This talk describes theoretical constraints on current attempts to work decode mental content from brain data.

This is a talk about whether LLMs can be said to have beliefs.

This is a talk about brain-computer interfaces and their relationship to intentional mental states.

Rather than merely demonstrating the fragility of ML models, strange error might be evidence of hidden knowledge.

In order to tell whether someone is culpable for an action initiated by a brain-computer interface, it is not necessary to work out whether the brain-computer interface correctly decoded their intention.

Where ML models are used as the centerpiece of an epistemic classification procedure, reliability is not sufficient for ethical use. The nature of classification errors should be taken into account.

Reading minds is easier than you might think.

There can be an objective fact about the number of bits in a biological signal, despite the fact that the signal is receiver-relative.

Argument maps represent some kinds of arguments better than others.

In the brain, semantic information is intertwined with Shannon information.

A popular article arguing that, when evaluating LLMs, anthropocentrism is just as misleading as anthropomorphism.

Network models support novel forms of discovery, prediction, and explanation. They also raise a philosophical puzzle about unification.

Neural reuse helped to liberate humans from evolutionary constraints faced by our ancestors.

The concept of neural coding makes sense, if the codes can be learned by neurons.

If meta-cognition evolved, there is probably something like semi-meta-cognition.

Network representation compresses information about complex systems without abstracting away from the properties that make them complex.

If there are localized functions in the brain, they can only be articulated by abstracting away from functions associated with particular experimental tasks.