What is generative AI? A Google expert explains.
Before we dive into generative AI, we have to 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.
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 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.
I raised two kids and got a literature degree before I went into computer science, so I’m asking myself real questions about how educators measure success in a world where generative AI can write a pretty good eighth- or ninth-grade essay.
If you think back, when the graphing calculator emerged, how were teachers supposed to know whether their students did the math themselves? Education advanced by understanding what tools the students had at their disposal and requiring students to “show their work” in new ways.
This content was originally published here.
