Artificial intelligence is everywhere. Excitement, fear, and speculation about its future dominate headlines, and many of us already use AI for personal and work tasks.
Of course, it’s generative artificial intelligence that people are talking about when they refer to the latest AI tools. Innovations in generative AI make it possible for a machine to quickly create an essay, a song, or an original piece of art based on a simple human query.
So, what is generative AI? How does it work? And most importantly, how can it help you in your personal and professional endeavors?
This guide takes a deep dive into the world of generative AI. We cover different generative AI models, common and useful AI tools, use cases, and the advantages and limitations of current AI tools. Finally, we consider the future of generative AI, where the technology is headed, and the importance of responsible AI innovation.
Table of contents
- What is generative AI?
- How generative AI works
- Generative AI models
- Generative AI tools
- Generative AI use cases
- Advantages and benefits
- Disadvantages and limitations
- Future of generative AI
- Conclusion
What is generative AI?
Generative AI refers to a branch of machine learning (ML) in which models are trained on large amounts of raw data to create original outputs, such as text, images, or other content. Unlike traditional machine learning, which makes predictions based on historical data, generative AI can produce entirely new outputs. Generative AI models typically require complex neural networks and massive datasets for training.
Many generative AI systems, such as ChatGPT, are built on foundational models, which are large-scale models trained on diverse data. Foundational models can be easily fine-tuned to handle a wide variety of tasks, making them a great base on which to build generative AI applications for a wide range of tasks, like content creation and problem solving.
For example, a generative AI model could write a formal email for business communications. By learning from millions of examples, the model would understand concepts like “email structure,” “formal writing,” and “business terminology” without being directly programmed. The model would then generate an original email by predicting the most likely sequence of words that fit the requested style, format, and purpose.
How generative AI works
To best understand how generative AI works, let’s break down its operations into simple steps.
1 A user enters a prompt
Generative AI responds to prompts entered by humans. For example, someone might enter a prompt such as “Write a professional letter of acceptance for an offer of employment as a project manager.” The more specific and well-written the prompt, the more likely the model is to produce a satisfactory output. You might hear the term prompt engineering, which refers to the process of tweaking a prompt’s phrasing or including additional instructions to get higher-quality, more accurate results from a generative AI tool.
Prompts aren’t always provided as text. Depending on the type of generative AI system (more on those later in this guide), a prompt may be provided as an image, a video, or some other type of media.
2 The generative AI tool analyzes the prompt
Next, generative AI analyzes the prompt, turning it from a human-readable format into a machine-readable one. Sticking with text for the purposes of this example, the model would use natural language processing (NLP) to encode the instructions in the prompt.
This starts with splitting longer chunks of text into smaller units called tokens, which represent words or parts of words. The model analyzes those tokens in the context of grammar, sentence structure, and many other kinds of complex patterns and associations that it’s learned from its training data. This might even include prompts you’ve given the model before, since many generative AI tools can retain context over a longer conversation.
3 The tool generates a predictive output
Using everything that the model has encoded about the prompt, it tries to generate the most reasonable, statistically likely response. In essence, the model asks itself, “Based on everything I know about the world so far and given this new input, what comes next?”
For example, imagine you’re reading a story, and when you get to the end of the page, it says, “My mother answered the,” with the next word being on the following page. When you turn the page, what do you think the next word is going to be? Based on what you know about the world in general, you might have a few guesses. It could be phone, but it could also be text, call, door, or question. Knowing about what came before this in the story might help you make a more informed guess, too.
In essence, this is what a generative AI tool like ChatGPT is doing with your prompt, which is why more specific, detailed prompts help it make better outputs. It has the start of a scenario, like “Write a funny poem about a dog.” Then it tries to complete the story word by word, using its complex model of the world and the relationships in it. Crucially, generative AI tools also go through what’s called reinforcement learning with human feedback to learn to prefer responses that humans will approve of.
If you’ve played around with generative AI tools, you’ll notice that you get a different output every time—even if you ask the same question twice, the tool will respond in a slightly different way. At a very high level, the reason for this is that some amount of randomness is key to making the responses from generative AI realistic. If a tool always picks the most likely prediction at every turn, it will often end up with an output that doesn’t make sense.
Generative AI models
Generative AI models are advanced machine learning systems designed to create new data that mimics patterns found in existing datasets. These models learn from vast amounts of data to generate text, images, music, or even videos that appear original but are based on patterns they’ve seen before.
Here are some common models used in generative AI:
Large language models (LLMs)
LLMs are an application of machine learning, a type of AI that can learn from and make decisions based on data. These models use deep learning techniques to understand context, nuance, and semantics in human language. LLMs are considered “large” due to their complex architecture, with some models having hundreds of billions of parameters and requiring hundreds of gigabytes to operate. These powerful models are highly skilled in language translation, creative content generation, human-like conversations, and summarizing long documents.
