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What is generative AI

GenAI is a remarkable field of artificial intelligence that allows users to create novel output that is highly reminiscent of human-generated content. These outputs can take a variety of formats, including text, images, video, sound, and 3D models. Thanks to recent advances in the field, genAI is experiencing unprecedented growth and adoption, revolutionizing many industries and sectors.

The core of genAI is in its name: "Generation." While traditional AI algorithms focus on identifying patterns in data for predictive purposes, such as predicting whether the next image in a series of images is a cat or a dog, genAI uses learned patterns to and produce entirely new results. Given the example above, the genAI model could create an entirely new representation of a cat, a dog, or a cat and a dog.

How does generative AI work?

GenAI is based on machine learning techniques inspired by the human brain's neural systems called neural networks. These genAI networks are “trained” on very large amounts of data, from which they learn how to capture and identify features, patterns, and relationships within the data. This allows the model to generate new data instances that are similar but different from the original training data.

For example, a GenAI model trained on facial images can learn to understand facial features such as nose shape, eye position, and smile curvature, thereby identifying specific faces with the same structure and features. Generates a new face that does not correspond to Similarly, models like ChatGPT are trained on large amounts of audio data to understand grammar, sentence structure, common phrases, context, tone, and style to produce coherent and contextually appropriate text.


Popular AI Generators

There are several generation AI platforms that you can get used to. These are useful for automating certain processes within your workflow.

ChatGPT: This language model is based on the GPT architecture and produces text similar to that produced by humans. A useful tool for research, strategy, and content creation.

DALL-E2: This model generates images from text prompts, allowing creatives to create vibrant illustrations and concept art to help with content marketing.

GitHub Copilot: This collaboration between GitHub and OpenAI serves as a coding companion that helps developers write code faster and more intuitively.]

GenAI can use a variety of models, each using unique methods to train the AI ​​and produce results. There are many types of models, but the most popular are generative adversarial networks (GANs), variational autoencoders (VAEs), and denoising diffusion probabilistic models (DDPMs). Each model has its own strengths and limitations and is suitable for different situations. Some models are better at producing high-quality results, while others give you more control over the generation process. Therefore, model selection plays a critical role in determining the capabilities and limitations of GenAI applications across disciplines.

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