I recently started reading Topology for Computing by Afra J. Zomorodian, and before it launched into its admirably lucid explanation of topology, group theory and the like, it featured a clever illustration by the author:
|
||||
|
In a twitter-like display of attentional deficit, I present to you, a list of things I am currently thinking about:
Please comment if (and this is a big if) you find any of these admittedly fragmentary ideas/questions intelligible. by Kurt Vonnegut Give us this day our daily bread. Oh sure. Ned Block defined a system known today as a Blockhead (“Troubles with Functionalism”, Minnesota Studies in the Philosophy of Science) to illustrate a problem that look-up tables pose for the Turing test. Blockhead is a “stupid” machine that stores all possible conversations within some limited duration and, thus, passes the Turing Test. This is, of course physically impossible, as Frank Tipler argues with back of the envelope calculations in The Physics of Immortality:
In typical philosophical fashion we suspend the laws of physics and look to conceivability and logical possibility to analyze this scenario. Given that Blockheads are logically possible, what does it say about the Turing Test? Should we not attribute intelligence to Blockhead merely because he uses brute-force methods? It is difficult to know how to respond to this. When we are trying to break a candidate program for the Turing test we ask it questions that, while coherent, have high surprisal. This is used to defeat look-up tables/canned responses, which are the hallmarks of poorly designed AI. However, in this case it is stipulated that the table has all of the possible answers, so such a strategy is useless. So, we have a system that passes the Turing Test with abhorrent methods. Is this really a problem?Let’s consider the larger set of agents I call Brutes (for brute force). Brutes use any or all algorithms that are foreign to ours, or are, in general, unappealing in their processes. This can be in the form of look-up tables, randomized answers, Liebnizian monsters that have monadic processes that never talk to one-another or receive any input, etc. However, Brutes, by fiat pass the Turing test. Now suppose after a neuro-imaging breakthrough we discover some percentage of the population utilizes some abhorrent algorithms in their thinking. Are we to then discount them as being mere Brutes, not truly thinking, feeling people? To do so would be ridiculous. Now suppose that some percentage of the population use nothing but abhorrent algorithms. Can we dismiss them? Again, I do not think so. To maintain otherwise seems to me to be a result of arbitrary algorithmic chauvinism or finding consciousness and intelligence to be an essence that exists apart from function–the same intuition that allows philosophers to take zombie arguments seriously and succumb to the quagmire of privileged access. In a way, talk of Brutes and Blockheads just misses the point of the Turing test, which is to dissociate the methods, substrate and appearance of the candidate system from the difference that makes a difference: behavior. I do not see how we can hold an agent’s (or potential agent’s) algorithms against them. In the past few months I have been challenged to defend computational philosophy, particularly philosophical modeling. Philosophical modeling, like scientific modeling, is a formalization process. However, instead of capturing real-world phenomena, philosophical modeling captures thought experiments. The process of encoding a thought experiment in a formal system is, itself, beneficial in the same way as standard conceptual analysis: hidden assumptions are unburied and seemingly simple ideas yield refined notions. But, in a way, encoding is more honest–the process is not satisfied until you reach a syntactic, algorithmic level of explicitness and, once our intuitions are encoded, further light may be cast during runtime. Will your thought experiment crash, loop endlessly, or churn away at intractable problems? Do your assumptions actually lead to your conclusions? Do they yield more than expected? Less? As any programmer knows, what we program to happen and what actually happens can be quite different. Philosophers often use formal systems to reinforce their arguments, but they are typically piecemeal and the most significant assumptions (often unintentionally) remain outside the formalism. Computers are well suited to explore certain features of complex systems like thought experiments. With a computer, philosophers may encode their thought experiments and systematically explore their features. This cannot be matched by armchair speculations. We are prone to errors in inference and unconscious biases, and simply do not have the time or energy to consider the complex interactions of philosophical presuppositions. I should note that this methodology is not completely foreign to philosophers. Here are a few examples:
In the last essay we are encouraged to "put our model where our mouth is" when it comes to our epistemological theories. Indeed we should, in epistemology and elsewhere. |
||||
|
Copyright © 2010 Johnny Logic - All Rights Reserved |
||||