Generative AI is a groundbreaking technology capable of creating a wide range of content, including text, images, audio, and synthetic data. Its recent popularity stems from user-friendly interfaces that enable the quick production of high-quality content in seconds. While generative AI isn’t entirely new, having roots in 1960s chatbots, it truly evolved in 2014 with the advent of generative adversarial networks (GANs). These machine learning algorithms can create lifelike images, videos, and audio, significantly enhancing the technology’s capabilities.
The emergence of GANs has opened up various opportunities, such as improved movie dubbing and enriched educational content. However, it also raises concerns about deepfakes—realistic but fake digital images or videos—and potential cybersecurity threats where malicious actors could mimic someone’s boss convincingly. Two key advancements have been instrumental in bringing generative AI into the mainstream: transformers and the advanced language models they support.
Transformers are a type of machine learning that enables researchers to train large models without pre-labeling all data. This advancement allows models to be trained on billions of text pages, resulting in more comprehensive and insightful answers. Additionally, transformers introduced the concept of attention, which allows models to understand the relationships between words across entire texts, not just within sentences. This ability extends beyond text to include code, proteins, chemicals, and DNA.
The rapid development of large language models (LLMs)—featuring billions or even trillions of parameters—has ushered in an era where generative AI can create engaging text, photorealistic images, and even on-the-fly entertainment. Multimodal AI innovations further allow the generation of content across various media, including text, graphics, and video. This capability underpins tools like DALL-E, which can create images from text descriptions or generate captions from images.
Despite these advancements, generative AI is still in its early stages. Initial implementations have struggled with accuracy, bias, and hallucinations, sometimes producing bizarre or incorrect outputs. Nevertheless, the technology’s potential suggests it could revolutionize enterprise technology and business operations. Future applications will include writing code, designing new drugs, developing products, redesigning business processes, and transforming supply chains.
Generative AI operates by starting with a prompt—text, image, video, design, musical notes, or any other input the AI system can process. Various algorithms then generate new content in response to the prompt. This content can range from essays and problem solutions to realistic fakes created from pictures or audio.
In the past, using generative AI involved submitting data via an API or a complex process requiring developers to use specialized tools and languages like Python. Today, pioneers in the field are creating better user experiences, allowing users to make requests in plain language and customize results based on feedback regarding style, tone, and other elements.
Generative AI models combine different algorithms to represent and process content. For text generation, natural language processing techniques transform raw characters into structured sentences, parts of speech, entities, and actions, represented as vectors through various encoding methods. Similarly, images are converted into visual elements, also expressed as vectors. However, these techniques can encode biases, racism, and other negative aspects present in the training data.
Once developers establish a way to represent the world, they apply neural networks to generate new content in response to a query. Techniques such as GANs and variational autoencoders (VAEs)—neural networks with a decoder and encoder—are effective for creating realistic human faces, synthetic data for AI training, or facsimiles of specific individuals.
Recent progress in transformers, like Google’s Bidirectional Encoder Representations from Transformers (BERT), OpenAI’s GPT, and Google AlphaFold, has led to neural networks that can encode and generate new content in language, images, and proteins. This continual evolution in generative AI signifies a significant shift in how we create and interact with digital content, promising even more sophisticated applications in the future.