Transformer models
Transformer models are the core architecture that make LLMs so powerful. Transformers introduced a new mechanism called attention, revolutionizing NLP. Unlike models that process input in sequence, the attention mechanism allows transformers to analyze relationships between all words in a sentence at once. This mechanism means that transformers can more easily capture context, leading to higher-quality language generation than models using sequential processing.
Foundational models
Foundational models are large-scale systems trained on huge amounts of varied data and can be adapted to many different tasks. This broad category of models forms the foundation for many of today’s AI systems, such as LLMs.While LLMs are specific to natural language generation, other types of foundational models can work with audio, images, or multiple data types. For example, DALL-E can work with text and images simultaneously, and vision transformers (ViT) can analyze and generate images.
Diffusion models
In a diffusion model, Gaussian noise is gradually added to training data, creating increasingly noisy versions. Adding noise affects the original values of the pixels in the image. The noise is “Gaussian” because it’s added based on probabilities that lie along a bell curve. The model learns to reverse this process, predicting a less noisy image from the noisy version. During generation, the model begins with noise and removes it according to a text prompt to create a unique image. The uniqueness of each generation is due to the probabilistic nature of the process.
Generative adversarial networks (GANs)
GAN models was introduced in 2010 and uses two neural networks competing against each other to generate realistic data. The generator network creates the content, while the discriminator tries to differentiate between the generated sample and real data. Over time, this adversarial process leads to increasingly realistic outputs. An example of an application of GANs is the generation of lifelike human faces, which are useful in film production and game development.
Variational autoencoders (VAEs)
Introduced around the same time as GANs, VAEs generate data by compacting input into what is essentially a summary of the core features of the data. The VAE then reconstructs the data with slight variations, allowing it to generate new data similar to the input. For example, a VAE trained on Picasso art could create new artwork designs in the style of Picasso by mixing and matching features it has learned.
Hybrid models
A hybrid model combines rule-based computation with machine learning and neural networks to bring human oversight to the operations of an AI system. Basically, you could take any of the above generative AI models and subject them to a rules- or logic-based system after or during their operations.
Generative AI tools
You may have already used some of the more prominent generative AI tools for work, research, or personal activities. OpenAI’s ChatGPT, for example, is commonly used for everything from writing party invitations to finding answers to esoteric and specialized questions.
ChatGPT uses a LLMs to process users’ natural language prompts and deliver straightforward, conversational responses. The tool resembles a chatbot or a message exchange with an actual person—hence its name. Google’s Gemini is another generative AI tool that uses an LLM to provide unique responses to user prompts. It works much like ChatGPT.
LLMs aren’t the only type of generative AI available to consumers. DALL-E, another generative AI innovation from OpenAI, uses a diffusion model to generate original images. For example, a user might prompt DALL-E to create an image of a frog riding a horse on a basketball court in the fauvist style of Henri Matisse. Relying on its neural network and a vast dataset, the tool would create an original image incorporating the user’s desired stylistic elements and specific requests for image content.
Those are some of the more widely known examples of generative AI tools, but various others are available. For instance, Grammarly is an AI writing tool that uses generative AI to help people improve the clarity and correctness of their writing wherever they already write.
With Grammarly’s generative AI, you can easily and quickly generate effective, high-quality content for emails, articles, reports, and other projects. Examples include group emails to your department inviting them to a company function or executive summaries for business documents.
Generative AI use cases and applications
The potential uses for generative AI span multiple industries and applications, whether professional or personal. Here are a few generative AI use cases to consider.
Healthcare
- Generating patient prescriptions based on diagnostic criteria and clinician notes
- Producing summaries based on notes taken during an appointment
- ER or telehealth triage tasks—generative AI tools can note a patient’s symptoms and produce a summary for clinicians to view before meeting with the patient
- Spotting instances of insurance fraud within large volumes of patient financial data
Banking and finance
- Autodetection of potential fraudulent activity
- Generating financial forecasts
- Providing specialized and nuanced customer support
- Creating marketing plans based on financial data about the past performance of different products and services
Marketing
- Generating different versions of landing pages for A/B testing of headlines and marketing copy
- Creating unique versions of otherwise identical sales pages for different locations
- Getting new content ideas based on performance data for existing content
- Quickly creating new images or infographics for marketing campaigns
- Generating unique musical scores for use in marketing videos
Entertainment and performances
- Creating unique imagery for promotional materials
- Building new, immersive landscapes and scenarios for virtual reality
- Rapid storyboarding for new scripts or ideas in film, television, or theater
- Improving computer-generated imagery by portraying characters in difficult- or impossible-to-film scenarios
Advantages and benefits of generative AI
Generative AI brings with it a host of advantages, including enhanced efficiency, faster development of AI applications, creative ideation, and adaptability.
Generative AI can significantly increase efficiency by automating time-consuming and tedious tasks. This productivity boost allows professionals in various fields to focus on high-value activities that require human expertise. For example, healthcare clinicians can use generative AI to assist with administrative tasks, allowing them to spend more time with patients and provide better care.
