse allows collaborative and realistic 3D simulation, used by industries like robotics for optimizing real-world scenarios.
Reinforcement Learning	Type of machine learning where an agent learns by receiving rewards/penalties	Reinforcement learning is used when AI must learn to interact with an environment to achieve specific goals.
Supercomputer	Extremely powerful computer for advanced tasks	Supercomputers are designed to process massive amounts of data and perform complex calculations, often used in research.
Supervised Learning	Machine learning where the model is trained on labeled data with known outputs	Used for classification and regression, the model learns from labeled examples.
Tensor Core	Specialized processing unit for tensor operations	Tensor Cores enhance the efficiency of deep learning tasks, performing math ops used in neural network training.
Transfer Learning	Leveraging knowledge gained from one task to improve performance on another	Accelerates model training and improves accuracy, especially when labeled data is limited.
Unsupervised Learning	Machine learning where the model finds patterns in unlabeled data	Commonly used for clustering and dimensionality reduction tasks without explicit guidance.
Virtualization	Creating a virtual version of a resource	In the context of AI, virtualization may involve creating virtual environments for testing and optimizing AI algorithms.