What is Generative AI?

 WHAT IS GENERATIVE AI?

Generative AI, sometimes called gen AI, is artificial intelligence (AI) that can create original content such as text, images, video, audio or software code in response to a user’s prompt or request.

Generative AI relies on sophisticated machine learning models called deep learning models algorithms that simulate the learning and decision-making processes of the human brain. These models work by identifying and encoding the patterns and relationships in huge amounts of data, and then using that information to understand users' natural language requests or questions and respond with relevant new content.

AI has been a hot technology topic for the past decade, but generative AI, and specifically the arrival of ChatGPT in 2022, has thrust AI into worldwide headlines and launched an unprecedented surge of AI innovation and adoption. Generative AI offers enormous productivity benefits for individuals and organizations, and while it also presents very real challenges and risks, businesses are forging ahead, exploring how the technology can improve their internal workflows and enrich their products and services. According to research by the management consulting firm McKinsey, one third of organizations are already using generative AI regularly in at least one business function.¹ Industry analyst Gartner projects more than 80% of organizations will have deployed generative AI applications or used generative AI application programming interfaces (APIs) by 2026.

How generative AI works

Generative AI works on the principles of machine learning, a branch of artificial intelligence that enables machines to learn from data. However, unlike traditional machine learning models that learn patterns and make predictions or decisions based on those patterns, generative AI takes a step further — it not only learns from data but also creates new data instances that mimic the properties of the input data.

Across the major generative AI models – discussed in more detail below – the general workflow for putting generative AI to work is as follows:

• Data collection: A large dataset containing examples of the type of content to be generated is collected. For example, a dataset of images for generating realistic pictures, or a dataset of text for generating coherent sentences.

• Model training: The generative AI model is constructed using neural networks. The model is trained on the collected dataset to learn the underlying patterns and structures in the data.

• Generation: Once the model is trained, it can generate new content by sampling from the latent space or through a generator network depending on the model used. The generated content is a synthesis of what the model has learned from the training data.

• Refinement: Depending on the task and application, the generated content may undergo further refinement or post-processing to improve its quality or to meet specific requirements.

The cornerstone of generative AI is deep learning, a type of machine learning that imitates the workings of the human brain in processing data and creating patterns for decision-making. Deep learning models use complex architectures known as artificial neural networks. Such networks comprise numerous interconnected layers that process and transfer information, mimicking neurons in the human brain.

Generative AI by Numbers

• By 2025, generative AI will generate 10 percent of all data. Compare that number to one percent today, and you’ll see its significance!
• An estimated 33.2%compound annual growth rate for AI between 2020 and 2027 suggests that we’re entering an “AI-enhanced” era.
• By 2027, 30% of manufacturers will use generative AI to make product development more effective.

These figures show that generative AI is laying the groundwork for a future that requires less employee training. Yet greater efficiency in creating unique content will be faster and less costly.

Generative AI & Visual Marketing

As colorful art, generative AI is finding its way through various forms of marketing. And when it comes to digital marketing, our story gets even more interesting as we see more and more generative AI applications in content marketing – especially visual marketing.

For example, German automakers have recently tried using generative AI. BMW, for example, uses this art to link many data points in its advertising campaigns. This includes BMW images, descriptions, and content created for each vehicle. 

The company has been instrumental in developing or using AI software to link more than 500,000 photos.