How does GenAI work?
Generative AI learns patterns and structures from vast datasets using large learning models. These models are constantly trained, tuned, and evaluated to deliver accurate and reliable results. GenAI uses deep learning algorithms and neural networks to perform its tasks. These algorithms enable GenAI to analyze vast amounts of data, identify patterns, and make accurate predictions. Deep learning algorithms mimic the human brain's workings, processing, and interpreting data. This approach is different from other AI models and often utilizes multiple techniques.
Variational autoencoders (VAEs) Variational autoencoders are deep learning models composed of an encoder that learns to isolate critical latent variables from training data and decoder then uses those variables to create new variations of the data they’re trained on. These generative models use machine learning to generate data and to perform denoising. VAEs are more commonly associated with image generation, but they can be used for AI applications, like anomaly detection.
Deep convolutional GANs (DCGANs) are popular for generating realistic images. They consist of two networks: a generator that creates images and an adversarial network that compares these generated images with real ones. DCGANs utilize convolutional neural networks for both the generator and discriminator. In contrast, recurrent neural networks (RNNs) generate sequential data, with each neuron processing inputs to produce outputs. Transformers are employed in language modeling, and natural language processing (NLP) is used to create humanlike text for tasks like content creation and language translation.