AI Glossary of Terms & Definitions

An ML method where the model identifies patterns in unlabeled data, applied in clustering manufacturing defects without prior categorization.

The Turing Test, proposed by British mathematician and computer scientist Alan Turing in his 1950 paper "Computing Machinery and Intelligence," is a method to evaluate a machine's ability to exhibit intelligent behavior indistinguishable from that of a human. In this test, a human evaluator engages in a text-based conversation with both a human and a machine, without knowing which is which; if the evaluator cannot reliably distinguish the machine from the human based on the responses, the machine is said to have passed the test, suggesting it possesses artificial intelligence. The Turing Test remains a foundational benchmark in AI development, though it has evolved to include debates about its relevance given modern AI capabilities like natural language processing and multimodal systems.

An ML approach where a model is trained on labeled data (e.g., input-output pairs) to make predictions, commonly used in quality assurance in manufacturing.

An AI technique where an agent learns by interacting with an environment, receiving rewards or penalties, used in optimizing robotic assembly lines.

A computational model inspired by the human brain, consisting of interconnected nodes (neurons) that process data, foundational to deep learning applications in 2025 AI systems.

The AI field focused on enabling machines to understand, interpret, and generate human language, seen in chatbots like Grok and virtual assistants.

In artificial intelligence, multimodal systems refer to advanced models or frameworks capable of processing and integrating multiple types of data or input modalities—such as text, images, audio, and video—simultaneously to generate more comprehensive and context-aware outputs.

These systems, exemplified by models like xAI's Grok, leverage techniques such as transformers and cross-modal learning to combine information from diverse sources, enabling applications like real-time translation with visual cues, automated video analysis, or interactive chatbots that respond to both voice and gestures.

This approach enhances AI's understanding and interaction with the world, making it more versatile and human-like compared to single-modal systems focused on one data type.

A subset of AI where algorithms enable computers to learn from data and improve over time without explicit programming, used in manufacturing for predictive maintenance and quality control.

AI that creates new content (e.g., text, images, music) based on learned patterns, with models like DALL·E and ChatGPT driving creative industries

The study and application of principles to ensure AI systems are fair, transparent, and unbiased, a growing focus amid regulations on data privacy.

AI processing performed on local devices (e.g., factory sensors) rather than cloud servers, improving speed and security, a trend in manufacturing.

A specialized ML technique using neural networks with many layers to analyze complex data (e.g., images, speech), powering advancements like autonomous vehicles and xAI’s multimodal models.

An AI domain enabling machines to interpret visual data from the world, critical for defect detection and autonomous navigation in factories.

Extremely large datasets that AI processes to uncover insights, essential for training models like those used in supply chain forecasting.

The simulation of human intelligence processes by machines, including learning, reasoning, problem-solving, and perception, enabling systems like xAI’s Grok to perform tasks from image recognition to natural language understanding.