What Is Generative AI? Essentials, Models, and Considerations

Generative AI is a branch of artificial intelligence capable of producing content such as images, videos, audio, code, synthetic data, and text. It typically relies on prompts to produce specific outputs. In this context, prompts are instructions you provide AI so it generates a desired response.

Generative AI is gaining a lot of popularity, with tools such as ChatGPT amassing a million users just five days after its launch. The generative AI market has also grown significantly and is expected to reach $110.8 billion by 2030.

Whether in writing, software development, healthcare, marketing, or finance, generative AI can streamline processes and help people become more productive. For example, marketing departments can use generative AI to gain new insights into potential customers, travelers can use it to plan trips, and designers can use it to draw inspiration and glean new content ideas.

In this article, we’ll cover what generative AI is, how it works, and possible uses, as well as some of the ethical issues to be mindful of.

Understanding generative AI

Generative AI is an artificial intelligence technology that applies statistical algorithms to the data used to train it.  The magic behind AI is that, with the right algorithms and enough training data, it can recognize patterns.

When prompted by a user, generative AI applies statistical methods to the patterns it has learned from its training data, to predict the user’s desired output. Depending on the AI, they can generate 3D models, text, audio, videos, and images.

ChatGPT is an example of generative AI that can produce complex content. ChatGPT was trained on massive amounts of data and uses complicated algorithms. This allows it to better mimic human creativity than earlier AI models, and to generate interactive responses.

However, while ChatGPT is demonstrably impressive, and while it has uses that can improve performance in a number of industries, OpenAI (the developer of ChatGPT) is also frank about the software’s ability to “produce inaccurate information about people, places, or facts.”

When used appropriately, generative AI can be a very useful tool. However, the information it produces should be taken with a grain of salt, and in many cases a professional or expert (either in AI or in the appropriate industry) should verify both the ethical use of the technology, as well as the information in the AI’s output.

While generative AI is designed to create (or “generate”) new content, there are other kinds of AI designed for different purposes. Conversational AI uses natural language processing (NLP) and machine learning to mimic conversation. It’s used in customer service chatbots or voice assistants like Alexa or Siri.

Discriminative AI is trained to identify boundaries between classes of objects (to “discriminate” between them). It can be used, for example, to predict whether an email is spam, or whether an object in an image is a car or a truck. Unlike generative AI, discriminative AI is not designed to create new data outputs.

Examples of generative AI

Now that we’ve covered what generative AI is, here are some popular examples of generative AI:

• ChatGPT. Developed by OpenAI, ChatGPT is perhaps the most popular generative AI in use today. Apart from producing responses interactively, ChatGPT can also help produce web content, plan trips, summarize text, create personalized recommendations, and much more.
• Google Bard. Google Bard is a conversational AI that can also generate text, code, formulas, and translations. Unlike the current version of ChatGPT, which can only access the internet with plugins, Google Bard can access the internet natively, which allows it to work from current information.
• DALL-E. This generative AI produces new images and art from text prompts.
• GitHub Copilot. This generative AI is geared toward software developers, enabling them to be more productive through code generation.
• Alphacode. Alphacode is a conversational AI that can help programmers develop code by responding to natural language prompts.
• Midjourney. Midjourney brings out the power of generative AI by allowing you to produce uniquely styled images with text-based prompts. Where most other image generators are more designed to create photorealistic images, Midjourney is designed to create images in the style of a painting.

How does generative AI work?

Whether it's ChatGPT, Google Bard, or any other form of Generative AI, they are all guided by the same principles.

Generative AI relies on neural networks (a software system intended to mimic the layered structure of a brain) to apply statistical models to, and identify patterns from, large datasets. Neural networks are the processes and methods that tell computers how to handle data.

When a user inputs a prompt, the AI applies statistical models to recognize the intention behind the prompt, and to then identify an output that is most probably desired. One criticism of these models is that they are designed to find the most average output—which means they aren’t great at creating exceptional quality.

Another criticism is that their output is only as good as the data they were trained on. If their training data was limited or contained biases or flaws, then their output will represent that.

That being said, they can generate content at great speed, and are very useful for many applications in many industries.

Depending on the type of generative AI, users can train neural networks using a wide range of data, including text, images, code, and videos. They use this training data to create new things that look, read, and sound similar.

Generative AI models

Generative AI models are the building blocks of generative AI. They’re responsible for identifying and learning patterns from large datasets and using that information to produce similar content.

Creating generative AI models from scratch requires a number of resources, including technical skill, time, and computing power. They have to be trained using lots of high-quality data to produce relatively high-quality outputs.

From Generative Adversarial Networks (GANs) to Large Language models (LLMs), we cover the different types of generative AI models in the following section.

