Generative AI and traditional AI (or machine learning) represent different paradigms or applications of artificial intelligence. Often it is misunderstood what the true difference is between the two. Here's a breakdown of the differences:
- Generative AI: As the name suggests, the primary aim of generative AI is to generate data. This could be in the form of images, text, music, etc. Models like GANs (Generative Adversarial Networks) and certain variants of autoencoders are classic examples. Generative models learn the underlying distribution of the input data to produce new, previously unseen data.
- Traditional AI/Machine Learning: The purpose of traditional machine learning is typically to make predictions or classifications based on input data. For example, a supervised learning algorithm might predict housing prices based on features of a house, or it might classify emails as spam or not spam.
- Generative AI: Generative models often require specialized training mechanisms. For example, GANs involve two networks: a generator and a discriminator, which are trained together in a sort of "game" where the generator tries to produce fake data that the discriminator can't distinguish from real data.
- Traditional AI/Machine Learning: Training often involves feeding labeled data into an algorithm, adjusting the model's parameters to minimize prediction error. Algorithms like linear regression, decision trees, and neural networks fall into this category.
- Generative AI: Produces new, previously unseen data samples.
- Traditional AI/Machine Learning: Typically outputs a prediction, classification, or some other form of structured information.
- Generative AI: Art creation (e.g., music, paintings, etc.), drug discovery (by generating molecular structures), data augmentation, creating virtual environments, etc.
- Traditional AI/Machine Learning: Fraud detection, recommendation systems, image classification, natural language processing tasks like translation and sentiment analysis, among countless others.
- Generative AI: Many generative models, especially deep generative models, require substantial amounts of data to generate high-quality outputs.
- Traditional AI/Machine Learning: The amount of data required varies based on the task and complexity of the model. Some algorithms can work well with small datasets, while others, especially deep learning models, may require large datasets.
- Generative AI: Generative Adversarial Networks (GANs), Variational Auto Encoders (VAEs), certain types of Reinforcement Learning agents, etc.
- Traditional AI/Machine Learning: Linear Regression, Decision Trees, Support Vector Machines, Convolutional Neural Networks (for tasks like image classification), etc.
In summary, while both generative AI and traditional AI operate under the umbrella of artificial intelligence and machine learning, they serve different purposes, utilize different training techniques, and produce different types of outputs. They are totally different animals in the same jungle and also require a different approach in terms of Governance and Law. Let's see what the EU AI Act will bring for Generative AI.
