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AI is revolutionizing manufacturing, but do you know the essential terms? This quick guide breaks down key technologies driving the future of industrial innovation.

As manufacturing leaders navigate Manufacturing 4.0’s complexities, artificial intelligence has emerged as a critical enabler of digital transformation. With AI evolving rapidly, staying on top of the latest terms and technologies can feel overwhelming.

To cut through the complexity, we’ve put together a quick guide to key AI concepts shaping manufacturing today. Whether you’re exploring predictive maintenance, product development, or human-machine collaboration, understanding these AI technologies will give you a clearer picture of their potential impact on your operations.

• Agentic AI – AI systems capable of autonomous decision-making and action-taking to accomplish goals, often integrating planning, reasoning, and adaptability to dynamic environments.
• Causal AI – AI that goes beyond correlation-based learning to understand cause-and-effect relationships, improving decision-making, diagnostics, and scientific discoveries.
• Edge AI – AI that runs on local devices rather than centralized cloud servers, enabling real-time processing and low-latency applications in areas like IoT, industrial automation, and smart devices.
• Generative AI (GenAI) – AI models that create new content, such as text, images, audio, or code, based on training data. Examples include ChatGPT for text and DALL·E for images.
• Large Language Models (LLM) / Small Language Models (SLM) – LLMs are advanced AI models trained on vast amounts of text to generate human-like language, while SLMs are smaller, more efficient models optimized for specific tasks with lower computational requirements.
• Machine Learning (ML) – A subset of AI that enables systems to learn from data, identify patterns, and make decisions with minimal human intervention. It includes supervised, unsupervised, and reinforcement learning techniques.
• Natural Language Processing (NLP) – AI focused on enabling computers to understand, interpret, and generate human language, allowing for applications like chatbots, translation services, and sentiment analysis.
• Physical AI – AI integrated into physical systems, such as robots or autonomous vehicles, enabling interaction with the physical world through sensors, actuators, and adaptive control.
• Vision Systems – AI that processes and interprets visual data from the world, such as images or videos, enabling tasks like facial recognition, object detection, and quality control in manufacturing.