Types of generative models

Generative AI applications are built on top of the following types of generative models:

• Generative Adversarial Networks (GANs). A generative adversarial network (GAN) features two underlying neural networks; a generator and a discriminator. The generator is responsible for producing new data, while the discriminator compares the output against its training data and determines if it's appropriate. This process continues until the generator produces an output that the discriminator can’t differentiate from its training data. The outcome is presented to the user. GAN models are commonly used to generate new data, images, art, and audio.
• Variational Autoencoder Models (VAEs). Like GANs, VAEs use two neural networks (encoders and decoders) to compress data into a smaller representation and transform it into new forms of content similar to the original data. Applications of VAE models include audio, video, and image generation.
• Large Language Models (LLMs). LLMs require large amounts of data, including social media posts, articles, books, and websites, to produce natural language text. Generative AIs like ChatGPT and Google Bard use LLMs. Chatbots and virtual assistants also use large language models.
• Transformer-based models. These types of generative AI models can recognize context and relationships in sequential data (like text). As a result, generative pre-trained transformer models feature in tasks like object detection, image recognition and classification, and language translation.

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How is generative AI used?

From image generation to scientific research, generative AI has many use cases. We cover some in the following sections.

Image generation

Organizations and content creators use generative AI for image synthesis. This technique involves producing artificially generated images with specific qualities. Breakthroughs in generative AI have made it possible to create photorealistic images and artwork from simple text descriptions or prompts.

Generative models also support image-to-image translation and style transfer. Through robust training, generative AI can use data to imitate various artistic styles and manipulate and enhance images.

Machine learning models are limited in their ability to perform many tasks as well as people do. For example, generative AI cannot process abstract concepts like emotions, making them less suitable for activities that require the portrayal of specific feelings.

Midjourney and DALL-E are examples of generative AI tools for generating images from text prompts.

Below is a realistic image of French fries generated by the community on the Midjourney Discord server.

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One criticism of AI for this use is that the images aren’t always photorealistic—even when the AI is given a prompt specifically asking for that style. A second criticism is that, because its output is only as good as the data it was trained on, the output tends to be similar to images that were mass produced.

In the case of these French fries, the presentation looks very similar to images seen in advertisements for fast food, even though the prompt was only for “a photorealistic image of French fries.” With more precise prompts, you can ask for fries on a platter, at a picnic, or on the surface of Mars.

Natural language processing

Generative AI has several use cases for natural language processing.

First, it's used in chatbots to provide user feedback. Once a person asks a question, a chatbot processes the prompt and uses generative AI to produce a response. Such chatbots are available 24/7, allowing organizations to be more productive and to provide helpful feedback to clients.

Second, generative AI can assist in generating articles and social media posts, as well as email and other personalized messages. It’s trained on large datasets of human-generated text to learn and create content in different styles.

Tools like Huggingface and DeepAI are examples of generative AI for text-to-image synthesis. This process involves extracting meaning from text and using the information to generate a corresponding image. Generative AI is also used to caption or add descriptions to images.

Despite advancements, generative AI still has some challenges in natural language processing. For instance, some generated texts may not make sense, or may be inaccurate. Offensive or biased language may also appear in generated content.

Creative fields

Generative AI can improve the workflows in the generation of art, music, and design—a fact that many creators are already aware of. In a survey of 1,000 professionals in creative roles, Adobe found that only 19% reported not having used generative AI. Additionally, 71% acknowledged that they expect to use generative AI in their work, and 59% said they’d use in their personal lives.

Musicians can use generative AI to produce and experiment with different lyrics, beats, and vocals. Similarly, artists can also use generative AI for design ideas and inspiration. Generative AI tools like ChatGPT and Google Bard have gained popularity in this area due to their ability to produce helpful output.

To illustrate ChatGPT’s content generation feature, we asked it to write a poem about flowers. Here’s what it produced:

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Whatever your feelings about the quality of that output, the fact is that it conforms to many of the rules of poetry. We could have given ChatGPT another prompt, asking it to refine the poem, to change the rhyme scheme or meter, or to make it a limerick or sonnet.

As useful as this may be in ideation and in exploring creativity, ethical issues arise from the application of generative AI in creative fields.

For instance, it can be challenging to determine who owns the rights to AI-generated content. In some jurisdictions, courts have found that AI cannot hold copyrights, and that work created using mechanical processes without creative input from a human author is in the public domain.

There are also ongoing lawsuits against some companies that have developed AI, alleging that the programs infringed on copyrights in the training process and later use.

Another risk, more easily solved by verifying the AI’s output, is that AI could produce offensive, inaccurate, or misleading content.

Healthcare and science

Generative AI has the potential to revolutionize the healthcare sector, making it easier and faster to perform research, and making care more accessible for patients.

Generative AI technologies can accelerate drug discovery processes. A study by Ohio State University revealed that artificial intelligence can boost drug discovery by iterating through millions of possible chemical reactions and generating helpful results.

The adoption of generative AI in healthcare can also improve the disease diagnosis process. Medical professionals can use generative AI models to analyze a large dataset of potential diseases and identify patterns of a specific illness. As in many uses, the AI alone isn’t enough to solve the problem—it can’t actually diagnose anyone. But it can make the diagnostic process faster and more accurate for medical professionals and their patients.

