Generative artificial intelligence

Generative AI is a type of artificial intelligence that can create new content, such as text, images, music, audio, and videos, using generative models. Generative models are machine learning models that learn the patterns and structure of a given dataset and then generate new data that has similar characteristics. For example, a generative model trained on a dataset of human faces can generate new faces that look realistic but do not belong to any real person. There are different types of generative models, such as variational autoencoders (VAEs), generative adversarial networks (GANs), autoregressive models, and transformers. Each of these models has its own advantages and disadvantages, depending on the task and the data. For instance, GANs are good at producing high-quality images, but they are difficult to train and prone to mode collapse. Transformers are good at generating natural language, but they require a lot of computational resources and data. Generative AI has many applications in creative activities, data enrichment, problem-solving, and more. For example, generative AI can be used to synthesize new images for art or entertainment, augment existing data for training or testing purposes, solve complex optimization problems, or generate novel ideas or solutions. More on reddit.com

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A predictive AI predicts things like weather or traffic. A generative AI generates new content, like images or text. You could phrase it in a way that mixes them; "can you predict what a kangaroo cooking pizza would look like?" And some use cases are bit of a combination, eg AI rendering a red circle around a fracture in a X-ray. A possible difference is in how they are trained. For predictive AI, you usually feed past data with a known outcome, and then the network is modified with what is called backpropagation to change the network so that its output matches more closely with the known correct answer. Generative AI is usually taught unsupervised, meaning that it is fed data without knowing what the output should be. There is usually also a supervised learning step, for example, the AI might generate two outputs and humans then label one as better than the other. Especially in image generation, an adversial network can be used in training. Here, the adversial network tries to determine how good the output of the generative network is, and the two networks essentially compete. But, lot of techniques can really be shared between predictive and generative AI, so it's more about their purpose really. These terms aren't fully descriptive of the technical implementations, as most modern bleeding edge AI solutions tend to be a combination of many learning techniques and even many network architectures. And e.g. for weather modelling, you might use a generative network which then produces your weather prediction. More on reddit.com

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