Generative AI

Generative AI (GENAI)  is a type of Artificial Intelligence technology Advancement proficient in generating different contents which include text, imagery, synthetic data, or other media. GENAI systems use generative models such as large language models (LLM is a machine learning model consisting of a neural network with many parameters, trained on large quantities of unlabelled text using self-supervised learning) to statistically sample new data based on the training data set that was used to create them.

Generative models fall under the larger field of machine learning, which is a subfield of artificial intelligence (AI). The idea is to learn a statistical model of the data, which can then be used to generate new samples that are similar to the original data.

Each of these models has its own strengths and weaknesses and is suited to different types of data and applications. Overall, generative models are a powerful tool for generating new data and have many applications in fields such as computer vision, natural language processing, and music and art generation

There are many types of generative models, but some of the most common ones include:

  • Autoencoders: Autoencoders are neural networks that are trained to encode and decode data. They can be used to generate new samples by randomly sampling from the latent space of the encoder.
  • Variational Autoencoders (VAEs): VAEs are a type of autoencoder that learns a probability distribution over the latent space. This allows them to generate new samples by sampling from the latent space and decoding the samples.
  • Generative Adversarial Networks (GANs): GANs consist of two neural networks: a generator and a discriminator. The generator is trained to generate new data that is similar to the original data, while the discriminator is trained to distinguish between the generated data and the original data. The two networks are trained together in a game-like setting, where the generator tries to fool the discriminator and the discriminator tries to correctly identify the generated data.
  • Autoregressive models: Autoregressive models are a type of generative model used in artificial intelligence to generate new data based on previous values. These models predict the next value in a sequence by learning the probability distribution of the training data. Autoregressive models are often implemented using neural networks and are successful in generating realistic and diverse outputs.

They are an exciting area of research in machine learning, and they are likely to play an increasingly important role in many fields in the years to come.

  • Pelumi Onafuye

    Pelumi is a Data Scientist who has competed in many Data Science competitions. He has the motivation to write to assist those who want to learn, grow and excel in the field of AI and data science. At his free time, he loves to play the drum 🥁.