Understanding generative AI and its applications
Generative AI is a type of artificial intelligence technology that has the ability to produce various types of content, such as text, imagery, audio, and synthetic data. In this lesson, we will delve deeper into the concept of generative AI and explore its applications.
Dr. Gwendolyn Stripling, an artificial intelligence technical curriculum developer at Google Cloud, introduces generative AI as a discipline within computer science that focuses on the creation of intelligent systems capable of reasoning, learning, and acting autonomously. It aims to build machines that can think and act like humans.
Discriminative models vs. generative models
To understand generative AI better, it is important to differentiate between discriminative models and generative models. Discriminative models are used for classification or prediction tasks. They learn the relationship between features of data points and their corresponding labels. Once trained, these models can predict the label for new data points.
On the other hand, generative models generate new data instances based on a learned probability distribution of existing data. They focus on capturing the underlying structure of the data to generate new content. For example, a generative language model can take input text and output more text, an image, audio, or even decisions.
Supervised, unsupervised, and semi-supervised learning with generative models
Generative models can utilize different learning approaches, including supervised, unsupervised, and semi-supervised learning.
Supervised learning involves training a model on labeled data, where each data point has a corresponding tag or label. This enables the model to learn from past examples and make predictions based on new data.
In contrast, unsupervised learning deals with unlabeled data, where there are no predefined tags or labels. The goal of unsupervised learning is to discover patterns and groupings within the data, without specific guidance on what to look for.
Semi-supervised learning combines elements of both supervised and unsupervised learning. It involves training a model on a small amount of labeled data and a large amount of unlabeled data. The labeled data helps the model learn the basic concepts of the task, while the unlabeled data aids in generalization to new examples.
Understanding these different learning approaches is crucial for grasping the fundamentals of generative AI.
Introduction to large language models
Large language models play a significant role in generative AI. These models are a subset of deep learning, employing artificial neural networks to process complex patterns.
Artificial neural networks, inspired by the human brain, consist of interconnected nodes or neurons that learn to perform tasks by processing data and making predictions. Deep learning models, including large language models, typically have multiple layers of neurons, enabling them to learn intricate patterns compared to traditional machine learning models.
Large language models can process both labeled and unlabeled data, making them suitable for various learning scenarios. They can generate novel combinations of text, providing natural-sounding language outputs.
Generative AI, as a discipline within artificial intelligence, focuses on creating intelligent systems that can generate new content based on learned patterns. Discriminative models classify or predict labels, while generative models generate new instances of data.
Generative models can utilize supervised, unsupervised, or semi-supervised learning approaches to train on labeled or unlabeled data. Large language models, a subset of deep learning, excel at processing complex patterns in text.
By understanding these concepts, you are building a strong foundation for further exploration of generative AI and its practical applications.