Last week, we went over some basics of Artificial Intelligence (AI) using Ollama, Llama3, and some custom code. Artificial intelligence (AI) encompasses a broad range of technologies designed to enable machines to perform tasks that typically require human intelligence. These tasks include understanding spoken or written language, recognizing visual patterns, making decisions, and providing recommendations. Machine learning (ML) is a specialized subset of AI that focuses on developing systems that improve their performance over time without being explicitly programmed. Instead, ML algorithms analyze and learn from large datasets to identify patterns and make decisions based on these insights. This learning process allows ML models to make increasingly accurate predictions or decisions as they are exposed to more data.
A few months ago, I added Liner to the resource page of my website. It allows you to easily train an ML model so that you can do image, text, audio, or video classification, object detection, image segmentation, or pose classification. I created “Is this Joe or Not Joe?” using that tool. TensorFlow.js is running client-side with a model that is trained on a half dozen examples of photos that are Joe and a half dozen examples of photos that are not Joe. You can supply a photo and get a prediction if Joe is in the image or not. You can always retrain the existing model with more examples. That is an example of machine learning.
So, you can think of ML as a subset of AI and Deep Learning (DL) as a subset of ML.
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