by David J. Lobina

In previous posts on AI [sic], I have argued that contemporary machine learning models, the dominant approach in AI these days, are not sentient or sapient (there is no intelligence on display, only input-output correlations), do not exhibit any of the main features of human cognition (in particular, no systematicity), and in the instantiation of so-called large language models (LLMs), there is no natural language in sight (that is, the models make no use of actual linguistic properties: no phonology or morphology, no syntax or semantics, never you mind about pragmatics).
The claim about LLMs is of course the most counterintuitive, at least at first, given that a chatbot such as ChatGPT seemingly produces language and appears to react to users’ questions as if it were a linguistic agent. I won’t rerun the arguments as to why this claim shouldn’t be surprising at all; instead, I want to reinforce the very arguments to this effect – namely, that LLMs assign mathematical properties to text data, and thus all they do is track the statistical distribution of these data – by considering so-called adversarial attacks on LLMs, which clearly show that no meaning is part of LLMs, and moreover that these models are open to attacks that are not linguistic in nature. It’s numbers all the way down!
An adversarial attack is a technique that attempts to “fool” neural networks by using a defective input. In particular, an adversarial attack is an imperceptible perturbation to the original sample or input data of a machine learning model with the intention to disrupt its operations. Originally devised with machine vision models in mind, in these cases the technique involves adding a small amount of noise to an image, as in the graphic below for an image of a panda, with the effect that the model misclassifies the image as that of a gibbon, and with an extremely high degree of confidence. The perturbation in this case is so small as to have no effect to the visual system of humans – another reason to believe that none of these machine learning models constitute theories of cognition, by the way – but the perturbation is the kind of mathematical datum that a mathematical model such a machine learning model would indeed be sensitive to (all graphics below come from here). Read more »