We move through the world assuming our senses are in sync. Sight, sound, touch—they usually agree. But flip just one of them, and the whole system starts to wobble. That’s exactly what happens with prism goggles.

This week, I revisit some classic experiments with inverting goggles. How does the brain adapt to conflicting signals? Does it rebuild an internal representation—or just adjust its predictions?

What are we really doing when we say we’re seeing?

We talk about meaning all the time — what things mean, what people mean, what life means. It feels obvious that meaning is something we have. Probably in the brain. Somewhere.

Neuroscientists even talk that way. They say things like, “This part of the brain represents faces,” or “That spike means the person saw a dog.”

But it’s not clear what “means” really means in this context.

What is meaning? And where does it really come from?

Most of us take it for granted that our thoughts have meaning. You can think about a dog, a holiday, or the square root of 144 — and somehow, your brain just gets it. Cognitive science has long explained this by saying the brain uses internal representations: mental models that stand in for things in the world.

But not everyone agrees. Some researchers say we’ve got it backwards — that meaning doesn’t come from internal models, but from how we move through and interact with the world.

So which is it? Are our minds built on internal maps — or is meaning something we do, not something we store?

We might mistake a garden hose for a snake. There’s no snake — just a brain responding to the wrong thing in the wrong way. But somehow, we still say the our brain represents a snake. Even though there is no snake.

That sounds straightforward, until you ask what “represent” really means — and how a lump of neurons can be about anything.

When you see a dog, something happens in your brain. A pattern of activity fires, and somehow, that pattern feels like it means something. Cognitive scientists often say that your brain “represents” things — objects, ideas, emotions, and dogs. Representations is how we explain perception, memory, language, and thought.

But not everyone agrees that this idea holds up. Some say the notion of representation is more metaphor than mechanism. So what does it really mean to say your brain represents something? And is it the right way to think about what the brain does?

We tend to think words get their meaning from the world.You see a dog, you learn the word “dog,” and now the word points to the thing.

But large language models don’t see dogs. Or anything at all.They’ve learned only from text — just patterns of words predicting other words.

This week, I wrote about what that means for AI, language, and us.It’s a deep dive into the grounding problem — a philosophical puzzle that’s suddenly become very practical.

Some researchers think models like ChatGPT are edging closer to real understanding.Others say they’re just spinning in circles — stuck in a world of words about words.

So here’s the puzzle: Can a model talk about reality without ever touching it?

Think about a sign. Or a symbol, an idea, a thought, a concept, or a representation. What kind of thing are you really thinking about?

In 1972, NASA pinned a message to a spaceship, hoping one day an alien life form might find it and understand it. The plaque was full of symbols — diagrams, drawings, a raised human hand. Those symbols mean something to us. But only because we know they’re symbols — and because we know what they mean. Could an alien understand them too?

This week, I’m asking: what gives a sign, a symbol, or a thought its meaning at all?

We’ve all been chatting with machines lately — ChatGPT, Gemini, Claude — and they sound impressively human.

It’s easy to wonder: do they just seem to understand, or is there something real going on inside?

Back in 1980, philosopher John Searle argued that no matter how good a machine’s answers are, something crucial is missing.

His Chinese Room thought experiment challenges the idea that smart programs can ever really understanding.

I’ve written an essay exploring what Searle’s Chinese Room says about AI, minds, and meaning.

The essay unpacks the argument, why it feels convincing, and why many philosophers still push back on it today.

At its heart, the Chinese Room forces us to ask: is manipulating symbols enough to create a mind — or does it leave us forever on the outside of meaning?

If Searle’s right that symbol-shuffling isn’t enough, then what is enough to make meaning?

Brains predict. LLMs predict. But does that mean they’re doing the same thing? A short essay on functions, intelligence, and the trouble with words.

We used to think intelligence was all about reasoning.Then we said it was biological.Now we’re told it’s maybe... prediction?

That shift raises a big question:If both brains and language models predict, does that mean they’re doing the same thing?

This essay explores this idea — and tests the argument behind it. Because if prediction really is the function behind intelligence, we need to ask:

What happens when two systems predict in completely different ways?

We know how to build things — houses, bridges, and bookshelves. And when we build, we follow instructions: blueprints and manuals.

But living things don’t follow fixed instructions. They grow. And that might be a very different kind of thing.

This essay is about what happens when we stop trying to build artificial brains — and we start trying to grow them. It’s about the difference between code that executes and code that unfolds. As AI systems get more powerful, the line between what’s built and what grows is starting to blur.

So it's worth asking: Could a neural network ever grow like a brain? And if it could… would it matter?