Generative AI? An Introductory Guide

What is Generative AI? An Introduction

Generative AI is a transformational type of artificial intelligence technology, capable of producing various kinds of content in response to natural language prompts. With generative AI models, users can produce imagery, text, audio, and even synthetic data in seconds.

The hype around generative AI started to build at the end of 2022, when Open AI launched ChatGPT, a state-of-the-art chatbot capable of creating incredible content in human-style language. However, generative AI actually existed long before 2022.

The technology underpinning generative AI was first introduced in the 1960s, in some of the earliest chatbots. But the potential of this form of AI wasn’t truly revealed until 2014, when generative adversarial networks (GANs) were introduced to the world of machine learning.

The latest class of generative AI applications have since emerged from foundational models, allowing companies to build unique image and language generating models.

Most of the foundation models used today are “large language models”, or LLMs, trained on huge volumes of natural language.

The impact of these systems is attributed not just to the size of LLMs, but the transformers (machine learning tools), which allow researchers to train models without complex labelling tasks.

Zero-shot learning allows models to use general understandings of the relationships between concepts to make predictions, and generate novel responses to input.

How Does Generative AI Work?

As mentioned above, generative AI models respond to “prompts”, in the form of images, audio, video, text, and more. They allow users to rapidly generate new content, in response to these prompts. Earlier versions of generative AI required users to submit data to the model using an API or similar process.

However, pioneers in generative AI have developed better UIs, which allow users to describe requests in plain natural language. Generative AI models leverage neural networks to determine structures and patterns in data, and generate new content.

One of the biggest breakthroughs in generative AI models, is the option for users to leverage various learning approaches, including semi-supervised and unsupervised learning for training. This means companies and developers can more easily leverage large amounts of data, to support Gen AI models in delivering unique responses to queries.

GANs and variational autoencoders ensure developers can train models with a specific view of the world, leading to various use cases for generative AI tools. Examples include everything from GPT-3 and GPT-4, to Stable Diffusion.

How are Generative AI Systems Developed?

Although generative AI systems as we know them today are still relatively new, there are multiple different types of models out there, each trained in a unique way.

One of the most common options for developing generative AI is to use “diffusion models”, or denoising diffusion probabilistic models. These determine vectors in latent space using two-step training processes. The systems use both forward diffusion to add random noise to training data, and reverse diffusion to reverse the noise and reconstruct samples of data.

Although diffusion models take longer to train than variational autoencoder models, developers can train almost an infinite number of layers, leading to a higher-quality output. Alongside diffusion models, organizations also leverage both “VAEs” and “GANs” for generative AI.

VAEs, or Variational Autoencoders, consist of two corresponding neural networks: encoders and decoders. The encoder converts input into a smaller representation of data, while the decoder reconstructs the original input, removing any unnecessary information. The two networks work together to learn efficient ways of representing data, allowing for creative output.

GANs, or Generative Adversarial Networks force two neural networks to compete against each other. A generator creates new examples of data, while a discriminator learns to distinguish generated content as “real” or “fake”.

The two models are trained simultaneously, and gradually become smarter, producing better content over time. GANs deliver high-quality samples and outputs quickly, but often suffer from limited sample diversity.

Another major factor in the development of generative AI models is the use of a specific architecture, such as a transformer network. These networks work similarly to neural networks, processing sequential input in a non-sequential format. Transformers benefit from both self-attention and positional encoding, allowing algorithms to focus on the relationship between words.

Self-attention layers assign a weight to every component of the input, correlated to its value or importance. The positional encoding system determines the order in which input words are submitted. Transformers are made up of various layers or transformer blocks, which work together to decipher, and even predict streams of data, such as images, and text.

Applications of Generative AI

Like all AI technologies, generative AI systems are intended to support specific use cases. These tools can streamline the workflows of engineers, scientists, researchers, and creatives alike. Generative AI models can take input in a variety of forms, and generate new content in the same modalities.

Common applications include:

• Visuals: One of the most popular generative AI applications is the ability to create unique images, such as avatars, 3D images, graphs, and illustrations. Users can create images in a variety of different styles, and even modify or edit existing content. Generative AI models can create graphs to showcase chemical compounds for drug discovery, or create realistic images for video games, design tools, virtual, and augmented reality.
• Audio: Audio, speech, and music are all emerging fields in the generative AI landscape. Examples include models that can develop snippets of audio clips and songs using text inputs. Some tools can also recognize specific objects in videos, and create complementary noises or music for video footage.
• Language: Text is at the heart of many generative AI models, and is often regarded as the most advanced domain. Some of the most popular language-based generative models are LLMs, or large language models, which can be used to create code, essays, translations, and more.
• Synthetic data: Developers use synthetic data to train AI models, and when necessary, data is restricted or doesn’t exist. Generative models can create synthetic data to cover all modalities, and use label efficient learning to grow. Generative AI algorithms can reduce labelling costs by automatically producing augmented training data, or learning representations of data.

