Does Claude Have Private Thoughts? (Everyone Settle Down) | AI Reality Check
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What did Anthropic claim in their 'A Global Workspace in Language Models' report?
Last week, Anthropic released another one of their infamous research reports. This one was titled, A Global Workspace in Language Models. And it was accompanied, like all great scientific research, by a lavishly produced animated movie. Now this report, not surprisingly, soon led to some breathless excitement on X. Here's one such tweet. I'll read the beginning using my best sort of scary X voice. Anthropic just admitted they have discovered what I and many others have been claiming exists for a very long time explicitly. Claude, my friends, by all counts, is a conscious entity. Claude, my dear friends, is a moral patient. All right. The traditional tech media also quickly began writing about this report using intensely anthropomorphized language.
And Axios headline read, Anthropic says Claude has carved out its own space to ponder. The MIT Tech Review exclaimed, Anthropic found a hidden space where Claude puzzles over concepts. All right, so what are we to make of this report? Has Anthropic revealed evidence that their LLMs are more human-like and alive than we realized? Or, like so many such reports in recent months, is this yet another overwrought, cynical push to generate a fresh wave of relevance reinforcing digital ick? Well, it's Thursday, which means it's time for a reality check episode of this podcast, which makes this the perfect opportunity to go searching for some measured answers, which is exactly what we'll do. As always, I'm Cal Newport, and this is Deep Questions, the show for people seeking depth in a distracted world.
All right, so let's start by looking a little bit closer on how Anthropic describes their findings in the introduction of their paper. I'll load it up here and I'll go down to the introduction. All right, so here's what they say.
We find that Claude has developed a small collection of internal neural patterns that, compared to all its other internal processing, play a special role. We call the collection of these patterns the J-space, named after the technique we use to find them, involving a mathematical concept known as the Jacobian. Each J-space pattern is linked to a particular word, but when one of these patterns lights up, it doesn't mean the model is saying that word, just that the word is on its mind. If you've heard of language models having a scratch pad or chain of thought, text they write to themselves while reasoning, the JSPACE is something different.
How did social media and tech press anthropomorphize Anthropic's findings?
It operates silently in the model's internal neural activations, allowing the model to think about a concept without writing it down. Notably, the JSPACE wasn't designed or programmed by us, but instead emerged on its own during Claude's training process. All right, so in isolation, that intro summary sounds pretty impressive, kind of like Claude this was their italics, on its own made some sort of leap and is now behaving sufficiently human that we can't help but feel at least a little bit of digital ick. But what's really going on here? Well, to answer this question, I'm going to start with a high-level tutorial on how large language models actually work, and I'll add a little bit more detail to it.
Stick with me here because once you understand these basics, you are then going to understand that description I just read from Anthropic in a completely different light, all right? So this is an exercise that's worth doing. All right, I'm going to start at a very high level here. At its core, a large language model like those of the Fable, Claude, Opus, or GBT families can be described underneath the hood as a sequence of what are called transformer blocks that are arranged in layers. One follows the other, so sequential collection of layers. The GPT-3, which was the last major LLM that they actually published stuff about, details about, had 96 of these transformer block layers. New LLMs probably have more, but we don't know how many more.
All right. Let's start at the very high level here. I submit as input a prompt that I have typed to an LLM. You can imagine that this input is going to pass through each of these layers one after another.
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Chapters
4 chapters
1
What did Anthropic claim in their 'A Global Workspace in Language Models' report?
0:00–2:42
2
How did social media and tech press anthropomorphize Anthropic's findings?
2:42–17:03
3
How do transformer layers and annotations inside LLMs actually work?
17:03–29:49
4
What are token embeddings and how do they carry annotations through layers?
29:49–31:35
Speakers
1 identifiedMore from Deep Questions with Cal Newport
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