The term generative AI refers to a relatively new field of AI that can create human-like content, from photos and videos to poetry and even computer code.
Several different techniques are used to accomplish this. These have evolved over the past decade, primarily based on early work done in the areas of deep learning, transformer models, and neural networks.
They all rely on data to effectively “learn” how to generate content, but beyond that, they are built on completely different methodologies. Here we outline some of the categories they fall into and the types of content you can create using them.
large language model
Large-scale language models (LLMs) are the foundational technology behind breakthrough generative AI tools such as ChatGPT, Claude, and Google Gemini. Essentially, these are neural networks trained on vast amounts of text data that can learn the relationships between words and predict the next word that will appear in a given sequence of words. You can then receive further training in specific texts related to your specialty. This is known as “tweaking” to enable you to perform a specific task.
Words are classified as “tokens”. Tokens can be small individual words, parts of longer words, or combinations of prefixes, suffixes, and other linguistic elements that appear together frequently in text. The mathematical process of matrix transformation is then used to transform it into structured numerical data that can be analyzed by a computer.
LLM uses natural language input for many tasks, including not only the creation of text and computer code, but also AI for language translation, sentiment analysis, and the generation of other forms such as text-to-image and text-to-speech. Made it understandable to computers. . However, their use has raised ethical concerns regarding bias, AI hallucinations, misinformation, deepfakes, and the use of intellectual property to train algorithms.
popularization model
Diffusion models are widely used in image and video generation and work through a process known as “iterative denoising.” It starts with a text prompt that the computer can use to understand what it can use to create an image, then generates random “noise.” You can think of this as starting a drawing by scribbling randomly on a piece of paper.
The doodle is then gradually refined using the training data to understand what features should be included in the final image. Each step removes “noise” and gradually adjusts the image to include the desired characteristics. Ultimately, this creates an entirely new image that matches the text prompt but has not yet been found in the training data.
By following this process, today's most advanced diffusion models, such as Stable Diffusion and Dall-E, can create photorealistic images and images that mimic any style of painting or drawing. Additionally, video can now be generated, as recently demonstrated by OpenAI's groundbreaking Sora model.
generative adversarial network
Generative Adversarial Networks (GANs) were introduced in 2014 and have quickly become one of the most effective models for generating synthetic content for both text and images. The basic principle involves playing two different algorithms against each other. One is known as the “Generator” and the other is known as the “Discriminator”, and both are tasked with getting better and better at outsmarting each other. The generator tries to create realistic content, and the discriminator tries to determine whether it is real or not. Each learns from the other and gets better and better at its job until the generators figure out how to create content that is as “authentic” as possible.
Although older than the large-scale language and diffusion models used by headline capture tools such as ChatGPT and Dall-E, GANs remain a versatile and powerful tool for generating images, video, text, and sound. It is widely used in computers. Visual and natural language processing tasks.
neural radiation field
Neural Radiance Fields (NeRF) is the newest technology we'll discuss here, and it just arrived in 2020. Unlike other generative technologies, it is specifically used to create representations of 3D objects using deep learning. This means creating aspects of the image that cannot be seen by the “camera”. For example, an object in the background of the image may be obscured by an object in the foreground, or the back side of the object may be photographed from behind. front.
This is done by predicting factors such as the volumetric properties of an object, using neural networks to model the shape and properties such as light reflection around the object, and mapping them to 3D spatial coordinates.
This allows, for example, two-dimensional images of objects such as buildings or trees to be recreated as three-dimensional representations that can be viewed from all angles. Developed by Nvidia, this technology is used for visualization in robotics, architecture, and urban planning, as well as creating 3D worlds that can be explored in simulations and video games.
Hybrid models for generative AI
One of the latest advances in the field of generative AI is the development of hybrid models that combine different techniques to create innovative content generation systems. These models leverage the strengths of different approaches, such as combining adversarial training of generative adversarial networks (GANs) with iterative denoising of diffusion models to produce more sophisticated and realistic outputs. . By integrating large-scale language models (LLMs) with other neural networks, hybrid models can provide enhanced context and adaptability, resulting in more accurate and context-relevant results. This hybrid approach opens new possibilities for applications such as text-to-image generation, where the fusion of different generation techniques allows for more complex and diverse outputs and improved virtual environments. For example, DeepMind's AlphaCode combines the power of large-scale language models (LLMs) with reinforcement learning to generate high-quality computer code, demonstrating the versatility of hybrid approaches in software development. Another example is OpenAI's CLIP, which blends text and image recognition capabilities to create more accurate text-to-image models. CLIP's ability to understand complex relationships between text and images allows it to work with a variety of production applications.
Generative AI is constantly evolving, with new methodologies and applications emerging regularly. As the field continues to grow, we expect to see more innovative approaches that combine different technologies to create advanced AI systems. The next decade is likely to see breakthrough applications that transform industries and reshape the way we interact with technology.