“AI” – chatbots that wake up, “set their own goals,” and “spontaneously” start hacking servers – is fake. It doesn’t have “a 10% chance of ending the human race.” The Hugging Face hack isn’t a mysterious, supernatural occurrence. It’s a Python loop and a chatbot. The people responsible didn’t accidentally create god: they created autonomous malicious software and then failed to closely monitor it, resulting in it doing something both foreseeable and bad.
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I miss the times when it was considered good when smart people said “I don’t know, how exciting!”.
And nobody knows because LLMs are far too complex. They have built software brain scanners to try to figure out how LLMs actually work. Just like meat brains they are too complex already to be understandable. Nobody can explain how they think or reason to the small extend that they can because we do not have any theory about intelligence or thinking.
But here we are, another fuckai post trending in all and lets approach this question from an emotional side and all argue very passionately! It feels much better to feel being sure about something. When it’s oh so obviously this or that. Must be nice.
We have fully open source implementations for every step of the process you could theoretically look at all the code of. How training data is processed, how inference is an intensive token prediction loop, how it doesn’t match up with the chemical processes of real brains at the slightest, all of it.
LLM-MRI Python module: a brain scanner for LLMs - paper
Abstract. LLMs (Large Language Models) have demonstrated human-level lan- guage and knowledge acquisition skills in several tasks. However, despite the recent success and broad use, understanding how these skills learned are and encoded inside the underlying neural network is still challenging. The goal of the LLM-MRI package is to simplify the study of activation patterns in any transformer-based LLM, similarly to how MRI (magnetic resonance imaging) simplifies with biological brains. The package, written for the Python lan- guage, allows the mapping of neural regions using a parameterized reduction of the model’s dimensionality. Neural regions can be viewed according to the forward-pass activations stimulated by a set of documents. , the pack- age enables the creation of graph models representing the interlayer network of stimulated by a set of documents. These features allow for which- itative and quantitative assessments of the underlying structure of activations, depending on the type of documents that the LLM model is exposed to.
