We finally uncovered what AIs REALLY think.

Have you ever wondered why this newsletter is called TheWhiteBox? For the sake of my ego, I’m going to assume you ask yourself that question first thing in the morning, every day.

Jokes aside, the idea was that AI is a “black box” (we don’t know how they work, like, at all), and my newsletter would try to demystify it. Rather pretentious, but what marketing giveth, marketing taketh.

But let me tell you something: Did you know that a model might be thinking bad things about you while sounding completely normal?

Well, it’s true. Until now, we suspected it, but we can now see it thanks to Anthropic’s new research, Natural Language Autoencoders (NLAs), which begin to answer the question of what lies beneath ChatGPT’s overly formal and professional demeanor.

And the answer is a mixture of candidness, honesty, lies, anger, scheming, and many other behaviors and, for lack of a better term, “beliefs” and “sentiments” that are now emerging and that you should definitely be aware of.

The opportunity? An entirely new AI industry worth hundreds of billions in AI assurance.

Let’s dive in.

From Neurons to Thoughts

To fully comprehend the huge implications of today’s research, it’s important that we transition ourselves from seeing AIs for what they look like to what they actually are, so that we can understand the fundamentals of interpretability, the field that aims to uncover AI’s biggest mysteries.

The basic structures

On paper, AIs are just a bunch of elements called neurons that interact and combine to produce the output.

We call them ‘neurons’ because they are tightly connected to each other, much like human brain neurons, and they also have the “fire or mute” behavior, too.

It’s a “little bit” more complicated, but at the heart of every AI model you’ve interacted with lately, sits something like this:

And how does it work?

Say we have an animal classification model that takes in several images of a specific animal and outputs its name.

We feed the three inputs to the model, which processes them through the hidden layers to identify common patterns and determine the likely animal, for example generating a text response, “horse,” to three inputs depicting horses.

Source: Author

This is something ChatGPT would do, though the actual architecture under the hood is way more complicated than this. But at heart, it’s just a “bunch of neurons.”

In reality, what is going on under the hood looks something like the image below.

Source: Author

I know, this sounds awfully mathematical and complex, but don’t worry, I’m not going to bore you with maths. Instead, what we’re going to do is reveal what these activation circuits, like the one depicted in red, actually mean.

That is, the issue with these neurons is that, taken at face value, they are gibberish; they are a bunch of numbers magically understanding that there were horses in the images. Taken at face value, they reveal very little about their nature, making AIs look like black…

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