What exactly is AI?

 

Artificial intelligence (AI) is a general term frequently used to refer to various types of advanced computer systems. However, I prefer to focus more precisely on "machine learning." The majority of what we encounter in AI today is actually machine learning—giving computer systems the capability to learn from examples.

Machines designed to learn from examples are referred to as "neural networks." A key method they use to learn involves being exposed to numerous examples, such as identifying the content of an image—a process known as classification. For instance, teaching a network to recognize an elephant requires a person to provide many examples of elephant images, and label them appropriately. Through this process, the model learns to differentiate an elephant from other elements 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 color “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 videos.