Artificial Intelligence (AI) is a broad and dynamic field. While many people are familiar with AI models that are good at prediction and classification, a new technological trend called generative AI is becoming more and more prominent. However, what is generative AI exactly, and how is it different from previous models of artificial intelligence? This article will walk you through generative AI vs other types of AI models. Let’s get started!

Fundamental Classification and Prediction

Conventional AI models are very good at finding patterns and forecasting outcomes by analyzing large volumes of data. Here's an explanation of the two typical kinds:

1- Models of Supervised Learning: Labeled data sets are used to train these models. By identifying patterns in the data, they are able to categorize previously undiscovered data points. To recognise future spam communications, an email spam filter, for instance, may be trained on a collection of labeled emails (spam and non spam).

2- Unsupervised Learning Models: These models do not use labeled data sets, in contrast to supervised models. They look for hidden correlations and patterns in unlabeled data through analysis. An unsupervised learning model could, for instance, look up past purchases made by customers to find product clusters or suggest related products.

The Generative Spark: From Interpretation to Original Work

Understanding Foundational Structures: Models of generative AI do more than just find patterns in data. They get knowledge of the data set's underlying statistical correlations and features. This enables them to produce completely new information that is consistent with those acquired frameworks, rather than only categorize or anticipate.

Accepting Probabilistic Models: To generate fresh data, generative AI models make use of probabilistic models. In essence, these models show how likely it is for various characteristics or elements to occur in the output that is produced. This mimics the complexity of real-world data by allowing for a wide range of alternatives.

A Range of Ingenuity: Distinct Generative AI Types

GANs, or Generative Adversarial Networks: In these models, two neural networks compete with one another. While the other network (discriminator) works to separate the created data from actual data, the first network (generator) produces new data.

The generator's capacity to generate outputs that are more and more realistic is improved by this adversarial process. Deepfakes, or fake movies made with GANs, can produce realistic pictures and even compose music.

Variational Autoencoders (VAEs): VAEs aim to extract the latent variables or components that are hidden beneath the surface of the data. They can then create new data points with the same properties as the original data set by using these latent variables. Image compression, anomaly detection, and creating new versions of preexisting data are among the activities carried out by VAEs.

Applications of Generative AI in Drug development

AI can speed up the drug development process by creating new molecules with desirable characteristics.

Material Science: From stronger metals to more effective solar cells, generative AI can assist in the design of novel materials with certain properties.

Personalized Learning: AI is capable of producing materials that are specifically tailored to each student's needs.

Creative Industries: Generative AI can help designers, composers, and artists explore new concepts and produce new material.