What Is Generative AI (GenAI)?

What Is Generative AI?

Generative AI is a form of artificial intelligence that can analyze large datasets based on criteria extracted from prompts to create new content—including text, images, videos, audio, and code—with the same patterns and structures. GenAI models continue to train on or learn from available data, providing the end consumers of the content new and continuously evolving responses.

Generative AI solutions are being used across industries to inspire creativity, improve business processes, provide better experiences, and inform decision-making. New implementations are making headlines every day, and organizations everywhere are seeking ways to operationalize GenAI and capture its game-changing value.

However, thought leaders also note serious and plausible concerns with using GenAI, including job displacement, privacy concerns, the potential for misinformation, and ethical dilemmas. Accordingly, it’s crucial that companies make a conscious effort to understand and mitigate the risks as they explore and implement generative AI solutions.

GenAI Benefits

The value of GenAI is realized through how its generated content is used by individuals and businesses to improve daily life and achieve goals:
 

• Personalization: Generative AI can be used to personalize products, services, and experiences based on individual preferences and needs. For example, in healthcare GenAI can help to generate personalized treatment plans based on a patient’s medical history and test results. Financial organizations can use the technology to generate investment recommendations based on market data and customer preferences.
• Improved customer service: Generative AI can automate repetitive tasks and provide more-efficient and -effective customer service. This can help customers get their questions answered more quickly and resolve issues more easily.
• Increased creativity: Generative AI can create new and unique content, such as music, art, and writing, based on patterns and preferences. For example, in retail GenAI can help generate product descriptions and images for customer-facing e-commerce websites.
• Improved accessibility: Generative AI can be used to make products and services more accessible for people with disabilities, such as by generating captions for videos or converting text to speech.
• Increased efficiency: Generative AI can automate repetitive and time-consuming tasks, such as data entry, document review, and language-related tasks. This can help organizations and customers be more productive and achieve their goals more easily. For example, in transportation and logistics, GenAI can be used to generate delivery schedules based on traffic data and customer preferences.
• Better decision-making: Generative AI can generate insights and recommendations based on large amounts of data, making it easier for individuals and businesses to make informed decisions. For example, in manufacturing organizations can use GenAI to generate ideas for new product designs based on existing products and customer preferences.
• New and exciting experiences: Generative AI can create new and exciting experiences, such as virtual and augmented reality, that would not be possible without the technology.

How GenAI Works

Implementing a generative AI solution for any use case requires significant efforts from data scientists and developers. That’s because GenAI is made possible by massive datasets and intricately trained AI algorithms. The technology is built on and deployed in conjunction with language AI and natural language processing (NLP), which allow artificial intelligence to process and understand human language. Together, GenAI and NLP can understand a user prompt to generate an appropriate response, whether text, video, imagery, or audio.

Generative AI solutions make use of AI systems called large language models (LLMs) that employ deep neural networks to process and generate text. They’re trained on massive amounts of data, working to find commonalities between similar data types and information to create and deliver new, coherent outputs.

LLMs rely on transformer architectures to process input sequences in a parallel fashion, which improves performance and speed compared to traditional neural networks. Model training is also informed by the input of data scientists and subject matter experts who help guide the algorithm’s learning and shepherd it toward more-accurate outputs.

To enable GenAI solutions, businesses can either train GenAI models from scratch or select a pretrained model that can be fine-tuned to their specific needs. For example, a GenAI chatbot algorithm can be trained to the specific attributes of an organization’s customer base and business model. Or a model intended to generate text for content marketing can be further specialized or fine-tuned to focus on a specific industry and audience. More domain-specific models are also emerging at a rapid pace. These are trained on smaller, more targeted datasets than larger models. Emerging results indicate these smaller models can replicate the accuracy of larger ones if trained on carefully sourced data.

Developers may also use retrieval augmented generation (RAG) to supplement pretrained models with up-to-date, proprietary, and confidential data from vector databases during inference. This simplifies customization and updating and enables attribution of generated information to its source.

Here’s a brief (though technically advanced) overview of three common types of GenAI models:
 

• Generative adversarial networks (GANs): These involve two neural networks, a generator and a discriminator, which are trained simultaneously. The generator creates new data instances, while the discriminator evaluates them against a set of real data. The generator’s goal is to produce data that is indistinguishable from real data, while the discriminator’s goal is to correctly distinguish between the two. Over time, the generator improves, creating increasingly realistic data.
• Variational autoencoders (VAEs): VAEs combine principles from neural networks and probabilistic modeling to generate new data instances through an encoding and decoding process. A VAE model starts by compressing input data into a simplified representation of its characteristics. Then it decodes that simplified version, attempting to reconstruct the input data to its original form. Through this process, the VAE learns the essential underlying features and parameters of the data, helping it to generate more-realistic and customizable outputs.
• Transformer models: Models like Generative Pre-trained Transformer (GPT) can generate highly coherent and contextually relevant text. These models, originally designed for natural language processing tasks, are trained on large datasets and can complete text prompts, translate languages, answer questions, and even generate creative writing.

Various strategies can be used during the generation process to balance creativity and coherence in the outputs. Ongoing research aims to make these models more transparent, reduce biases, and improve factual accuracy. There has also been movement towards models that can simultaneously work with multiple types of data, such as text, images, and audio.

How GenAI Is Used

How businesses apply GenAI depends on the business challenge they seek to solve.

