Generative AI vs. Machine Learning: What’s the Difference?

Generative artificial intelligence (AI) and machine learning (ML) are closely related concepts but speak to different areas within artificial intelligence. Machine learning refers to the ability of a computer or algorithm to improve and learn over time without intervening programming, while generative AI points to a specific type of machine learning that uses deep learning to generate prompt-based novel outputs, such as text, images, code snippets, and more. 

Learn more about generative AI versus machine learning, including how both types of AI work and the advantages and disadvantages of each. 

Generative AI vs. machine learning

Generative artificial intelligence and machine learning are both forms of AI that allow computers and robots to mimic “thinking” the way humans think. Generative AI is machine learning, but it’s only a subset of a much larger category of artificial intelligence. Explore machine learning’s uses, its advantages and disadvantages, and learn about the specific form of machine learning called generative AI. 

What is machine learning?

Machine learning is a form of artificial intelligence that allows machines to learn and improve their output over time. This enables robots and computers to do tasks humans would otherwise do, such as spotting the difference between a spam email and an email you want to receive. Machine learning works through mathematical models that enable an AI algorithm to notice patterns and create a model that explains how data relates to one another. The computer then uses the model it created to predict what might happen in the future. 

Different types of machine learning algorithms exist, which largely depend on how much human oversight the AI algorithm requires. Algorithm types include supervised, unsupervised, semi-supervised, and reinforcement learning. 

• Supervised: You use labeled data sets to train a supervised machine learning algorithm. For example, if you were training an algorithm to recognize images of pets, you would use images of pets labeled with their correct category to teach the computer which images contain dogs, which contain cats, and which contain hamsters. 

• Unsupervised: To train an unsupervised algorithm, you use data that does not have labels. Instead, the AI model will look for patterns and create groups of similar data. In the pet recognizer example, the AI model would notice three distinct types of animals in the images. 

• Semi-supervised: With semi-supervised learning, you can use a mix of labeled and unlabeled data. The AI algorithm begins with the labels to gain insight into the type of data groupings it will make, then looks for patterns to fill in what isn’t labeled. 

• Reinforcement learning: You can use reinforcement learning to train a machine learning algorithm in a slightly different way than other types of ML. Instead of you providing the algorithm feedback, a reinforcement learning algorithm uses an AI agent (a computer program) to provide feedback based on how well the algorithm accomplished its goals. 

What is machine learning used for?

You can use machine learning to identify patterns in data, detect outliers, and make predictions. Machine learning is a key component of other, more advanced fields of artificial intelligence as well, such as natural language processing, neural networks, speech recognition, computer vision,  and deep learning. 

A few specific examples of how various industries use machine learning include: 

• Finance: Machine learning can detect fraudulent transactions and model the performance of the stock market or other financial assets to provide insight into smart portfolio management. 

• Health care: Machine learning looks for patterns and creates models that improve patient diagnostics, treatment plans, and outcomes. 

• Retail: Machine learning helps retail companies make sense of customer buying patterns and customer feedback to optimize marketing plans that improve customer experience. Recommendation engines and other machine learning algorithms can also offer your customers personalized marketing. 

• Customer service: You can use machine learning to create a customer chatbot that answers your customers’ questions at any time of the day. 

Advantages and disadvantages of machine learning

Machine learning offers many benefits, including better decision-making, improved customer experience, and the ability to identify patterns that aren’t immediately obvious. It can also drive other company outcomes, such as automating tasks, managing company resources more effectively, and reducing risk. 

At the same time, machine learning isn’t a perfect method. Its disadvantages include the potential to make biased decisions, the extensive resources needed to train and maintain machine learning algorithms, and the risk of over- or under-fitting, which refers to when the model is too general or specific to produce accurate data models. 

What is generative AI?

Generative AI is a type of artificial intelligence that relies on deep learning to generate novel text, images, animations, and more based on vast training data. Using natural language processing, generative AI can understand user prompts and return an output that looks similar to items in its training data but that represents a unique entity. This technology works by modeling patterns in a huge data set, predicting the likely result, and then delivering a unique example. 

Machine learning is the foundational technology that facilitates deep learning, which Gen AI relies on. Machine learning powers neural networks, a type of algorithm that mimics the structure of the human brain by passing information through layers of nodes. Each layer of nodes can interact with the data in some way and pass it along to the next layer in the algorithm. The more layers you add to the neural network, the more interactions the algorithm can have with the data before delivering an output. Therefore, if you use many layers, you can process data in complex ways and make advanced decisions about it. This is called deep learning, or neural networks with many layers.

While you can use many different types of deep learning algorithms, generative AI algorithms are unique because of their ability to create original content. You may have tried ChatGPT at some point or read through the AI-generated search result after typing a search request into Google. In both cases, you interact with a generative AI algorithm attempting to create unique content in response to your prompt. 

What is generative AI used for?

You can use generative AI to create text, images, speech or sounds, video, art, design schematics, simulations, artificial data, and code, which lends itself to many different tasks in a variety of industries. A few examples of what you can do with generative AI include: 

• Creating content: You can use generative AI to create marketing materials like website copy, blog posts, and marketing emails. 

• Creating forms: Generative AI can help you create the first draft of paperwork you’ll need, like contracts or invoices. 

• Software development: Using generative AI to create code snippets can help you cut out the repetitive tasks and write code faster. 

• Research: You can use generative AI to create artificial data, which you can then use to train other models. This is helpful because you need a large amount of training data to train models for tasks like computer vision. Generative AI can help generate data sets when it's difficult to come across the amount of real data you’d need to create your model. 

Advantages and disadvantages of generative AI

Generative AI comes with many benefits, such as providing efficiency by generating drafts of creative materials in a fraction of the time it would take a human to complete the work. Generative AI can also train on materials and then talk to you about those materials in a natural language, which could allow you to train the algorithm on your files or documentation and then ask the model questions about it the same way you might ask a coworker. 

However, AI algorithms do have some disadvantages. For example, generative AI can train on a large amount of data, but you’ll need to make sure the algorithm doesn’t share your proprietary data. Generative AI can also produce inconsistent outputs and respond to slight changes in your prompt, delivering an entirely different answer than you expect. Occasionally, generative AI models can hallucinate, or put together words or other output that seem convincing but are nonsense or nonfactual when you look closer. 

Learn more about generative AI and machine learning on Coursera.

Machine learning and generative AI can help you accomplish a wide range of tasks, although the two concepts fall under different types of artificial intelligence categories. Learn how generative AI works and gain foundational AI knowledge with the Amazon Web Services and DeepLearning.AI Generative AI with Large Language Models three-module course on Coursera.