To understand the distinction between generative AI and other types of AI, it's helpful to view AI models generally in terms of their primary function. In broad strokes, AI models are usually designed to either classify/analyze data (discriminative models) or generate new data (generative models). Here's a breakdown:
- Generative AI:
- Function: Generate new data samples that resemble a given dataset.
- Generative Adversarial Networks (GANs): Composed of a generator (creates data) and a discriminator (evaluates data). The generator tries to produce data that the discriminator can't distinguish from real data.
- Variational Autoencoders (VAEs): Generate new data by learning a probabilistic mapping between the data space and a latent space.
- Models like GPT (Generative Pre-trained Transformer) that can generate coherent sequences of text.
- Applications: Creating artworks, generating music, producing realistic images, drug discovery, text generation, etc.
- Function: Differentiate between different types of data or predict specific outcomes based on input data.
- Support Vector Machines (SVM): Used for classification tasks.
- Convolutional Neural Networks (CNNs): Primarily used for image classification and recognition.
- Models like BERT (Bidirectional Encoder Representations from Transformers) that are designed to understand the context in text for tasks like classification, entity recognition, etc.
- Applications: Image classification, sentiment analysis, medical diagnosis, spam detection, etc.
- Function: Learn by interacting with an environment to maximize a reward over time.
- Examples: Q-learning, Deep Q Networks (DQN), Proximal Policy Optimization (PPO), etc.
- Applications: Game playing (like AlphaGo by DeepMind), robotics, certain trading strategies, etc.
- Function: Predict continuous values based on input data.
- Examples: Linear regression, polynomial regression, etc.
- Applications: Predicting stock prices, weather forecasting, real estate price prediction, etc.
- Generative AI models focus on understanding the underlying structure and distribution of the data to create new data samples. Their primary aim is not to classify or label existing data but to produce novel data.
- Discriminative models, on the other hand, emphasize understanding the boundary or difference between classes or labels in the dataset. They are more concerned with correctly identifying or predicting labels for given data points.
While generative and discriminative models represent two prominent categories of AI models, the broader landscape of AI encompasses a range of models, each suited to specific tasks and applications.