We have come a long way from traditional programming to neural networks to generative models. In traditional programming, we used to have to hard code the rules for distinguishing a cat — type, animal; legs, four; ears, two; fur, yes; likes yarn, catnip. In the wave of neural networks, we could give the network pictures of cats and dogs and ask, is this a cat? And it would predict a cat. In the generative wave, we as users can generate our own content, whether it be text, images, audio, video, or other.
Generative AI is a type of artificial intelligence technology that can produce various types of content, including text, imagery, audio, and synthetic data.
In supervised learning, testing data values or x are input into the model. The model outputs a prediction and compares that prediction to the training data used to train the model. If the predicted test data values and actual training data values are far apart, that is called error. And the model tries to reduce this error until the predicted and actual values are closer together. This is a classic optimization problem.
Neural networks have the advantage that they can use both labeled and unlabeled data. This is called semi-supervised learning. In semi-supervised learning, a neural network is trained on a small amount of labeled data and a large amount of unlabeled data. The labeled data helps the neural network to learn the basic concepts of the task while the unlabeled data helps the neural network to generalize to new examples.
Gen AI is a subset of deep learning, which means it uses artificial neural networks, can process both labeled and unlabeled data using supervised, unsupervised, and semi-supervised methods.