Exploring how AI like ChatGPT ‘understands’ words much like we do — and what that means

If you’ve ever wondered whether AI truly “gets” what it’s saying, you’re not alone. The question of AI understanding language is a hot topic, and recently, Geoffrey Hinton, one of the pioneers of neural networks, shared some fascinating insights that really made me think.

So, what does it mean when we say AI understands language? It turns out, this isn’t as clear-cut as you might think. Hinton points out that large language models (LLMs) like ChatGPT don’t just spit out words they’ve memorized. Instead, they process and generate language in a way that’s surprisingly similar to how humans do.

Two Ways We’ve Tried to Teach AI Language

For over seven decades, there’ve been two main approaches to AI: symbolic AI, which relies on rules and logic, and neural networks, which learn from data. After 2012, neural networks started to dominate because they can learn from vast amounts of information and adapt.

Words as High-Dimensional Lego Blocks

Hinton’s analogy is pretty cool. Imagine each word as a “thousand-dimensional Lego block” — a flexible shape that can change depending on the context. These blocks “shake hands” with each other using something called attention mechanisms, helping them fit together just right. That’s where understanding happens: figuring out the best configuration of all these word blocks.

AI Isn’t Just Guessing What Comes Next

You might think LLMs are just autocomplete on steroids, but they actually learn complex feature vectors stored in their neural weights. These weights carry knowledge adapted to all sorts of contexts. This is similar to how our own brains store and use information.

Why Do AIs Sometimes ‘Hallucinate’? We Do Too

AIs sometimes produce confident but wrong answers — called hallucinations. That’s actually a lot like how humans remember things. Our memories aren’t perfect recordings; they’re constructed and can mix up details. The difference? We’re usually better at telling when we’re guessing — for now.

The Mind-Blowing Part: Sharing Knowledge Instantly

Here’s a little sci-fi feeling. Digital brains can share knowledge by copying weights — that’s trillions of bits at once! Compare that to sharing a sentence with about 100 bits of information. This is why models like GPT-4 can “know” way more than any one person.

So, What Now?

Understanding AI understanding language helps us see these tools as more than magic word machines. They reflect some of our own cognitive patterns, which makes them all the more interesting — and sometimes a bit scary.

Want to dig deeper? Check out the International Association for Safe and Ethical AI for the original talk and some thought-provoking resources. Also, if you’re curious about how neural networks actually work, DeepLearning.AI has great beginner-friendly courses. And for a broad view on AI language models, OpenAI’s overview is always a solid read.

Understanding AI understanding language helps put both its amazing potential and limitations into perspective. Next time you chat with an AI, you’ll know a little more about what’s really going on behind the scenes.