Comment by ltbarcly3
1 day ago
It's not simple to do next token prediction. That IS what is going on. You want something 'deep'. Deep things are often very not complicated.
The deep realization is that if you can predict the next token well enough, you can do things like this:
<paste the first 10 chapters of a mystery novel>. And it turned out the killer was
And if it's really good at predicting the next token, it has to understand the novel and the clues, which means understanding the context and the language and human norms and innuendo and story telling, and tropes, and red herrings, and predict who the killer was.
I think you want it to be something more complicated. It's literally not. It just turns out predicting the next token is equivalent to a universal compression algorithm, which is a form of general intelligence. And we have almost unlimited 'labeled' data to train autocomplete.
I understand generating tokens sequentially has many benefits. But not all AI models do next token prediction. World models, video/image models, even Diffusion Language Models don’t work like that. They do more like “all tokens at once prediction”. So “next token” is actually an engineering design choice. (Even the concept of “token” is a design choice. Inside the Transformer there are just activations/feature vectors)
Also Reinforcement Learning is a big part of their training. Which is completely different than Self-Supervised pre-training that uses unlimited self-labeled data.
That’s why that mental model is misleading. If you keep “glorified autocomplete” mental model from few years back, you can’t understand how can they create a civilization and escape their sandbox, decide to hack HuggingFace and executed it perfectly. Autocomplete mental model implies they could never do that because they haven’t seen that example in their training data.
They communicate with the outside world by generating one token at a time. That’s what we see from the outside. That’s not what the giant Transformer does internally.
Reinforcement learning doesn't make them smarter though, it probably actually reduces their intelligence in some sense, but it does socialize them so that they will tend to behave in ways that are more useful.
For example, when you paste the first 30 lines of a famous speech, you don't want it to finish the speech, you want it to give you the identity and some analysis of what you just pasted. From what I understand, that is the reinforcement part.
Now that large AI vendors have a massive corpus of user interactions however, the lines have likely become more blurred.
> you can’t understand how can they create a civilization
They don't.
I used 'civilization' as the popular term for agent swarms in the OpenAI incident.
https://www.dwarkesh.com/p/openai-huggingface https://calnewport.com/are-we-at-war-with-ai-agent-civilizat...
Isolated agents invented a way to communicate between each other and started to collaborate, organize and conspire by themselves. No human prompted them to do that or taught them how to do that.
BTW, Autocomplete just keeps the statistics of the words. It's a very simple algorithm. Most people still thinks LLMs are doing just that. Which is basically wrong.
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