"AI is an entire field of study like physics, and machine learning is a subfield of AI. Deep learning is a subset of machine learning, and large language models fall under deep learning. Generative and discriminative models are further subdivisions, with ChatGPT and Google Bard existing at the intersection of generative models and LLMs."
"Machine learning uses input data to train a model that makes predictions on unseen data. Supervised learning models use labeled data, while unsupervised models use unlabeled data. In supervised learning, the model compares predictions to training data and closes gaps. Unsupervised learning identifies natural groupings in raw data without labels."
"Deep learning uses artificial neural networks inspired by the human brain. More layers create more powerful models. Semi-supervised learning combines small amounts of labeled data with large amounts of unlabeled data. For example, banks label 5% of transactions as fraudulent or legitimate, then apply learnings to the remaining 95%."
"Discriminative models learn relationships between data labels and classify new data points. Generative models learn patterns in training data and generate new content based on those patterns. If output is a number, classification, or probability, it's not generative AI. If output is text, speech, image, or audio, it is generative AI."
AI is a broad field of study, with machine learning as a subfield and deep learning as a subset of machine learning. Large language models are part of deep learning. Generative and discriminative models are further subdivisions, with ChatGPT and Google Bard positioned at the intersection of generative models and LLMs. Machine learning trains models using input data to make predictions on unseen data. Supervised learning uses labeled data, while unsupervised learning uses unlabeled data to find natural groupings. Deep learning uses artificial neural networks, where more layers can increase model power. Semi-supervised learning combines small labeled and large unlabeled datasets. Discriminative models classify by learning label relationships, while generative models learn patterns and produce new content. Output type helps distinguish generative AI from non-generative AI. Generative model types include text-to-text, text-to-image, text-to-video, text-to-3D, and text-to-task. LLMs are pre-trained on large datasets and then fine-tuned for specific purposes.
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