Generative artificial intelligence has rapidly gained traction amongst businesses, professionals, and consumers. But what is generative AI, how does it work, and what is all the buzz about? Read on to find out.
What is generative AI in simple terms?
Generative AI is a type of artificial intelligence capable of generating new content — including text, images, or code — often in response to a prompt entered by a user. Its models are increasingly incorporated into online tools and chatbots, which allow users to type questions or instructions into an input field. In the output field, the AI model will generate a human-like response.
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How does generative AI work?
Generative AI uses a computing process known as deep learning to analyze patterns in large sets of data and replicate those patterns to create new data that mimics human-generated data. It employs neural networks, a type of machine learning process loosely inspired by the way the human brain processes, interprets, and learns from information over time.
For example, if you were to feed fictional writing into a generative AI model, it would eventually learn to craft stories or story elements based on this literature — which could include elements such as plot structure, characters, themes, and other narrative devices. This is because the machine learning algorithms powering generative AI models learn from the information they’re fed.
Generative AI models get more sophisticated over time. The more data a model is trained on and generates, the more convincing and human-like its outputs become.
Examples of generative AI
The popularity of generative AI has exploded in recent years, largely thanks to the arrival of OpenAI’s ChatGPT and DALL-E models, which put accessible AI tools into the hands of consumers.
Since then, tech giants including Google, Microsoft, Amazon, and Meta have launched their own generative AI tools to capitalize on the technology’s rapid uptake:
- Google integrates generative AI into Search with AI Overviews.
- Microsoft incorporates the Copilot AI into PCs. Redmond plans to build generative AI even more deeply into its PCs with Recall, although the feature has been restricted to a preview for Windows Insiders following security concerns.
- Apple released Apple Intelligence, a mix of proprietary AI models and OpenAI technology, in iOS 18, iPadOS 18, and macOS Sequoia later this year.
Various generative AI tools now exist, although text and image generation models are arguably the most well-known. Google and Meta have both demonstrated photorealistic image generators, although these are not publicly available as of October 2024. Generative AI models typically rely on a user feeding a prompt into the engine, which then guides it towards producing some sort of desired output — such as text, images, videos or music, though this isn’t always the case.
Examples of generative AI models and products include:
GPT-4: OpenAI’s flagship generative AI model comes in a variety of sizes. It can be accessed through an API or through the major model-hosting platforms.
ChatGPT: An AI language chatbot developed by OpenAI that can answer questions and generate human-like responses from text prompts. It runs on GPT-4.
DALL-E 3: Another AI model by OpenAI, DALL-E 3 can create images and artwork from text prompts.
Google Gemini: Previously known as Bard, Gemini is Google’s generative AI chatbot and rival to ChatGPT. It’s trained on the PaLM large language model and can answer questions and generate text from prompts.
Claude 3.5: Anthropic’s AI model, Claude, offers a 200,000 token context window.
Midjourney: Developed by San Francisco-based research lab Midjourney Inc., this gen AI model interprets text prompts to produce images and artwork, similar to DALL-E.
GitHub Copilot: An AI-powered coding tool, GitHub Copilot suggests code completions within the Visual Studio, Neovim, and JetBrains development environments.
Llama 3: Meta’s open-source large language model can be used to create conversational AI models for chatbots and virtual assistants.
Grok: After co-founding and helping to fund OpenAI, Elon Musk left the project in July 2023 and announced this new generative AI venture. Its first model, Grok, debuted in November 2023.
Types of generative AI models
Various types of generative AI models exist, each designed for specific tasks and purposes. These can broadly be categorized into the following types:
Transformer-based models
Transformer-based models are trained on large sets of data to understand the relationships between sequential information such as words and sentences. Underpinned by deep learning, transformer-based models tend to be adept at natural language processing and understanding the structure and context of language, making them well suited for text-generation tasks. ChatGPT-4 and Google Gemini are examples of transformer-based generative AI models.
Generative adversarial networks
Generative adversarial networks comprise two neural networks known as a generator and a discriminator. The neural networks essentially work against each other to create authentic-looking data. This technique was first developed in 2024. The generator’s role is to create convincing output, such as an image based on a prompt, while the discriminator works to evaluate the authenticity of said image. Over time, each component gets better at their respective roles, resulting in more convincing outputs. DALL-E and Midjourney are examples of GAN-based generative AI models.
Variational autoencoders
Variational autoencoders leverage two networks to interpret and generate data — in this case, an encoder and a decoder. The encoder takes the input data and compresses it into a simplified format. The decoder then takes this compressed information and reconstructs it into something new that resembles the original data but isn’t entirely the same. This neural network architecture was first described in 2013.
For example, variational autoencoding could include teaching a computer program to generate human faces using photos as training data. Over time, the program learns how to simplify the photos of people’s faces into a few important characteristics — such as the size and shape of the eyes, nose, mouth, ears, and so on — and then use these to create new faces.
