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Artificial intelligence is transforming the world, but have you ever wondered what actually happens behind the scenes when you ask ChatGPT a question or use an AI-powered application? The answer involves something called AI inference. In this video, I explain what AI inference is, how it works and why it's becoming one of the most important parts of the artificial intelligence industry. When we talk about developing AI, much of the attention focuses on training increasingly powerful models using enormous datasets and vast amounts of computing power. But training is only one part of the process. Once an AI model has been trained, it needs to put its capabilities to work. That's where inference comes in. AI inference is the process of using a trained model to generate predictions, produce responses, recognise patterns or complete tasks based on new inputs. Every time you ask a large language model a question, use an AI assistant, generate an image or interact with an intelligent application, inference is taking place. And as AI adoption accelerates, the economics and efficiency of inference are becoming increasingly significant. For businesses, inference affects several critical factors, including the speed of AI responses, the cost of processing requests, the infrastructure required to support applications and the ability to scale AI services to thousands or millions of users. It's also an important reason why demand for AI chips, data centres, computing infrastructure and energy continues to grow. As more organisations deploy generative AI and increasingly autonomous AI agents, the volume of inference workloads could become an even more important consideration. This creates a fascinating challenge for the technology industry. How do we make AI systems not only more intelligent, but also faster, more efficient and more affordable to operate? Innovations in specialised AI hardware, model optimisation and more efficient computing architectures could play a major role in answering that question. Understanding the difference between AI training and inference is essential for anyone interested in how artificial intelligence works and where the technology is heading. Training is how an AI model develops its capabilities. Inference is how those capabilities deliver value in the real world. 💬 Do you think making AI cheaper and faster to run will be more important than developing increasingly powerful models? Share your thoughts in the comments. Subscribe for more insights into artificial intelligence, emerging technology, AI infrastructure, business transformation and the future of work. #AI #ArtificialIntelligence #AIInference #MachineLearning #GenerativeAI #ChatGPT #LargeLanguageModels #AIInfrastructure #AIChips #FutureOfAI #TechExplained

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