Generative AI, or gen AI, is a type of artificial intelligence (AI) that can create new content and ideas, like images and videos, and also reuse what it knows to solve new problems. It can learn human language, programming languages, art, chemistry, biology, or any complex subject matter. It reuses what it knows to solve new problems.
How Does Generative AI Work?
Generative AI models use neural networks to identify the patterns and structures within existing data to generate new and original content.
One of the breakthroughs with generative AI models is the ability to leverage different learning approaches, including unsupervised or semi-supervised learning for training. This has given organizations the ability to more easily and quickly leverage a large amount of unlabeled data to create foundation models. As the name suggests, foundation models can be used as a base for AI systems that can perform multiple tasks.
Examples of foundation models include GPT-3 and Stable Diffusion, which allow users to leverage the power of language. For example, popular applications like ChatGPT, which draws from GPT-3, allow users to generate an essay based on a short text request. On the other hand, Stable Diffusion allows users to generate photorealistic images given a text input.
How to Evaluate Generative AI Models?
The three key requirements of a successful generative AI model are:
- Quality
- Diversity
- Speed
How do generative AI models work?
Traditional machine learning models were discriminative or focused on classifying data points. They attempted to determine the relationship between known and unknown factors. For example, they look at images—known data like pixel arrangement, line, color, and shape—and map them to words—the unknown factor. Mathematically, the models worked by identifying equations that could numerically map unknown and known factors as x and y variables. Generative models take this one step further. Instead of predicting a label given some features, they try to predict features given a certain label. Mathematically, generative modeling calculates the probability of x and y occurring together. It learns the distribution of different data features and their relationships. For example, generative models analyze animal images to record variables like different ear shapes, eye shapes, tail features, and skin patterns. They learn features and their relations to understand what different animals look like in general. They can then recreate new animal images that were not in the training set.
AI models.
- Diffusion models
- Generative adversarial networks
- Variational autoencoders
- Transformer-based models
Here are the most popular generative AI applications:
Language: Text is at the root of many generative AI models and is considered to be the most advanced domain. One of the most popular examples of language-based generative models are called large language models (LLMs). Large language models are being leveraged for a wide variety of tasks, including essay generation, code development, translation, and even understanding genetic sequences.
Audio: Music, audio, and speech are also emerging fields within generative AI. Examples include models being able to develop songs and snippets of audio clips with text inputs, recognize objects in videos and create accompanying noises for different video footage, and even create custom music.
Visual: This encompasses the creation of 3D images, avatars, videos, graphs, and other illustrations. There’s flexibility in generating images with different aesthetic styles, as well as techniques for editing and modifying generated visuals. Generative AI models can create graphs that show new chemical compounds and molecules that aid in drug discovery, create realistic images for virtual or augmented reality, produce 3D models for video games, design logos, enhance or edit existing images, and more.
Synthetic data: Synthetic data is extremely useful to train AI models when data doesn’t exist, is restricted, or is simply unable to address corner cases with the highest accuracy.
