Aug 10 • General discussion
Say What?? AI Inference
AI model inference is the process where a trained artificial intelligence model uses its knowledge to make predictions or draw conclusions from new, previously unseen data. It is the operational phase of AI, where the model applies what it learned during training to real-world situations.[1][4][5][7]
The AI Lifecycle: Training vs. Inference
The lifecycle of a machine learning model consists of two main phases: training and inference.[4][8]
- Training This is the learning phase. An AI model is "trained" by processing vast amounts of labeled data to learn to recognize patterns, relationships, and features within that data. For example, a model designed to identify spam emails is fed millions of emails that are already labeled as "spam" or "not spam". This process builds the model's knowledge base, which is stored as parameters or "weights".[3][6][9][4]
- Inference This is the application phase. Once trained, the model is deployed to perform its designated task on live, real-world data it has never encountered before. It uses its stored knowledge to "infer" or deduce an outcome. For example, when a new email arrives, the trained spam model analyzes its content and characteristics to predict whether it is spam. This is considered the "moment of truth" for an AI model.[6][8][9][3]
Think of training as a student studying for an exam by reviewing course materials, while inference is the student taking the exam and applying that knowledge to answer new questions.[3]
Examples of AI Inference
AI inference is at the core of many modern technologies:
- Generative AI Large language models (LLMs) like ChatGPT use inference to predict the next most likely word in a sequence, allowing them to generate coherent sentences and paragraphs.[3]
- Autonomous Vehicles A self-driving car uses inference to identify a stop sign on a road it has never driven on by recognizing patterns learned from millions of images of stop signs during training.[1]
- Facial Recognition A model trained on millions of facial images can infer an individual's identity in a new photo by identifying features like eye color and nose shape.[2]
- Fraud Detection Banks use AI to analyze credit card transactions in real-time and infer whether a transaction is likely fraudulent based on learned patterns.[3]
Why Inference is Important
Inference is what makes AI practical and valuable. It is the process that allows a model to be put into action to solve real-world problems. Without inference, an AI model's knowledge would remain static and unusable on new information. The ability to analyze live data and make accurate, real-time predictions is crucial for applications in healthcare, finance, engineering, and customer service.[5][4][3]
Up to 90% of the computational cost and energy consumption of an AI model's lifecycle occurs during the inference phase, as it is used repeatedly by end-users, unlike the one-time training process. This makes optimizing inference for speed and efficiency a major focus for developers.[8][6]
[1] https://www.cloudflare.com/learning/ai/inference-vs-training/ [2] https://www.ibm.com/think/topics/ai-inference [3] https://www.oracle.com/artificial-intelligence/ai-inference/ [4] https://www.arm.com/glossary/ai-inference [5] https://www.redhat.com/en/topics/ai/what-is-ai-inference [6] https://research.ibm.com/blog/AI-inference-explained [7] https://www.hopsworks.ai/dictionary/model-inference [8] https://hazelcast.com/foundations/ai-machine-learning/machine-learning-inference/ [9] https://www.youtube.com/watch?v=XtT5i0ZeHHE [10] https://developer.nvidia.com/topics/ai/ai-inference
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Say What?? AI Inference
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