All people are so smart that they get bored quickly; the smarter the quicker. That is why we always look for new paths — and, if lucky, find new ones that are of interest. We are the creating kind — because we get bored.
(That is also why it is so enlightened to help people escape their state of boredom by stimulating opportunities, and so criminal to trap people in a state of boredom by blocking their opportunities.)
It can be tempting to think that science is the pursuit of universal truths. A hypothetical fact would then only be scientific when it describes some (nearly) universal pattern. And indeed some of the best examples of good science are of this kind; the paradigmatic one is of course Newton’s laws that unified the falling of apples and the circling of planets. Universal indeed! (If you read Dutch: I found Floris Cohen’s Isaac Newton en het ware weten worth my while.)
But one should not give in to this temptation. Universals are scarce, while defeasibles are ubiquitous.
Science is the pursuit of interesting truths. And interesting truths can be very specific. The discovery of the coelacanth is one example; another is the building of a computer that can beat the chess world champion, or more recently, also by IBM, a program that can play the strange game of Jeopardy well. In such cases it seems to even matter less how well the result is described in a peer reviewed highly ranked journal. Some results are just interesting in themselves.
Of course greater universality can make a truth more interesting. But more often it is the defeasibles that are the most interesting: defeasibles have the power to describe patterns as they exist in their relevant context, including a specification of how they depend on circumstances.
The here-and-now character of many interesting truths is (in my interpretation of his work) one of the themes of Stephen Toulmin; e.g., in his book Return to reason.
On the [ARGTHRY] mailing list the plan was posted (by David Hitchcock) to aim for the translation of Harald Wohlrapp‘s Der Begriff des Arguments. I read the interesting review by Christian Kock. Here is a segment of the review that struck me, in particular the last couple of sentences:
Wohlrapp is an independent and original thinker who wants to belong to no school, but has a broader intellectual scope than existing schools. In true Aristotelian manner, he presents a sane and balanced theory based on observation and reflection rather than on axioms. But I question his desire to present a unified theory that downplays the distinction between facts and norms; while an individual cannot have his own individual facts that differ from other people’s facts, he can have his own individual norms which in turn dispose him for his individual choices. In his attempt to counter what he sees as ‘rampant relativism’ of our time Wohlrapp sometimes seems to me to throw out the baby, choice, with the relativistic bath water. What alarms him about the relativism he sees around him is “the belief that, at the end of the day, arguing is useless” (6). But there is no need to believe that arguing is useless even if we abandon the idea that argumentation theory can dictate our moral and political choices. There has always been and always will be argument about issues where choice is possible. That kind of argument is what I understand by ‘rhetoric’. We need thinkers like Wohlrapp to theorize such argument.
(In passing Kock mentions that only Trudy Govier studied the weighing of pros and cons. In AI & law there is more. See Jaap Hage‘s work on this and my own, often coauthored with Jaap.)
I recently visited the 2010 AAAI Fall Symposium ”Computational Models of Narrative” (Arlington VA which I now know to be bordering on Washington DC) as organized by Mark Finlayson, Pablo Gervás, Erik Mueller, Srini Narayanan and Patrick Winston (the papers are here, somewhere among many others). It was a fine followup of another fine meeting organized by Whitman Richards, Mark Finlayson and Patrick Winston (the 2009 MIT Workshop on Computational Models of Narrative, see the report).
Patrick Winston emphasized the importance of stories for our lives by expressing the Strong Story Hypothesis (at the plenary meeting of the Fall Symposia). Shallowly paraphrased: our story capacity is our distinguishing characteristic. We also saw Roger Schank speak (wearing glasses, or wasn’t he?), which was a treat, one of several offered at the meeting.
Here is Bob Abelson commenting on Schank’s 1982 book Dynamic Memory (unfortunately as a jpg):
Schank himself quotes this nice excerpt in his text in the 1994 festschrift for Abelson (p. 31-32). What I find particularly stimulating here is how the (for me slightly cliché) memory reported on becomes fresh and shining (for me) by the analogy made with research writing. I like stories. They are important.
John Pollock, a founding father of the area of defeasible argumentation, has died. He combined theoretical, computational and practical considerations in his design of an ‘artificial person’, OSCAR (see, e.g., his Cognitive carpentry). In this high ambition, he has had no followers.
See, e.g., his influential paper Defeasible reasoning (in Cognitive Science, 11:481–518, 1987; also available in full text). A – too brief – one page introduction to his influential ideas on undercutting and rebutting defeaters is in this text (p. 229) on that other founding father of defeasible argumentation Stephen Toulmin.
