Latent Space as a New Medium for Creativity
I argue that the latent spaces within Large Language Models are becoming a revolutionary new medium for creativity. By compressing all human knowledge into a dense, high-dimensional map, these models allow us to navigate and synthesize ideas in ways previously impossible. This hidden space contains every concept and attribute, enabling artists and scientists to explore the connections between everything we know and imagine.
Everything — everything! — appears on just one map.
- ux266478
That's a mischaracterization. Latent space is simply a (multidimensionally) sorted collection, it's only a piece of the pie. A massive amount of structure is held in the unembedding layer. Generative AI models are a very specific ordering, LLMs a very specific subset of that, and they're hardly the only users of the concept.
I get what the author is going for, and they're on the right track. There is something interesting going on with embedding spaces: When used as the substrate for a neural network, you can effectively treat them as a kind of continuous form of computation. That is, given two functions, you can trivially derive a function which sits exactly between those two, and do so ad infinitum, for any arbitrary program (in theory. Obviously everything materially accessible is finite.) This is only one such manipulation. You can deform a function in an unenumerable amount of ways. Think like a bezier curve path tool in something like Krita or Photoshop, but for a function. You can keep adding points and twist it to your heart's content.
It's wrong to focus on LLMs specifically, as well. This is a much, much broader topic than you realize. Most of the interesting stuff has nothing to do with language models at all. I get a huge chunk of the industry is currently having a stroke over LLMs being able to brute-force problem solving, but if we're to talk philosophy, theory, and so on, we have to get past the surface level misuse of Machine Translation's holy grail. That's l […]
- chroma_zone
There's a lot of very strong claims made at the start of this article.
(emphasis mine)
> A Large Language Model (LLM) is like a small zip file that contains all human knowledge.
> In a strange but real way the resulting tiny file contains all the information that is on the internet and in our libraries.
> Likewise, the LLM could recognize the face of almost any person, and it could generate any possible human face,
All writings? All of human knowledge in general? Any person??
The example he gives for writing is Shakespeare, which might be one of the most overrepresented writers in the entire training dataset. So yes, of course LLMs can replicate his writings with high accuracy. That doesn't mean that the same applies to literally all of human writings and knowledge.
> We've never had a system to integrate everything we know and everything we can imagine.
Yeah, we still don't.
At first I thought he was just being intentionally hyperbolic for effect, but the rest of the article is even worse. From the closing paragraph:
> We’ll soon depend on this oracle to such an extent that we’ll wonder how we lived without it.
No! AI is not an oracle! That is honestly an extremely dangerous way to think about this technology.
This is the type of baseless hype that OpenAI and Anthropic have been exploiting for years, and I really wish it would stop.
- luisln
>In other words, correctness, truth, cohesiveness, completeness, comprehension, etc are all essentially patterns that are mapped in this space.
The caveat is that truth in latent space is just a reflection of the consensus from the corpus, and you find truth by comparing the answer in latent space to what is in reality.
But I just hate this idea that truth and facts are no longer real, they're just "directions". The more we rely on these models for our lives, the more we lose touch with reality and are pushed and pulled in all these different directions. Feels like the future is just ai psychosis and there's no way out. Is that what complete agi victory looks like?
- invictati
Dear Kevin: AI models don't contain all of human knowledge. They don't even contain, for example, the complete curricula of the least comprehensive K-12 program.
- mif
I like how Kevin Kelly brings together the ideas about what could be next. That’s always been his thing, hasn’t it? He is probably on to something here and it’s ok to be vague, I think. I don’t mind at all whether it’s all 100% technically correct.
Here are the bits I enjoyed:
LLMs (or GenAI, in general) as (the most?) efficient compression machines.
Creative or new ideas are the gaps or white space of the latent space. They already exist but become harder and harder for humans to find. It’s the perfect job for computers, therefore.
The concept of “true” is defined in the latent space (or compression) itself.