Artificial Intelligence

Artificial intelligence (AI) refers to the simulation of human intelligence in machines designed to think, learn and problem-solve. AI systems use algorithms and models to process data, recognize patterns and make decisions with minimal human intervention. AI encompasses a wide range of technologies and components, including machine learning, natural language processing, robotics and computer vision. It aims to perform tasks typically requiring human cognition, such as understanding language, recognizing images and playing games. Applications of AI span various industries, from healthcare and finance to autonomous vehicles and customer service, enhancing efficiency, accuracy and decision-making capabilities across different domains.

A Large Language Model (LLM) is a type of artificial intelligence that can process and generate natural language, such as text or speech. LLMs are trained on huge quantities of data, such as web content, published works and periodicals. From that, the system learns the patterns and rules of language and can effectively predict words to complete sentences, answer questions, summarize text and compose lengthy responses. Examples include GPT-3 and BERT, used in applications such as chatbots and automated content creation. In addition, LLMs can be used to help programmers and developers write code. With it, they can write functions upon request, help users to review code to identify and solve faults or given some code as a starting point, they can finish writing a program or command.

Generative AI (GenAI) is a broader AI category that aims to produce original content across multiple media, including text, images and music. It encompasses LLMs and extends to models like DALL-E for image creation and Jukedeck for music generation. While the goal is original content, because GenAI sources information from existing data sets, outputs may include verbatim lifts from sources or fabricated responses, known as hallucinations.

Retrieval-Augmented Generation combines generative AI with contextually relevant data to improve accuracy and reliability. It reduces AI "hallucinations" by grounding responses in a structured knowledge graph and validating them against a comprehensive knowledge model. Key components include contextual data enrichment, knowledge graphs, prompt enhancement and response validation.

RAG improves accuracy, speed, cost-efficiency, scalability and security in AI applications, making it ideal for customer service, knowledge management and research and development. The Progress Data Platform supports RAG by integrating various data sources, harmonizing, classifying and contextualizing the data and maintaining high data quality and governance.

For more details, and why our Semantic RAG approach to generative AI can help you in your organization, check the full blog here.

The "Human-in-the-Loop" (HITL) concept integrates human oversight to provide accurate, reliable and repeatable data classification and AI outcomes. This approach leverages human judgment to review and validate AI outputs, improving quality control and addressing complex scenarios that AI alone might struggle to address. HITL is vital to many enterprise environments, where human intervention can prevent errors, ensure governance and align decisions with societal values. By combining the strengths of both humans and machines, HITL creates a more adaptable, trustworthy and effective AI system, enabling technology to serve human needs responsibly and efficiently.