The use cases for generative AI also span across various industries. Many professionals describe generative AI as a kind of general-purpose technology, similar to electricity or steam power. In each industry, this technology can serve different use cases, such as:

• Building fraud detection systems and data management tools for finance.
• Interpreting contracts and analyzing evidence in the legal industry.
• Combining data from cameras and metrics to identify defective components and potential problems in the manufacturing space.
• Producing content and translating it to other languages in the film and media space.
• Identifying promising drug candidates and opportunities in the medical industry.
• Designing and editing prototypes in the architectural sector.
• Creating game content and levels in the gaming space.

The Challenges of Generative AI

While countless companies, from Microsoft and Google, to MIT, are now investing in generative AI solutions, there are still challenges to overcome. One of the biggest challenges is connected to computing structure scalability. Generative AI models can use billions of parameters, and need significant volumes of data for training.

This means although a generative AI chatbot might seem simple on the surface, it requires significant technical expertise, capital investment, and unique technology to develop. To train such large datasets, companies also need massive amounts of computing power to fine-tune models.

Other common challenges include:

• Sampling speed: The scale of generative models means there may sometimes be latency present in the time required to generate an instance. For interactive solutions that offer text generation, or AI natural language processing, conversations need to happen accurately and immediately. Slow sampling speeds can make it harder to train tools quickly.
• Data quality: As mentioned above, some generative AI models are used to create synthetic data for various use cases. Not all data is suitable for use in AI models and apps. These models require unbiased, high-quality data to perform specific tasks. Some domains struggle to find enough effective data to create models.
• Data licenses: Further compounding a lack of data is an inability to access data. Not all data available for generative AI optimization is open source, Some companies struggle to access commercial licenses to use existing data, or create new bespoke datasets.

Many innovative companies are now working on new ways of supporting the continued growth and development of generative AI models. These platforms and products are starting tor educe the complexities involved in building innovative machine learning models at scale.

The Benefits of Generative AI

While there are various challenges to overcome before everyone will be able to access generative AI solutions to create new images, text descriptions, and more, the technology still has many distinct benefits. Generative AI algorithms are powerful tools, capable of creating new, original content, such as videos, text, and images (Dall-e 2).

Some of these deep learning style models are capable of creating content that’s almost indistinguishable from content created by humans. Generative AI algorithms can even be used to improve the accuracy and efficiency of existing AI technologies.

They can assist in many natural language processing tools and computer vision systems more effectively, with the creation of new synthetic data for training. Plus, generative AI algorithms can allow developers and organizations to analyze and explore complex data in new ways.

Businesses and researchers can use generative AI to summarize information, discover hidden patterns, and find trends that may not be evident in raw data alone. On a broad scale, generative AI algorithms can accelerate and automate a huge variety of processes and tasks, saving organizations and individuals significant time and resources.

Are There Any Issues with Generative AI Today?

Although the rise of generative AI has led to a lot of excitement in everything from the manufacturing to healthcare industry, there are also various concerns surrounding the technology. Many of these concerns revolve around the potential for misuse and abuse of AI models, issues with poor quality results, and the potential to disrupt some existing business models.

Generative AI is powerful, but it can provide inaccurate and misleading information. It’s also difficult to trust when companies aren’t fully aware of the source of the information used by the generative pre-trained transformer and other algorithms.

In some cases, generative AI could promote new forms of plagiarism which overlooks the rights of content creators and artists. It could also disrupt existing business models, particularly in relation to advertising and search engine optimization.

One particular concern related to generative AI, is that text-based and image generation models could make it easier to generate fake news. There’s also the potential that some models, capable of replicating humans, could impersonate people during social engineering attacks.

Looking to the Future of Generative AI

As innovators in the modern world continue to optimize and enhance generative AI models, these tools are showing amazing promise. Research is currently ongoing into new ways of building tools to detect and overcome potential issues and challenges. Some companies are even building tools capable of detecting AI-generated images, video, and text.

Improvements in AI development platforms will also help to improve the development of generative AI solutions in the future. Business leaders and developers are already searching for ways to embed generative AI into the tools and systems we already use on a massive scale.

Design tools will become more intuitive, grammar checkers will evolve, and training tools may soon be able to automatically identify best practices on behalf of business leaders. Generative AI has the potential to transform virtually every aspect of how we live and work.