What Is Generative AI?

Generative AI, or gen AI, is a type of artificial intelligence (AI) that can create new content and ideas, like images and videos, and also reuse what it knows to solve new problems.

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.

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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.

Read more about generative AI in Financial Services on AWS

Finance Pie Chart

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

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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).

Read more about Generative AI for Automotive on AWS

Read more about Generative AI in Manufacturing on AWS

Automotive and manufacturing

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. 

Read more about Generative AI for Telecom on AWS

Telecommunication

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.
Media and entertainment

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.
Generative AI algorithms can explore and analyze complex data in new ways, allowing researchers to discover new trends and patterns that may not be otherwise apparent. These algorithms can summarize content, outline multiple solution paths, brainstorm ideas, and create detailed documentation from research notes. This is why generative AI drastically enhances research and innovation. For example, generative AI systems are being used in the pharma industry to generate and optimize protein sequences and significantly accelerate drug discovery.
Generative AI can respond naturally to human conversation and serve as a tool for customer service and personalization of customer workflows. For example, you can use AI-powered chatbots, voice bots, and virtual assistants that respond more accurately to customers for first-contact resolution. They can increase customer engagement by presenting curated offers and communication in a personalized way.

With generative AI, your business can optimize business processes utilizing machine learning (ML) and AI applications across all lines of business. You can apply the technology across all lines of business, including engineering, marketing, customer service, finance, and sales.

For example, here's what generative AI can do for optimization:

  • Extract and summarize data from any source for knowledge search functions.
  • Evaluate and optimize different scenarios for cost reduction in areas like marketing, advertising, finance, and logistics.
  • Generate synthetic data to create labeled data for supervised learning and other ML processes.

Generative AI models can augment employee workflows and act as efficient assistants for everyone in your organization. They can do everything from searching to creation in a human-like way. Generative AI can boost productivity for different kinds of workers:

     
  • Support creative tasks by generating multiple prototypes based on certain inputs and constraints. It can also optimize existing designs based on human feedback and specified constraints.
  • Generate new software code suggestions for application development tasks.
  • Support management by generating reports, summaries, and projections.
  • Generate new sales scripts, email content, and blogs for marketing teams.

You can save time, reduce costs, and enhance efficiency across your organization.

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.

generative AI model

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.

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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.

generative AI future

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.

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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.