Minds & Machines
1st Section: True or False (10 points)
1. A machine, strictly speaking, is a tangible object, all of whose actions causally originate in its subcomponents touching each other and transferring local motion. In this sense Descartes and Boyle speak of machines in nature.
2. A machine, loosely speaking, does not have to be tangible, so long as all of its actions are automatic and not mediated by consciousness. In this sense Husserl speaks of machines in the mind.
3. Since gravity affects all tangible bodies, and is not reducible to bodies touching each other, there are, strictly speaking, no machines in the universe. Accordingly, Hume speaks of Newton having “restored [nature’s] ultimate secrets to that obscurity, in which they ever did and ever will remain.”
4. The philosophical significance of gravity is that what is physical need not be reducible to tangible mechanical interactions. Consequently, minds may not be machines and yet, with the precedent of gravity, no dualism follows.
5. Since you can touch software, and software works automatically without the intervention of consciousness, it must be mechanical.
2nd Section: Multiple Choice (10 points)
1. For Hobbes the primary sign of rational intelligence is:
b. Bodily movement
2. For Descartes the primary sign of rational intelligence is:
a. Having a soul
b. Bodily movement
3. Minds, gravity, and software are all in the same ontological boat, because all 3
a. are tangible and have causal consequences
b. are tangible and do not have causal consequences
c. are intangible and have causal consequences
d. are intangible and do not have causal consequences
4. In a Smolenskyan architecture, an activation pattern representing a constituent (A), and another activation pattern representing another constituent (B), become a single symbolic structure (A&B) through what arithmetical operation:
Section III. Short Answer (20 points)
1. Compare Schopenhauer’s trichotomous functional architecture with Fodor’s trichotomous functional architecture. How are they similar? How do they differ?
2. Describe Marr’s hierarchy of explanations. Can an artificial neural network ever be more than an implementation of a symbolic processor? If so, how? If not, why not?
3. What is the philosophical significance of deep learning?