What is Gen AI?
Generative artificial intelligence, also known as generative AI or gen AI for short, is a type of AI that can create new content and ideas, including conversations, stories, images, videos, and music. It can learn human language, programming languages, art, chemistry, biology, or any complex subject matter. It reuses what it knows to solve new problems.
For example, it can learn English vocabulary and create a poem from the words it processes.
Your organization can use generative AI for various purposes, like chatbots, media creation, product development, and design.
Generative AI examples
Generative AI has several use cases across industries
Financial services
Financial services companies use generative AI tools to serve their customers better while reducing costs:
- Financial institutions use chatbots to generate product recommendations and respond to customer inquiries, which improves overall customer service.
- Lending institutions speed up loan approvals for financially underserved markets, especially in developing nations.
- Banks quickly detect fraud in claims, credit cards, and loans.
- Investment firms use the power of generative AI to provide safe, personalized financial advice to their clients at a low cost.
Healthcare and life sciences
One of the most promising generative AI use cases is accelerating drug discovery and research. Generative AI can create novel protein sequences with specific properties for designing antibodies, enzymes, vaccines, and gene therapy.
Healthcare and life sciences companies use generative AI tools to design synthetic gene sequences for synthetic biology and metabolic engineering applications. For example, they can create new biosynthetic pathways or optimize gene expression for biomanufacturing purposes.
Generative AI tools also create synthetic patient and healthcare data. This data can be useful for training AI models, simulating clinical trials, or studying rare diseases without access to large real-world datasets.
Read more about Generative AI in Healthcare & Life Sciences on AWS
Automotive and manufacturing
Automotive companies use generative AI technology for many purposes, from engineering to in-vehicle experiences and customer service. For instance, they optimize the design of mechanical parts to reduce drag in vehicle designs or adapt the design of personal assistants.
Auto companies use generative AI tools to deliver better customer service by providing quick responses to the most common customer questions. Generative AI creates new materials, chips, and part designs to optimize manufacturing processes and reduce costs.
Another generative AI use case is synthesizing data to test applications. This is especially helpful for data not often included in testing datasets (such as defects or edge cases).
Telecommunication
Generative AI use cases in telecommunication focus on reinventing the customer experience defined by the cumulative interactions of subscribers across all touchpoints of the customer journey.
For instance, telecommunication organizations apply generative AI to improve customer service with live human-like conversational agents. They reinvent customer relationships with personalized one-to-one sales assistants. They also optimize network performance by analyzing network data to recommend fixes.
Media and entertainment
From animations and scripts to full-length movies, generative AI models produce novel content at a fraction of the cost and time of traditional production.
Other generative AI use cases in the industry include:
- Artists can complement and enhance their albums with AI-generated music to create whole new experiences.
- Media organizations use generative AI to improve their audience experiences by offering personalized content and ads to grow revenues.
- Gaming companies use generative AI to create new games and allow players to build avatars.
Generative AI benefits
According to Goldman Sachs, generative AI could drive a 7 percent (or almost $7 trillion) increase in global gross domestic product (GDP) and lift productivity growth by 1.5 percentage points over ten years. Next, we give some more benefits of generative AI.
How did generative AI technology evolve?
Primitive generative models have been used for decades in statistics to aid in numerical data analysis. Neural networks and deep learning were recent precursors for modern generative AI. Variational autoencoders, developed in 2013, were the first deep generative models that could generate realistic images and speech.
VAEs
VAEs (variational autoencoders) introduced the capability to create novel variations of multiple data types. This led to the rapid emergence of other generative AI models like generative adversarial networks and diffusion models. These innovations were focused on generating data that increasingly resembled real data despite being artificially created.
Transformers
In 2017, a further shift in AI research occurred with the introduction of transformers. Transformers seamlessly integrated the encoder-and-decoder architecture with an attention mechanism. They streamlined the training process of language models with exceptional efficiency and versatility. Notable models like GPT emerged as foundational models capable of pretraining on extensive corpora of raw text and fine-tuning for diverse tasks.
Transformers changed what was possible for natural language processing. They empowered generative capabilities for tasks ranging from translation and summarization to answering questions.
The future
Many generative AI models continue to make significant strides and have found cross-industry applications. Recent innovations focus on refining models to work with proprietary data. Researchers also want to create text, images, videos, and speech that are more and more human-like.
How does generative AI work?
Like all artificial intelligence, generative AI works by using machine learning models—very large models that are pre-trained on vast amounts of data.
Foundation models
Foundation models (FMs) are ML models trained on a broad spectrum of generalized and unlabeled data. They are capable of performing a wide variety of general tasks.
FMs are the result of the latest advancements in a technology that has been evolving for decades. In general, an FM uses learned patterns and relationships to predict the next item in a sequence.
For example, with image generation, the model analyzes the image and creates a sharper, more clearly defined version of the image. Similarly, with text, the model predicts the next word in a string of text based on the previous words and their context. It then selects the next word using probability distribution techniques.
Large language models
Large language models (LLMs) are one class of FMs. For example, OpenAI's generative pre-trained transformer (GPT) models are LLMs. LLMs are specifically focused on language-based tasks such as such as summarization, text generation, classification, open-ended conversation, and information extraction.
What makes LLMs special is their ability to perform multiple tasks. They can do this because they contain many parameters that make them capable of learning advanced concepts.
An LLM like GPT-3 can consider billions of parameters and has the ability to generate content from very little input. Through their pretraining exposure to internet-scale data in all its various forms and myriad patterns, LLMs learn to apply their knowledge in a wide range of contexts.