What is Generative AI.?

Generative artificial intelligence (AI) describes algorithms (such as ChatGPT) that can be used to create new content, including audio, code, images, text, simulations, and videos. Recent breakthroughs in the field have the potential to drastically change the way we approach content creation.

?Generative AI systems?fall under the broad category of machine learning.

?Before we dive into generative AI, we must talk about AI more broadly. It’s one of those intriguing but often kind of nebulous terms. What exactly is AI?

AI is a broad term often used to describe all sorts of advanced computer systems. I prefer to talk more specifically about “machine learning.” Most of what we see in AI today is really machine learning: endowing computer systems with the ability to learn from examples.

?Difference between AI and Machine Learning

Artificial intelligence is pretty much just what it sounds like—the practice of getting machines to mimic human intelligence to perform tasks. You’ve probably interacted with AI even if you don’t realize it—voice assistants like Siri and Alexa are founded on AI technology, as are customer service chatbots that pop up to help you navigate websites.

Machine learning is a type of artificial intelligence. Through machine learning, practitioners develop artificial intelligence through models that can “learn” from data patterns without human direction. The unmanageably huge volume and complexity of data that is now being generated has?increased the potential?of machine learning, as well as the need for it.

We call machines programmed to learn from examples “neural networks.” One main way they learn is by being given lots of examples to learn from, like being told what’s in an image — we call this classification. If we want to teach a network how to recognize an elephant, that would involve a human introducing the network to lots of examples of what an elephant looks like and tagging those photos accordingly. That’s how the model learns to distinguish between an elephant and other details in an image.

Language models are another type of neural network.

?How do language models work?

Language models basically predict what word comes next in a sequence of words. We train these models on large volumes of text, so they better understand what word is likely to come next. One way — but not the only way — to improve a language model is by giving it more “reading” — or training it on more data — kind of like how we learn from the materials we study. If you started to type the phrase, “Mary kicked a…,” a language model trained on enough data could predict, “Mary kicked a?ball.” Without enough training, it may only come up with a “round object” or only its colour “yellow.” The more data involved in training the language model, the more nuanced it becomes, and the better chance it has the insight to know exactly what Mary is most likely to have kicked.

In the last several years, there have been major breakthroughs in how we achieve better performance in language models, from scaling their size to reducing the amount of data required for certain tasks.

Language models are already out there helping people — you see them show up with Smart Compose and Smart Reply in Gmail, for instance. And language models power Bard as well.

?Got it. So, we’ve defined AI and language models. What about generative AI?

A generative model can take what it has learned from the examples it’s been shown and create something entirely new based on that information. Hence the word “generative!” Large language models (LLMs) are one type of generative AI since they generate novel combinations of text in the form of natural-sounding language. And we can even build language models to generate other types of outputs, such as?new images, audio and even video, like with?Imagen,?AudioLM?and?Phenaki.

?What does generative AI mean for creative fields and creativity in general?

I think there’s huge potential for the creative field — think of it as removing some of the repetitive drudgery of mundane tasks like generating drafts, and not encroaching on their innate creativity. For instance, I think of generative AI the same way one might think of the arrival of the drum machine decades ago. The drum machine generated a rhythm that was different from what human drummers sounded like, and that fuelled entirely new genres of music.