What is Generative AI?
Generative artificial intelligence (AI), also known as generative AI or GenAI, is a type of AI that can create new content like text, images, videos, music, and more. It can learn from data and generate new data instances, unlike traditional AI systems that follow predetermined rules.
LLMs (Large Language Models) are a subset of GenAI that are designed to understand and generate human-like text. These generative AI models are trained on vast amounts of text data and use deep learning techniques to learn the patterns and structures of language. LLMs can perform a wide range of natural language processing tasks, such as text generation, translation, summarization, and question answering.
examples of generative AI models:
ChatGPT : A generative AI model that can generate text
DALL-E : A generative AI model that can generate images
Generative modeling is a type of statistical modeling that is used to generate new data points that are similar to a given set of data. It involves learning the underlying distribution of the data and then sampling from this distribution to create new instances. Generative models can be used for a variety of tasks, including:
- Data Augmentation: Generating additional training data to improve the performance of machine learning models.
- Image and Text Generation: Creating new images, text, or other types of data that resemble the original data.
- Anomaly Detection: Identifying data points that do not fit the learned distribution, which can be useful for detecting outliers or fraudulent activity.
Generative Models vs Discriminative Models
| Generative Modeling | Discriminative Modeling | |
|---|---|---|
| Purpose | Learns the joint probability distribution of the input data and the labels | Learns the conditional probability of the labels given the input data |
| Understand the data distribution and can create new data points | Focus on making accurate predictions and classifications | |
| Function | Can generate new data points similar to the training data | Focuses on distinguishing between different classes |
| Use Cases | Data generation, anomaly detection, unsupervised learning | Classification, regression, supervised learning |
In summary, generative models are about creating data, while discriminative models are about making decisions based on data.
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