This type of VAE might be used to increase the diversity and accuracy of facial recognition systems. By using VAEs to generate new faces, facial recognition systems can be trained to recognize more diverse, less common facial features.
Multimodal models
Multimodal models can understand and process multiple types of data simultaneously, such as text, images, and audio, allowing them to create more sophisticated outputs. An example might be an AI model capable of generating an image based on a text prompt, as well as a text description of an image prompt. DALL-E 3 and OpenAI’s GPT-4 are examples of multimodal models.
What is ChatGPT?
ChatGPT is an AI chatbot developed by OpenAI. It is based on GPT-4, a large language model using transformer architecture — specifically, the generative pretrained transformer, hence GPT — to understand and generate human-like text. ChatGPT popularized the use of generative AI for personal and professional work.
You can learn everything you need to know about ChatGPT in this TechRepublic cheat sheet.
What is Google Gemini?
Google Gemini (previously Bard) is another example of an LLM based on transformer architecture. Similar to ChatGPT, Gemini is a generative AI chatbot that generates responses to user prompts.
Google launched Bard in the U.S. in March 2023 in response to OpenAI’s ChatGPT and Microsoft’s Copilot AI tool. It was launched in Europe and Brazil later in 2023.
Learn more about Gemini by reading TechRepublic’s comprehensive Google Gemini cheat sheet.
SEE: Google Gemini vs. ChatGPT: Is Gemini Better Than ChatGPT? (TechRepublic)
Benefits of generative AI
Efficiency can affect a company’s bottom line. Generative AI can help automate specific tasks and focus employees’ time, energy, and resources on more important strategic objectives. This can result in lower labor costs, greater operational efficiency, and additional insights into how well certain business processes perform.
For professionals and content creators, generative AI tools can help with idea creation, content planning and scheduling, search engine optimization, marketing, audience engagement, research and editing, and potentially more. However, manual oversight and scrutiny of generative AI models remain highly important.
Use cases of generative AI
McKinsey estimates that activities currently accounting for around 30% of U.S. work hours could be automated by 2030, prompted by the acceleration of generative AI.
SEE: Indeed’s 10 Highest-Paid Tech Skills: Generative AI Tops the List
Generative AI has found a foothold in a number of industry sectors and is now popular in both commercial and consumer markets. The use of generative AI varies by industry and is more established in some than in others. Current and proposed use cases include the following:
- Healthcare: Generative AI is being explored to help accelerate drug discovery, while tools such as AWS HealthScribe allow clinicians to transcribe patient consultations and upload important information into their electronic health record.
- Digital marketing: Advertisers, salespeople, and commerce teams can use generative AI to craft personalized campaigns and adapt content to consumers’ preferences, especially when combined with customer relationship management data.
- Education: Some educational tools are beginning to incorporate generative AI to develop customized learning materials to cater to students’ individual learning styles.
- Finance: Generative AI is one of the many tools within complex financial systems to analyze market patterns and anticipate stock market trends. It is also used alongside other forecasting methods to assist financial analysts.
- Environment: Researchers use generative AI models to predict weather patterns and simulate the effects of climate change.
In terms of role-specific use cases of generative AI, some examples include:
- In customer support, AI-driven chatbots and virtual assistants can help businesses reduce response times and quickly deal with common customer queries, reducing the burden on staff.
- In software development, generative AI tools can help developers code more cleanly and efficiently by reviewing code, highlighting bugs, and suggesting potential fixes before they become bigger issues.
- Writers can use generative AI tools to plan, draft, and review essays, articles, and other written work — though often with mixed results.
What are the risks of using generative AI?
Biases and misinformation
A major concern around the use of generative AI tools — and particularly those accessible to the public — is their potential for spreading misinformation and harmful content. The impact of biases and misinformation can be wide-ranging and severe, from perpetuating stereotypes, hate speech, and harmful ideologies, to damaging personal and professional reputation.
SEE: Gartner analyst’s take on 5 ways generative AI will impact culture & society
The risk of legal and financial repercussions from the misuse of generative AI is very real; indeed, it has been suggested generative AI could put national security at risk if used improperly or irresponsibly.
These risks haven’t escaped policymakers. On Feb. 13, 2024, the European Council approved the AI Act, a first-of-kind piece of legislation designed to regulate the use of AI in Europe. The legislation takes a risk-based approach to regulating AI, with some AI systems banned outright.
Cybersecurity concerns
Security agencies have made moves to ensure AI systems are built with safety and security in mind. In November 2023, 16 agencies, including the U.K.’s National Cyber Security Centre and the U.S. Cybersecurity and Infrastructure Security Agency released the Guidelines for Secure AI System Development, which promote security as a fundamental aspect of AI development and deployment. Additionally, a survey released in October 2024 found that AI, including generative AI, expertise has become the most in-demand skill amongst IT managers in the U.K.
