Watch and Learn with Us | Introduction to Generative AI

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Definition of Generative AI

  • Generative AI creates new content based on learned data. [00:40]

  • Generative language models can generate new text based on training data. [05:48]

Difference between AI and Machine Learning

  • 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]

Types of Machine Learning Models

  • Supervised models predict based on labeled data, while unsupervised models group data without labels. [02:13]

Generative and Discriminative Models

  • Discriminative models classify data, while generative models create new data instances. [06:46]

Generative AI Applications

  • 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]

Takeaways


  • 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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