The central application of an artificial intelligence or AI system is that it can make decisions based on probabilities and predictions. The predicting capabilities are dependent on the size or parameters of the specific AI model used in an AI system. Developing this model involves training and the deployment of this model produces inference. Understanding the difference between training and inference is essential to understanding the basics of developing and developing an artificial intelligence model.
AI Training vs AI Inference: Understanding the Difference Between the Two Main Phases of Developing and Deploying Artificial Intelligence Models
Machine learning or its more advanced and specific subset called deep learning works in two main phases. These are the training phase and the inference phase. Note that machine learning is one of the subfields of artificial intelligence that equips a particular AI system with the capabilities to perform tasks without using explicit instructions. This is done through the development and use of algorithms that enable the system to process and analyze data without being programmed. Take note of the difference between AI training and AI inference to understand better how machine learning or deep learning works and how an AI system can make decisions:
What is Training in Artificial Intelligence?
Training in artificial intelligence is the process of developing an AI model that generally involves teaching it how to perform a specific task or a set of specific tasks. This specifically involves feeding the model with a large dataset of examples that are collectively referred to as the training data. The model uses this training data to learn patterns and relationships.
For example, when it comes to developing a large language model used for generative applications such as a chatbot, developers train the model with written texts that can include a knowledge base of an organization or public documents and reference materials. This is the same process behind the development of popular chatbots such as ChatGPT from Open AI, Google Gemini from Google, and Copilot from Microsoft.
The same process is used in developing various AI models for specific tasks. Examples include understanding and processing natural language for speech-to-text conversion or language translation, computer vision applications such as image recognition and object detection, and recommendation systems used in search engines and digital advertisements.
It is important to note that there are various approaches to training an AI model. The most common ones are supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves learning from labeled data while unsupervised learning learns from unlabeled data with no predefined output or target value for each input. Reinforcement learning involves learning from its own actions and external feedback.
What is Inference in Artificial Intelligence?
Nevertheless, once trained, the AI model is ready for deployment. A deployed AI model embedded in an AI system can make decisions based on probabilities or predictions on new data. This is called inference. Inference in artificial intelligence is specifically the ability of an AI model or an AI system to make predictions from novel data.
Consider an image recognition model as an example. This model is trained to recognize cats and dogs in images. Nevertheless, once deployed, inference transpires when this model can analyze a new image and tell its user that it contains either a cat or a dog. Another example is a multimodal large language model that can provide a descriptive analysis of images or graphs while also producing new graphs or images based on a descriptive prompt.
Another example is in generative artificial intelligence applications. A particular generative AI model can generate new content or data based on user-provided input or prompts. This specifically involves feeding the model with a prompt for it to infer the most probable next token or sequence tokens for generating user-intended content or data.
Inference is an essential part of an AI application because it allows a specific AI model and the corresponding AI system to solve real-world problems. It specifically enables a system to perform tasks that require human intelligence, such as recognizing images, understanding natural language, or making decisions. Inference can also help humans by providing insights, recommendations, or feedback based on the analysis of data.
Summary and Additions: A Concise Discussion of the Difference Between Training and Inference in the Development and Deployment of AI Models
Training and inference are two different processes in AI. Training is when an AI model learns from data and updates its parameters to find patterns or rules that can map the inputs to the outputs. Inference is when an AI system uses a trained AI model to make predictions on new data without human guidance or intervention. Training usually requires more time, resources, and data than inference, but it can improve the accuracy and performance of the model. Inference usually requires less time, resources, and data than training, but it can enable an AI system to perform tasks that normally require human intelligence.
