Generative AI

What is Generative AI? A Guide to How it Works and How it Could Shape Your Career

Generative AI technology is changing how people approach tasks such as writing software, researching complex topics, analyzing documents and creating images and video. It has uses across a variety of industries. For example, developers can use AI assistants to troubleshoot code, while healthcare researchers can apply generative models to identify promising candidates for new drugs. 

The term “generative AI” covers a range of technologies that produce new content based on patterns learned from large amounts of data. Behind familiar tools such as AI chatbots and image generators are sophisticated models that require expertise to build, train, evaluate and improve. 

So, what is generative AI? This article explores how this technology works, its applications across industries and what its emergence could mean for people considering careers in AI.

What Is Generative AI?

Generative AI is a type of artificial intelligence that learns patterns from existing data and uses what it has learned to create new content. Depending on the model, that content can include text, images, audio, video, software code and other forms of data. 

The ability to produce new material is what makes these models generative. For example, a model trained on large collections of text can learn relationships among words, concepts and patterns of language, then use those relationships to generate a response to a question or summarize a document. 

Other types of AI and machine learning (ML) are designed for tasks such as classifying information, detecting patterns or predicting outcomes. A fraud-detection model might estimate whether a transaction is suspicious, while an image-recognition system might identify objects in a photograph. 

Understanding how generative AI produces these outputs requires a closer look at how the underlying models learn and process information.

How Does Generative AI Work?

Generative AI models learn by analyzing large datasets during a process called training. The model identifies statistical patterns and relationships within its training data, and then draws on those patterns to calculate and generate an appropriate output when it receives new input. The exact mechanics vary by model and task.

Here are some of the key concepts related to how generative AI works: 

• Training: The process through which a model analyzes data, makes predictions, evaluates errors and adjusts its internal parameters to improve future results. 
• Prompts: The instructions or other inputs a user provides to a generative AI model. The model processes the prompt and uses it as context for determining what to generate. 
• Tokens: The smaller units of data that language models process. A token can represent a word, part of a word or another element of text. 
• Large language models (LLMs): Models trained on enormous amounts of text and other data to understand and generate language. When producing text, an LLM typically predicts a sequence one token at a time based on the patterns it learned during training. 
• Neural networks: Computing models made up of interconnected layers that learn to recognize patterns and relationships in data. They form the basic architecture used to build many generative AI models. 
• Transformers: The neural network architecture underlying most modern LLMs. Transformers use a mechanism called attention to identify relationships among different parts of a sequence, helping the model account for context when processing and generating language. 
• Diffusion models: Models commonly used to generate images. During training, they learn to reconstruct images that have been gradually distorted with random visual noise. To create a new image, the model begins with a field of random noise and removes it step by step, guided by a prompt, until a recognizable image emerges.

How Is Generative AI Being Used in Different Industries?

Generative AI can work with many forms of information, giving it practical uses across fields that depend on text, images, code, scientific data and other complex inputs. The applications vary considerably by industry, from helping developers test software to assisting researchers with drug discovery. 

Software Development

Developers can use generative AI to write and debug code, run unit tests and identify potential errors or security issues. These tools can also help teams document existing code and work through possible approaches to a programming problem, with developers reviewing the resulting output for accuracy and quality. 

Healthcare

In healthcare, generative AI is being studied and applied to tasks such as drafting clinical notes, summarizing patient records and assisting with medical research. Scientists are also exploring large language models for drug discovery and development, including identifying potential drug targets and analyzing clinical trial information. 

Finance

Financial institutions can use generative AI to support fraud detection, risk analysis and compliance by analyzing financial data and identifying patterns or anomalies that warrant further review. The technology can also summarize financial and regulatory documents, draft reports and help customer service teams respond to inquiries. 

Media and Creative Work 

Generative AI can support creative production at several stages, from developing early visual concepts to editing finished work. Photographers and video creators, for example, use generative tools to remove unwanted elements, combine images, create visual effects and assist with storyboarding and other preproduction tasks.

How Is Generative AI Changing Careers? 

As organizations build generative AI into products and workflows, they are looking to hire people who understand the technology at a deeper level. That demand is giving rise to specialized generative AI careers while expanding the responsibilities of software engineers, data scientists, machine learning professionals and other technical roles.

Generative AI Careers 

Growing adoption of generative AI is expanding opportunities in several career paths focused on building, adapting and improving these systems. Examples include: 

• Generative AI engineer: These professionals build software applications that use generative models. They may fine-tune existing models for a particular task, connect models with other software and data sources, and improve the quality of their outputs. 
• Machine learning engineer: ML engineers build and deploy models that learn from data. In generative AI projects, they may prepare training pipelines, refine models, and help move them into production. 
• NLP engineer: These specialists develop systems that process and generate human language. Their work can support conversational AI, text generation, summarization, translation and other language-focused applications. 
• AI research scientist: Researchers investigate new AI methods and test ways to improve model capabilities and performance. Their work may contribute to advances in model architectures, training techniques and evaluation. 
• AI developer: AI developers turn models into usable applications and features. Their work can include writing code, integrating generative AI models into products and testing how AI features perform for users.

Prepare for a Career in Generative AI

Building a career in generative AI requires technical expertise and experience applying AI to real-world problems. Graduate-level education can help you develop these skills through advanced coursework, hands-on projects and opportunities to explore emerging areas of AI. 

The M.S. in Artificial Intelligence program at the Katz School of Science and Health prepares students to design, build and deploy AI applications. The 12-course program covers areas such as machine learning, natural language processing, generative AI, multimodal systems and AI-augmented software development, with project-based learning that gives students experience developing intelligent applications. 

Ready to build your AI expertise? Learn more about the M.S. in Artificial Intelligence and see how the program can support your career goals.