Faster development of AI applications
The foundational models that underlie generative AI support the quick development of tailored AI applications without needing to build and train a model from scratch. This reduces development requirements for organizations looking to adopt AI and accelerates deployment. For example, a software startup could use a pre-trained LLM as the base for a customer service chatbot customized for their specific product without extensive expertise or resources.
Creative ideation
Generative AI is a powerful tool for brainstorming, helping professionals to generate new drafts, ideas, and strategies. The generated content can provide fresh perspectives and serve as a foundation that human experts can refine and build upon. For example, a marketer could use generative AI to produce multiple versions of marketing copy, giving them a range of creative starting points to develop further.
Adaptability
With their transfer learning capabilities, generative AI models can easily be fine-tuned for various tasks across a wide variety of domains. This versatility makes generative AI a flexible tool that can be adapted to all sorts of needs and challenges. For example, a single LLM can be fine-tuned to write professional emails, generate marketing campaigns, and create support documentation, which allows an organization to address multiple, diverse needs with one AI system.
Disadvantages and limitations of generative AI
Generative AI is an exciting technology, but that doesn’t mean it’s perfect.
You may have heard about the attorneys who, using ChatGPT for legal research, cited fictitious cases in a brief filed on behalf of their clients. Besides having to pay a hefty fine, this misstep likely damaged those attorneys’ careers. Generative AI is not without its faults, and it’s essential to be aware of what those faults are.
Hallucinations
Sometimes, generative AI gets it wrong. When this happens, we call it a hallucination.
While the latest generation of generative AI tools usually provides accurate information in response to prompts, it’s essential to check its accuracy, especially when the stakes are high and mistakes have serious consequences. Because generative AI tools are trained on historical data, they might also not know about very recent current events or be able to tell you today’s weather.
Bias
Several prominent generative AI tools output information that contains racial and/or gender bias. In some cases, the tools themselves admit to their prejudice.
This happens because the tools’ training data was created by humans: Existing biases among the general population are present in the data generative AI learns from.
Privacy and security concerns
From the outset, generative AI tools have raised privacy and security concerns. For one thing, prompts that are sent to models may contain sensitive personal data or confidential information about a company’s operations. How will these tools protect that data and ensure that users have control over their information?
As with any software, there’s also the potential for generative AI tools to be hacked. This could result in inaccurate content that damages a company’s reputation or exposes users to harm. And when you consider that generative AI tools are now being used to take independent actions like automating tasks, it’s clear that securing these systems is a must.
When using generative AI tools, make sure you understand where your data is going and do your best to partner with tools that commit to safe and responsible AI innovation.
The future of generative AI
For organizations, generative AI isn’t just software. It’s a junior member of your team. This is true in virtually every industry, so it’s essential to be ready for how AI will change the way you work.
According to Gartner, businesses should create AI plans that account for:
- Opportunity ambition: The specific advantages you or your organization hopes to enjoy after deploying AI tools.
- Deployment: Decide whether to use off-the-shelf tools, build in-house, or train the model using your own proprietary data.
- Risk: Organizations need to account for all potential risks inherent in AI, such as reliability, security, data privacy.
According to IBM, we should also expect governments around the world to make AI management a priority. The European Union, for example, is currently working to categorize AI into different risk categories and mandate certain rules about its use and deployment.
With those activities in mind, it’s essential to consider the ethical implications of generative AI and what it means to pursue responsible development. The World Economic Forum has published a list of factors that AI innovators must consider when developing and deploying new AI systems. They include:
- Efficacy of the AI system for what it is intended to do
- Resilience against security threats
- Bias mitigation to avoid the unjust treatment of marginalized groups
- Explainability, which means the outputs of an AI system should be understandable to users
- Privacy protection, which includes data minimization principles
The bottom line is that AI is here to stay. In the months and years ahead, expect institutions and governments to pay ever-increasing attention to the challenge of responsible innovation in the world of generative AI.
Conclusion: Make the most of generative AI
Generative AI is a force to be reckoned with across many industries, not to mention everyday personal activities. As individuals and businesses continue to adopt generative AI into their workflows, they will find new ways to offload burdensome tasks and collaborate creatively with this technology.
At the same time, it’s important to be aware of the technical limitations and ethical concerns inherent to generative AI. Responsible development is one thing—and it matters—but responsible use is also critical. Always double-check that the content created by generative AI tools is what you really want. And if you’re not getting what you expected, spend the time understanding how to optimize your prompts to get the most out of the tool. Navigate responsible AI use with Grammarly’s AI checker, trained to identify AI-generated text.
By staying abreast of the latest innovations in generative artificial intelligence, you can improve how you work and enhance your personal projects. While exciting, the current generation of AI tools offers merely a glimpse of what lies beyond the horizon.
