What is Generative AI?
Generative AI, or generative artificial intelligence, refers to artificial intelligence systems and algorithms that generate new content, such as text, images, audio, code, and videos, from scratch. Generative AI tools create this content based on patterns it learned from previously existing data.
Generative AI tools create content depending on the instructions entered into them. These instructions are called prompts and are usually text. Some AI tools allow users to upload images, audio, or other data types as prompts. Once done, the AI tool will generate content to meet the creator’s requirements.
Generative AI tools are trained using deep learning models. These models learn the patterns of human-generated content and then use that knowledge to generate new content. For this reason, generative AI tools’ output might be limited to the data they were trained.
Difference Between AI and Generative AI
AI (artificial intelligence) refers to any system that can perform tasks that typically require human intelligence. Generative AI, on the other hand, is a subset of AI. It specifically refers to artificial intelligence systems that can generate new content.
AI can be used for various tasks other than content generation. For example, AI can classify items, predict events, or interact with physical items. Computer vision, robotics, and predictive analysis all make use of AI.
However, they are not considered generative AI since they do not generate any content from scratch. Instead, computer vision interprets visual information, while robotics interacts with physical items. Predictive analysis analyzes historical data and uses that to forecast future events.
In all, AI refers to all types of artificial intelligence systems (including generative AI), while generative AI only refers to AI used to generate new content. That said, it is quite common for people to use AI when referring to generative AI.
Common Generative AI Terminologies
You will come across several terms when discussing generative AI. It is beneficial to understand these terms as they are crucial to understanding what generative AI is all about.
1 Machine Learning
Machine learning is the subset of artificial intelligence that focuses on developing algorithms and statistical models that allow computers to learn and make decisions based on data, even when not explicitly programmed for such tasks.
Machine learning algorithms analyze patterns in the data presented to them and can improve their performance over time as they encounter new data.
2 Neural Networks
A neural network is an artificial intelligence and machine learning program, method, or model that teaches computers to process data and make decisions like a human brain.
Neural networks mimic how neurons in the human brain identify situations and make decisions. It is a subset of machine learning.
3 Deep learning
Deep learning is a specialized type of machine learning that uses neural networks to identify data and patterns. These networks simulate how the human brain processes information, allowing deep learning algorithms to automatically learn hierarchical features from raw data, such as images, text, or audio.
This capability allows deep learning systems to perform tasks like image classification, natural language processing, and speech recognition with a high degree of accuracy.
What Are Foundation Models in Generative AI?
Foundational models are large-scale, pre-trained neural networks that serve as the basis for a wide range of tasks in artificial intelligence. These models are trained on vast amounts of diverse data. After that, they can be fine-tuned to perform specific tasks, such as natural language processing, image recognition, or speech generation.
Types of Generative AI Foundation Models
There are multiple types of generative AI foundation models. The specific one used depends on the purpose for which the AI is being developed. However, four common ones are:
- Generative Adversarial Networks (GANs)
- Variational Autoencoders (VAEs)
- Transformer-based models
- Diffusion models
1 Generative Adversarial Networks (GANs)
Generative adversarial networks (GANs) are a deep learning architecture that involves training two neural networks to generate new data that closely resembles the data on which they were trained.
Both neural networks compete against one another. One is called a generator, and the other is a discriminator. The generator creates new data, while the discriminator evaluates them against real data and tries to determine whether they are real or generated by the AI model.
The generator and discriminator are trained simultaneously and continuously compete with one another. The generator tries to produce increasingly realistic outputs to trick the discriminator into thinking it is real, while the discriminator tries to improve its ability to distinguish between real and generated data.
2 Variational Autoencoders (VAEs)
Variational autoencoders (VAEs) are a deep learning architecture that generates variations of the data on which they are trained. Variational autoencoders include an encoder and a decoder.
The encoder extracts the important latent variables from the training data, and the decoder uses those latent variables to improve the input and generate it as an output. Because variational autoencoders create outputs similar to the input, they are useful for generating synthetic data for training AI models.
3 Transformer-Based Models
Transformer-based models are a deep learning architecture that analyzes prompts before transforming them into responses.
A transformer model identifies the context in which the words in the prompt are used. It also identifies the relationship between the words. It then uses both to generate an output.
Transformer models consist of an encoder and a decoder. The encoder processes the prompt and converts it into mathematical terms, while the decoder converts it into an output that is returned to the user.
Transformer models are the foundation for many state-of-the-art generative AI models, including Gemini and ChatGPT. In fact, the GPT in ChatGPT stands for generative pre-trained transformer.
4 Diffusion Models
Diffusion models are a deep learning architecture that creates data by simulating a process wherein noise is progressively added and removed from the data.
During training, these models learn to gradually corrupt data by introducing noise and transforming the original data into a noise distribution. Once trained, the model can generate new data by starting from pure noise and then denoising it.
How Generative AI is Trained
Generative AI works by feeding existing content to foundation models. These models then make use of neural networks to identify the patterns in the content. They then use these patterns to create new content.
Here is a simplified breakdown of how generative AI works:
1 Training
The foundation model is trained on a large dataset containing the type of content it is expected to generate. During training, the model analyzes the data to learn patterns, structures, and relationships within the content. Neural networks may be used during the training process.
2 Generate New Content
Once trained, the model will generate new content by taking input from a user (prompts) and using the patterns learned during training to create new content called outputs.
3 Refinement
Some generative AI models incorporate feedback loops or additional algorithms to refine the generated content. So, the AI system will run the generated content through its own system to improve it for quality and relevance.
4 Evaluation
The generated content is then cross-checked against certain criteria to ensure it meets the desired specifications. This evaluation could be performed by a human, though it may also be wholly or partly automated.