Text Generation

Perhaps the most common use of GenAI is text generation, in which the technology—supported by large neural networks—can understand and create human-like text. Text generation has been around for decades but has gained measurable momentum in the past several years. Popular uses include chatbots, marketing content, language translation services, summarization tools, customer support responses, and business reports. If a project requires any kind of writing, an organization may choose to explore text generation as part of the creative process.

Image, Video, Speech, and Music Generation

Using massive datasets of millions of existing images as a foundation, GenAI can learn patterns and then create new, original images based on criteria included in text prompts. Advertising, gaming, and product design companies use the technology to quickly explore and expand on creative concepts and accelerate prototyping processes.

Organizations are also using GenAI to create video and speech. Whether generating extra frames for an existing video, creating an entirely new scene, or manipulating or adding speech or audio, the potential time and cost savings of using GenAI to accomplish these tasks is appealing in many cases.

GenAI can make music as well by using neural networks trained on vast musical datasets to understand structure, style, and emotional content. Music is highly subjective, so whether listeners like the output is a matter of personal taste—much like it is for human-created music.

Code Generation

GenAI can enhance developer productivity by producing code, which it accomplishes by learning patterns from existing codebases and documentation. The technology can generate functions, classes, or entire programs based on natural language prompts or specifications. Many organizations use GenAI to accelerate software development, automate routine coding tasks, and assist in debugging—while simultaneously searching for the appropriate amount of human oversight to ensure quality, security, and alignment with project requirements.

Chatbots

Fast, efficient, and useful customer service is a nonnegotiable requirement for any organization. That’s why so many are implementing dynamic and intelligent conversational AI models that customers can interact with through text or speech. GenAI powers chatbots by understanding and generating human-like text responses. In addition to customer service, AI chatbots can supplement marketing efforts and support internal communications. They can also be integrated into websites, messaging apps, or voice assistants.

Data Augmentation

Using GenAI, developers can create synthetic data to augment training datasets for machine learning and deep learning models or help improve model performance and generalization. The technology can generate AI variations of images, text, or other data types, helping to expand limited datasets.

Challenges of GenAI

As with most nascent and continually evolving technologies, there are challenges to implementing and using generative AI. Principally, decision-makers should be aware of data security and privacy risks, computational resourcing and expense, and ethical and societal implications, including the chance of spreading misinformation.

Use Case-Specific Challenges

Each generative AI use case presents its own challenges:
 

• Text generation: Despite the incredible strides made seemingly every day, text generation is far from foolproof. It’s therefore crucial that actual human beings oversee the process, ensure the accuracy and appropriateness of the generated content, and, in many instances, provide original, thoughtful, and valuable ideas and language to a first draft created by GenAI technology.
  Moreover, for both creative and legal reasons, organizations should implement guidelines for the responsible use of text generation, addressing potential biases and verifying AI-generated content before publishing it.
• Image, video, speech, and music generation: As with text generation, there are risks and potential concerns when using GenAI to create images, particularly surrounding creativity, authenticity, and intellectual property rights. Talented human designers are required not only to effectively prompt GenAI tools but also to review, refine, and customize the images produced by the technology.
  Potential challenges with GenAI video and speech include significant ethical risks, from inadvertent misrepresentation to deepfakes. Accordingly, GenAI video and speech should be used responsibly, preferably by professionals adhering to formal brand guidelines and organizational oversight.
  When using GenAI to create music, organizations should note that musicians have been writing, performing, and sharing songs, sounds, and beats for thousands of years, and they arguably bring uniquely human advantages to the process and the results.
• Chatbots: Because GenAI chatbots can handle routine inquiries 24/7, they help free up human beings to handle more-complex issues. But as with other GenAI implementations, there are limits to what the technology can accomplish. Organizations should ensure that human support is available when needed. Furthermore, effective implementation requires continuous monitoring and refinement based on user interactions and feedback.
• Data augmentation: In addition to its practical benefits, data augmentation can help reduce bias in datasets and make models more robust. However, organizations must ensure that the synthetic data accurately represents real-world scenarios and doesn’t introduce new biases or errors.

Responsible AI Considerations

Leaders in AI innovation are collaborating on and committing to responsible AI practices to lower these risks while maximizing the benefits of the technology for society. Key attributes of responsible AI include:
 

• Developing, assessing, and deploying AI systems in a safe, trustworthy, and ethical way, ensuring inclusive AI that’s created by diverse teams.
• Respecting human rights, enabling human oversight, requiring transparency and explainability, and committing to security, safety, reliability, personal privacy, equity and inclusion, and environmental sustainability.
• Adhering and attributing to strong internal and external governance.
• Engaging in multistakeholder research and collaboration initiatives to help ease the burden of responsible AI development for all.

Future of GenAI

Optimism and energy are driving organizations everywhere to investigate generative AI solutions that create efficiencies and drive new business opportunities. Looking ahead, we will likely see new implementations in fields ranging from scientific research to design:
 

• Decision-making processes will be enhanced through AI-generated scenarios and predictions, offering valuable insights in nearly every industry.
• Product design and prototyping processes will become more efficient and innovative, accelerating time to market.
• Chatbots and virtual assistants will evolve to handle more-complex interactions, improving customer service and internal support.
• Personalized content creation at scale may revolutionize marketing and customer engagement strategies.

The possibilities are as exciting as they are immeasurable. However, organizations will need to develop robust ethical artificial intelligence frameworks and governance structures to ensure responsible use—and must adapt their workflows and upskill their workforce to innovate and take advantage of the wide and ever-increasing range of opportunities.