How generative AI is different than other types of AI and how it works ?
Generative AI is a type of AI that, as this name suggests, generates new content. This is in contrast to other types of AI, like discriminative AI, which focuses on classifying or identifying content that is based on preexisting data. Generative AI is often used in applications such as image generation, video synthesis, language generation, and music composition, but to really understand this new tool, we need to know first where it fits in the broader AI landscape.
The term AI, which is artificial intelligence, is an umbrella term that encompasses several different subcategories, including generative AI. These subcategories are used to perform different tasks. For example, reactive machines are used in self-driving cars. Limited memory AI forecasts the weather. Theory of mind powers virtual customer assistance. Narrow AI generates customized product suggestions for E-commerce sites. Supervised learning identifies objects from things like images and video. Unsupervised learning can detect fraudulent bank transactions, and reinforcement learning can teach a machine how to play a game. These are only a few of the subcategories, and generative AI models fall into a lot of these categories, and honestly, it’s only growing. These other types of AI may still generate content, but they do it as a side effect of their primary function. Generative AI is specifically designed to generate new content as its primary output. Whether this is text, images, product suggestions, whatever, that’s what generative AI is designed to do. So, now that we know where generative AI fits in the broader landscape, together, let’s explore how it works.
How generative AI works
To understand how generative AI works, we first have to understand how it comes to life. I know how you’re feeling. You’re looking at the news, and ChatGPT, and Midjourney, all this generative, what is generative, what is AI? It’s so complicated. You have no idea how to make sense of it all, but you know you need to, because this is where the world is evolving towards.
Okay, let’s start with AI 101. Imagine you and me were having dinner and you asked me to pass you the salt. I look at the table and I can make a discernment between a salt shaker and the rest of the objects on the table. Why? Because my mind has been trained with thousands, or millions, or trillions of salt shakers earlier. AI works the same. You feed it with thousands, millions, trillions of content, and then you teach a certain algorithm to generate outputs and solutions as a result.
Okay, now that we got AI 101 out of the way, let’s get into generative AI 101. Let’s use cars as an example. Just like a Porsche has a different engine than a Mazda, under the umbrella term of generative AI, there are a variety of different generative AI models. These AI models, or car engines, are written and manufactured by groups of highly advanced computer vision specialists, machine learning experts, and mathematicians. They’re built on years of open source machine learning research and generally funded by companies and universities. Some of the big players in writing these generative AI models, engines, are Open AI, NVIDIA, Google, Meta, and universities like UC Berkeley and LMU Munich. They can either keep these models private, or they can make them public, what we call this, open source, for those to benefit from their research.
All right, now that these complex generative models are written, meaning the engines are made, what are we going to do with them? Depending on your level of technical expertise, this can look a bit different. I’m going to paint a picture for you with three different end users of these models. The first person is a business leader who comes up with a product idea that involves a generative AI model, or several. For the development of their tool, this business leader either uses free open source generative AI models or enters into a partnership with a corporation to get rights to their generative AI model, then their team creates their vision. To continue the chronology, let’s say this person owns the car factory. They direct where the engine and chassis go, but don’t actually work on the floor. The second person is a creative person with an appetite for adventure. They might have some technical knowledge, but they aren’t an AI engineer. I mean, they can be if they want. This person goes to a car engine showroom, where they pick a pre-made car engine or a generative AI model from a repository like GitHub and Hugging Face. After that, they go to a chassis manufacturer to pick their empty shell for their new engine, their precious new engine. These chassis are called AI notebooks. Their purpose is to hold and run the generative AI model code. The most widely used one is Google Colab, but there are others like Jupiter Notebooks. And the third person would be my mother, bless her heart. She has absolutely no technical pedigree, nor she’s interested in acquiring one. But this doesn’t mean she cannot benefit from generative AI. My mother would be buying her already made car. She will have way less control over the outcome of her car, but she will be able to drive, just like the business leader and the creative technologist. People with no technical knowledge can simply subscribe to an online service like OpenAI’s ChatGPT or DALL-E, or download Discord and play with Midjourney, or download Lensa AI and Avatar Maker in their smartphone to play with the magic of generative AI. Well, this all depends what you want to do and what you want to build, and how much technical expertise you already have. Now that we have our car, our generative AI model, we can now start creating our own content and go for a drive.