In another area of application, medical chatbots using generative AI can give patients personalized advice and recommendations. Though they can’t replace healthcare professionals, the information they provide can improve the patient’s well-being.

Healthcare is a highly sensitive sector, meaning the development and deployment of generative AI models in healthcare must adhere to rigorous validation, regulation, and ethical considerations. The models must undergo thorough evaluation and validation to ensure reliability and safety before being integrated into clinical practice.

What are the benefits of generative AI?

Recent breakthroughs in generative AI have allowed companies and individuals to enjoy many benefits, including the following.

Data augmentation

Generative AI can produce additional synthetic data by combining or modifying the existing data. As a result, it saves on costs related to collecting additional data and improves model performance and generalization.

Realistic simulation

Generative AI can create realistic simulations and synthetic environments. Such simulations facilitate the training and testing of AI systems in a safe and controlled manner. Generative AI also reduces operating costs and potential property damage. Simulation is particularly useful in robotics, autonomous vehicles, personnel training, virtual reality, and healthcare.

Personalization

Personalization allows organizations to create a loyal following and increase their customer satisfaction rates. Businesses can use generative AI to produce personalized messages and experiences that resonate well with the target audience.

By generating content tailored to individual preferences, businesses can create content that enhances user engagement and satisfaction. It's also helpful in creating recommender systems to suggest products, personalized marketing, and adaptive user interfaces.

Research and education

Generative AI can be a valuable educational tool, enabling educators to develop customized study plans, find interactive and engaging learning activities, and provide prompt student feedback.

Regarding research, generative AI can help produce synthetic data for analysis, saving time and money. It's also great for identifying patterns in huge datasets, enabling researchers to reach conclusions faster. Plus, researchers can also run simulations and perform experiments using generative AI.

What are some considerations of generative AI?

Generative AI can be both powerful and beneficial. Here are key areas of consideration to get the most out of generative AI.

How it might impact your business

The potential impact of generative AI on your business varies depending on your business model, current practices, and industry.

Artificial intelligence can help businesses and freelancers increase efficiency and productivity. These professionals can use AI to generate design ideas, quickly produce outlines or mock ups that may or not be further developed, and improve customer service, among other activities.

Artificial intelligence is not a direct replacement for these professionals. Rather, it’s a tool that can increase their productivity. More work can be done, or higher accuracy achieved, in the same amount of time.

Ethical implications

The use of generative AI raises several ethical issues.

Generative AI videos are already popular in entertainment. But the US government warns that they can also be used to imitate heads of state or other notable personages in ways that could erode public trust or affect the public discourse.  

Training data for generative AI models might also include copyrighted materials or the existing work of living creatives who aren’t compensated for reproduction.

Finally, potential bias could be built into AI algorithms or trained into the models. Such bias can affect the quality of the content a generative AI produces.

The bottom line is that businesses and people should develop responsible use guidelines that are appropriate to their industry and business model.

Consider working with legal experts on Upwork to navigate murky ethical waters when using AI.

Evaluation and quality

Assessing the quality and evaluating the output of generative AI models can be challenging. You must use robust evaluation techniques and metrics to ensure the generated samples meet your desired quality standards, especially in terms of readability, accuracy, originality, and coherence.

Data privacy and security

Generative AI models are subject to data privacy and security concerns. If a user gives an AI a prompt that includes sensitive or private data, they are in essence giving that data to whoever owns the AI. Even if that owner is operating with the best of intentions, the user is now trusting that third party’s security measures with their sensitive data.

Historically, companies that hold a lot of third-party data—in the way that companies like OpenAI, Google, and Microsoft do—have been prime targets for hackers.

Overfitting and generalization

Overfitting is a phenomenon where the training data is small or doesn’t apply to the AI’s specific use case. This limits an AI’s capabilities. When this happens, generative AI models cannot generalize well enough or perform accurately. You can use techniques like data augmentation and regularization to avoid overfitting.

How can generative AI impact business?

While the capabilities are impressive, artificial intelligence is not a replacement for experts or professionals. Rather, AI can help people be more creative and productive. Tasks like content creation, image generation, data augmentation, and research can be done more quickly and more accurately with the appropriate use of these new tools.

Generative AI is a valuable technology with lots of benefits. Consider working with AI engineers on Upwork to help you integrate generative AI into your business workflows.

And if you’re an AI professional looking for work, start your search on Upwork. Hundreds of clients are in need of your services. Get started today.

Disclosure: Upwork is an OpenAI partner, giving OpenAI customers and other businesses direct access to trusted expert independent professionals experienced in working with OpenAI technologies.

Upwork does not control, operate, or sponsor the other tools or services discussed in this article, which are only provided as potential options. Each reader and company should take the time to adequately analyze and determine the tools or services that would best fit their specific needs and situation.