Ethical and privacy concerns
Generative AI has prompted workforce concerns, most notably that the automation of tasks could lead to job losses. Research from McKinsey suggests that around 12 million people may need to switch jobs by 2030, with office support, customer service, and food service roles most at risk. The consulting firm predicts clerks will see a reduction of 1.6 million jobs, “in addition to losses of 830,000 for retail salespersons, 710,000 for administrative assistants and 630,000 for cashiers.”
SEE: Establish guidelines to propel your company into the future safely with our generative AI use policy template.
Because generative AI models are often trained on internet-sourced information, generative AI companies may clash with media companies over the use of published work.
If an organization implements Generative AI systems, IT and cybersecurity professionals should carefully delineate where the model can and cannot access data.
Environmental concerns
Generative AI can increase emissions and rapidly deplete water sources. The data centers needed to run generative AI have become a key conversation in the debates over the Earth’s future energy needs.
What is the difference between generative AI and machine learning?
Generative AI is a subfield of artificial intelligence. Broadly, AI refers to the concept of computers capable of performing tasks that would otherwise require human intelligence, such as decision making and natural language processing. Generative AI models use machine learning techniques to process and generate data.
Machine learning is the foundational component of AI and refers to the application of computer algorithms to data for the purposes of teaching a computer to perform a specific task. Machine learning is the process that enables AI systems to make informed decisions or predictions based on the patterns they have learned.
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What is the difference between generative AI and traditional AI?
Different people interpret these terms in different contexts. However, in many cases, “traditional AI” refers to machine learning. “Traditional AI” predicts outcomes or rearranges information. Generative AI “generates” new content based on training data not necessarily directly related to the input it receives.
SEE: The confusion around types of AI and requirements for deploying them can lead to unnecessary costs in business.
The term “general AI” comes up in forward-looking conversations about this technology. General AI, also known as artificial general intelligence, broadly refers to the concept of computer systems and robotics that possess human-like intelligence and autonomy. Most current AI systems are examples of “narrow AI” compared to these, in that they’re designed for very specific tasks.
To learn more about artificial intelligence, read our comprehensive AI cheat sheet.
What is the difference between generative AI and discriminative AI?
Whereas generative AI is used for generating new content by learning from existing data, discriminative AI specializes in classifying or categorizing data into predefined groups or classes.
Discriminative AI learns to distinguish between different types of data, making it ideal for tasks requiring sorting data into categories. For example, it can identify whether an email is spam, recognize objects in an image, or diagnose diseases from medical scans. By analyzing known data, it classifies new data correctly.
While generative AI is designed to create original content or data, discriminative AI is used for analyzing and sorting it, making each useful for different applications.
What is the difference between generative AI and regenerative AI?
Regenerative AI, while less commonly discussed, refers to AI systems that can fix themselves or improve over time without human help. The concept of regenerative AI is centered around building AI systems that can last longer and work more efficiently, potentially even helping the environment by making smarter decisions that result in less waste. Like artificial general intelligence, regenerative AI is theoretical.
In this way, generative AI and regenerative AI serve different roles: Generative AI for creativity and originality, and regenerative AI for durability and sustainability within AI systems.
How big a role will generative AI play in the future of business?
As more businesses embrace digitization and automation, generative AI looks set to play a central role in various industries, with many organizations already establishing guidelines for the acceptable use of AI in the workplace. The capabilities of generative AI have already proven valuable in areas such as content creation, software development, medicine, productivity, business transformation, and much more. As the technology continues to evolve, so too will generative AI’s applications and use cases.
SEE: Deloitte’s 2024 Tech Predictions: Gen AI Will Continue to Shape Chips Market
That said, the impact of generative AI on businesses, individuals, and society as a whole is contingent on properly addressing and mitigating its risks. Key to this is ensuring AI is used ethically by reducing biases, enhancing transparency, and accountability, as well as upholding proper data governance.
None of this will be straightforward. Keeping laws up to date with fast-moving tech is tough but necessary, and finding the right mix of automation and human involvement will be key to democratizing the benefits of generative AI. Recent legislation, such as President Biden’s Executive Order on AI, Europe’s AI Act, and the U.K.’s Artificial Intelligence Bill, suggests that governments around the world understand the importance of getting on top of these issues quickly.
Future of generative AI
Generative AI companies continue to try to push the envelope by creating higher-parameter models, photorealistic AI video, and incorporating AI closely into enterprise software. One potential change generative AI might bring to computing is the use of natural language commands to both find information and command the system.
“Agentic” AI — where teams of generative AI “agents” work together to solve multi-step, multivariable problems — is often cited as the future of the technology. In 2024, OpenAI released with great fanfare its OpenAI o1 model, trading speed for complex coding and math processes.
