Generative AI creates new content based on learned data. [00:40]
Generative language models can generate new text based on training data. [05:48]
AI is a discipline dealing with intelligent agents, while machine learning is a subfield of AI training models from data. [01:19]
Supervised models use labeled data, while unsupervised models work with unlabeled data. [02:13]
Supervised models predict based on labeled data, while unsupervised models group data without labels. [02:13]
Discriminative models classify data, while generative models create new data instances. [06:46]
Generative AI applications include text-to-text, text-to-image, and text-to-video models. [16:01]
Generative AI can help with code generation, text analysis, and more complex tasks. [18:49]
Machine learning is a subfield of AI, where a program or system trains a model from input data to make useful predictions from new, never-before-seen data drawn from the same source.
Machine learning models can be supervised or unsupervised, with the key difference being whether the data comes with labels or not.
Generative AI is a subset of deep learning, which is a type of machine learning that uses artificial neural networks to process complex patterns.
Generative models generate new data instances based on a learned probability distribution of existing data, while discriminative models discriminate between different kinds of data instances.
Generative AI models can be trained on both labeled and unlabeled data using supervised, unsupervised, and semi-supervised methods.
Large language models are a subset of deep learning and can generate new content based on what they have learned from existing content.
Generative AI models can take various forms, such as text-to-text, text-to-image, text-to-video, text-to-3D, and text-to-task models.
Foundation models are large AI models pre-trained on a vast quantity of data, designed to be adapted or fine-tuned to a wide range of downstream tasks.
Note: above summary is generated using JustRecap.it.
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