The present paper looks at one of the most thorough articles on the
intelligence of GPT, research conducted by engineers at Microsoft. Although
there is a great deal of value in their work, I will argue that, for familiar
philosophical reasons, their methodology, !Blackbox Interpretability"#is
wrongheaded. But there is a better way. There is an exciting and emerging
discipline of !Inner Interpretability"#(and specifically Mechanistic
Interpretability) that aims to uncover the internal activations and weights of
models in order to understand what they represent and the algorithms they
implement. In my view, a crucial mistake in Black-box Interpretability is the
failure to appreciate that how processes are carried out matters when it comes
to intelligence and understanding. I can#t pretend to have a full story that
provides both necessary and sufficient conditions for being intelligent, but I
do think that Inner Interpretability dovetails nicely with plausible
philosophical views of what intelligence requires. So the conclusion is modest,
but the important point in my view is seeing how to get the research on the
right track. Towards the end of the paper, I will show how some of the
philosophical concepts can be used to further refine how Inner Interpretability
is approached, so the paper helps draw out a profitable, future two-way
exchange between Philosophers and Computer Scientists.

通过研究 Microsoft 工程师进行的关于 GPT 智能的一篇详尽论文，本文认为现有的黑匣子可解释性研究方法是错误的，并推崇了一种内部可解释性的替代方法，以了解模型的内部激活和权重，以及它们的表示和实现算法，以此认识到智能和理解涉及的过程在意义上是重要的。同时，本文指出了内部可解释性与哲学对智能要求的可信合理的关系，以及如何进一步利用哲学概念来改进内部可解释性的方法，并呼吁哲学家与计算机科学家在未来进行双向的有益交流。