While there are many types of AI it may be helpful to be aware of some categories of AI. Two terms worth being a bit more familiar with are ‘Machine Learning’ and ‘Deep Learning’. Machine Learning represents a subset of AI and Deep Learning is a subset of Machine Learning, meaning that all Deep Learning AIs are Machine Learning AIs which are all examples of Artificial Intelligence, but not all AIs fit in the Machine Learning category and not all Machine Learning AIs are Deep Learning AIs.
Machine Learning refers to AIs which can be autonomously trained rather than being explicitly programmed. These AIs may be given labeled data or may be told if their output is correct or not. They may be given the rules of a game and be left to develop strategies that let the AI win. An AI might correctly produce the correct output, but understanding the process the AI uses might be difficult and may be only true for the data it is trained on.
Deep learning uses a neural network and more processing power to guide its own development, identifying errors and adjusting its strategy. This allows for greater automation of the training process and can allow the model to process a wider range of uncategorized data. Deep learning has enabled large AI models like ChatGPT, Claude, and Bard to work the way they do.