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

Some people say that we are in the age of AI. In fact, we seem to be smack in the middle of a bubble – AI is all anyone in tech can talk about! But what do people mean when they talk about AI? How can you be sure you’re building something that will solve real problems? One way is by understanding terms. Let’s start with the differences between Machine Learning (ML) and Generative AI (Gen AI).

Search Terms Indicate a Hype Bubble

In many cases, marketing for AI is really marketing for Gen AI. Take a look at Google trends for the term “generative” AI for the past five years:

But when you look at Google trends for the term “machine learning,” you notice that it’s been a popular search for the same period:

It’s apparent from Google search trends that interest is more consistent with ML than Gen AI. Why is that?

How is generative AI different than Machine Learning

Generative AI creates new content from existing data. Different models generate text, images, music, and more, based on the way they have been trained with advanced algorithms. It takes vast amounts of data to power the algorithms.

Machine learning is an entire subset of AI. According to IBM , ML is “a branch of AI focused on enabling computers and machines to imitate the way that humans learn, to perform tasks autonomously, and to improve their performance and accuracy through experience and exposure to more data.” ML is what fuels descriptive, predictive, and prescriptive AI applications.

Machine learning is an entire subset of AI. Algorithms are applied to large amounts of data so that the ML program can “perform tasks autonomously, and ... improve their performance and accuracy through experience and exposure to more data (via IBM).  ML is what fuels descriptive, predictive, and prescriptive AI applications.

Machine Learning Categories Explained

There are three ways to perform ML. With supervised learning, the models are trained with labeled data sets. My colleague, Tony Foster, walked through training a model to recognize cats in our VMworld session last year.

With unsupervised learning, the models use unlabeled data sets. This is what helps you find data trends you may not know to look for.

Reinforcement learning trains models “through trial and error to take the best action by establishing a reward system”. This could be as simple as letting the model know when it made the right decision.

Most AI in the Enterprise is Powered by Machine Learning

Here’s a list of applications that depend on machine learning that you probably already use (via TechTarget):

• Chatbots use ML and natural language processing (NLP) to mimic conversation.
• Digital assistants, such as Siri and Alexa use ML to understand and respond to voice commands.
• Recommendation engines process your past purchases + current inventory + what  other customers are buying to recommend what you should also buy.
• Dynamic pricing helps companies adjust the prices they charge based on market conditions. Controversially, it also seems like they have been marrying recommendation engines and dynamic pricing to charge different prices for individual consumers.
• Speech recognition can record calls, monitor customer calls with human agents, and provide language translation.
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Generative AI Doesn’t Work Without Machine Learning

Gen AI relies on ML techniques to work. It often uses NLP and computer vision in its creation process. Other ML disciplines used by Gen AI include (via Blue Prism):

• Multimodal AI to interpret text, images, and videos.
• Large Language Models (LLMs) were designed to generate and understand human language text.
• Generative adversarial networks (GANs) are unsupervised learning techniques that pit two neural networks against each other during training.
• Transformers use math to identify the context and relationships between data.
• Diffusion generates new data.
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Know AI Basics to Navigate the Hype

Generative AI is built to create new content. Machine Learning analyzes lots and lots of data to make sense of it, and there are many models to accomplish this. Knowing the difference between the two will help you zero in on what type of AI can help you solve business problems.

Here’s the question to ask yourself before a vendor calls you to pitch an “AI” solution: what is your business use case for AI? Will it require Gen AI? Or will an ML algorithm be enough?