AI Fundamentals

RAG-as-a-Service (RaaS) offers cloud-based retrieval-augmented generation models, combining search and language generation for enhanced AI-powered responses.

Real-Time AI Processing enables instant data analysis and immediate responses by AI systems, crucial for applications requiring low latency and fast...

Retrieval-Augmented Generation combines retrieval of relevant data with generative language models to produce accurate, context-aware, and informative text.

SearchGPT is an AI-enhanced search technology using GPT models to deliver more precise, natural language understanding and context-driven search results.

Self-Healing Integration enables automatic detection and resolution of integration failures, ensuring seamless and resilient inter-application connectivity.

Semantic Search improves search accuracy by understanding user intent and contextual meaning, delivering more relevant results than keyword-based systems.

Small Language Models are compact NLP models designed for efficient language tasks with fewer parameters and lower computational needs than large models.

Sora AI is an advanced AI platform enabling natural language understanding, automation, and intelligent insights across diverse industry applications and...

Synthetic Data Generation creates algorithmic artificial data mimicking real datasets for safe, scalable testing and training in AI and data science.

TPU (Tensor Processing Unit) is Google's specialized hardware accelerator designed to speed up machine learning tasks and deep learning model computations.

A Test is a procedure to evaluate and validate system functionality, quality, or performance, ensuring expected behavior and detecting defects early.

Test Data is the dataset used to evaluate system performance and accuracy, essential for validating models, software, or algorithms effectively.

Tokenization is the process of splitting text into smaller units called tokens, essential for natural language processing and text analysis tasks.

Tokenomics of Compute defines economic models and incentives for tokenized computational resources in decentralized systems, optimizing resource sharing.

Training data is the dataset used to teach machine learning models by example, enabling them to learn patterns and perform accurate predictions.

Transfer Learning applies pre-trained models to new tasks, boosting efficiency and accuracy when data is limited for the target problem.

Turing refers to Alan Turing's foundational concepts in computing, including the Turing Machine and Turing Test, pivotal in AI and computer science.

A vector database stores and searches high-dimensional vector embeddings, enabling efficient similarity queries for AI and machine learning data.

Vector embedding maps complex data like text or images into numeric vectors, enabling semantic analysis and AI-driven processing.

Zero-shot learning enables models to classify unseen categories without training examples by leveraging semantic knowledge and attribute-based reasoning.