model of skill acquisition, with which they argue that machines could never develop the skills humans do, in the depth that we do them. This is because while computers operate solely on explicit instruction, skills that humans excel at are executed with a high degree of “tacit knowledge” that cannot be reduced to explicit instructions (think muscle memory, kind of). However, many significant milestones have been passed since this book’s publication, and there now exists AI which can handle tacit knowledge pretty well.
Fjelland’s goal in this paper is to show that Dreyfus’s argument still holds, despite the extensive development of artificial neural networks. He argues that modern methods (e.g. ML) are merely correlative, they know nothing of causation. A higher level of reasoning, with a deeper understanding of the events in question and the context they occured in is necessary to determine causation. Thus, a machine that is not in-the-world cannot reason about causal relationships.
“We are bodily and social beings, living in a material and social world. To understand another person is not to look into the chemistry of that person’s brain, not even into that person’s soul, but is rather to be in that person’s ‘shoes’. It is to understand that person’s lifeworld.”
And you’d be in the company of some experts! ↩
I’ve always wanted to say this. ↩
Pronounced däzīn. This is a German word which just means “being there”. Our usage comes from Martin Heidegger (phenomenologist and student of Husserl). Dasein is a very important and nuanced concept for Heidegger, we’re being a little fast and loose. ↩
”Zuhandenheit”, in German. Contrast this with a present-at-hand (“vorhandenheit”) approach: perception of the world as objects with articulable properties. ↩
Heidegger, Being and Time. ↩