I came across Marvin Minsky’s 2006 book _The Emotion Machine_ (draft available at his site). I briefly looked at what he had to say about commensense, the biggest hurdle for AI. Here is how he estimates how much a typical person knows, ‘a few dozen millions of items of knowledge’:
Everyone knows a good deal about many objects, topics, words, and ideas—and one might suppose that a typical person knows an enormous amount. However, the following argument seems to suggest that the total extent of a person’s commonsense knowledge might not be so vast. Of course, it is hard to measure this, but we can start by observing that every person knows thousands of words, and that each of those must be linked in our minds to as many as a thousand other such items. Also a typical person knows hundreds of uses and properties of thousands of different common objects. Similarly, in the social realm, one may know thousands of things about tens of people, hundreds of things about hundreds of people, and tens of useful items about as many as a thousand people.
This suggests that in each important realm, one might know perhaps a million things. But while it is easy to think of a dozen such realms, it is hard to think of a hundred of them. This suggests that a machine that does humanlike reasoning might only need a few dozen millions of items of knowledge.
I found it worthwhile to read a bit in this work by one of the big defenders of ‘knowledge-intensive AI’; the old-fashioned kind that I am a child of. (I only recently learnt that the pun GOFAI for good old-fashioned AI should probably be pronounced with ‘o’ as in ‘goof’.) I saw that (in the same chapter on commonsense) Minsky briefly comments on the currently fashionable ideas that we need to copy the brain and that we need to harvest the web.
Here are some points in a review by the neurologist Richard Restak that make me curious:
Actually, the loss of cells results from passive disuse — use it or lose it — rather than active deletion.
Of the 1.1 trillion cells in the human brain, only 100 billion are neurons.
[A]natomical interaction of neurons highlights only one aspect of brain functioning. Equally important are alterations of the brain’s chemical messengers, the neurotransmitters, along with changes in local and distributed electrical fields.
I looked at Minsky’s web site after I Wolfram|Alpha’ed him together with Norbert Wiener, about whom Lambert spoke to me yesterday. (Beware: Wolfram|Alpha is the latest geek tool; innovative, useful, addictive.)
In AI, prediction is associated with people such as Norbert Wiener (who “worked with McCulloch and Pitts and influenced a number of young researchers including Marvin Minsky”, Russel & Norvig 2003, 757). In law, prediction is associated with Oliver Wendell Holmes’ stance towards law: the law should be regarded as a prediction of what brings punishment or other consequences from a court (see Wikipedia; see there how Hart took a different view).
I have been reading in a special issue of Foundations of Science on the connections between argumentation and mathematics (Vol. 14, Nos. 1-2, 2009); guest editors Andrew Aberdein and Ian J. Dove. There is Andrew’s useful introduction to the issue, and also a paper by Alison Pease and colleagues on the computational representation of Lakatos‘s proofs and refutations (see also Mandy Haggith’s ‘A meta-level argumentation framework for representing and reasoning about disagreement‘). Nice! Andrew gives pointers to Wilfrid Hodges’ text ‘An editor recalls some hopeless papers‘ on journal submissions disproving Cantor’s diagonal argument and to recent work by Erik Krabbe connecting pragmadialectics, argumentation and mathematics (‘Strategic Maneuvering in Mathematical Proofs‘; with a comment by Sally Jackson). Hodges refers to psychological work by Rips and also by Johnson-Laird and Byrne – about whose Deduction he is interestingly critical (“I know I am not alone in finding its accounts of logical theory almost incomprehensible”). Krabbe quotes Goethe:
Die Mathematiker sind eine Art Franzosen: redet man zu ihnen, so übersetzen sie es in ihre Sprache, und dann ist es alsobald ganz etwas anders.
Krabbe also discusses a ‘proof’ by mathematical induction that all horses have the same color.
Interesting, and simply: good fun.
In his paper Is logic empirical? Putnam has defended (Wikipedia: “at one point in his career”) that quantum logic is the ‘right’ logic from an empirical point of view. Quantum logic fails the principle of distributivity: p & (q v r) <=> (p & q) v (p & r).
Erik Sandewall reviewed the recent Handbook of Knowledge Representation in the Artificial Intelligence journal. He mentions two contemporary incarnations of Cyc: Freebase and Dbpedia. Here is his final sentence:
[O]ur field is missing a very important part of its mission when we focus so much on logic, algorithms and other methods for the representation and the computational use of knowledge, and when we disregard so regularly the actual bodies of knowledge that our methods are supposed to operate on.
