Why Computational Linguistics?

I was thinking about what to say to a student who is contemplating a career in computational linguistics. How can I convey my enthusiasm? How can I explain my fascination with language? Here are some of the things that came to mind:

AI Success Stories

I’ve been invited to give a talk on AI Success Stories, so I’ve compiled a list of things that illustrate progress in AI research. By success, for the purpose of this talk, I mean something that is interesting and impressive to a wide audience, rather than something that is successful in terms of commercial or industrial impact, or successful in terms of abstract mathematical or scientific significance, although I may mention a few examples of this other kind of success. Please let me know if there is anything you would add to this list.

Scientific Productivity, Age, and Field

I once saw a graph that plotted scientific productivity as a function of the scientist’s age, with different curves for different scientific fields. I remember that the curve for mathematics peaked between the ages of 20 and 30, but the curve for chemistry peaked somewhere around 50. There was no curve for AI researchers, and I occasionally wonder where such a curve might peak. This ties into the neats versus scruffies debate. The neats probably peak early, whereas the scruffies peak late. A good strategy would be to be neat early in life, and switch to scruffy later. Today I went searching for a copy of that graph. I didn’t find it (let me know if you can find it), but I found a lot of interesting studies on the topic.

The Evolution of Movies

In a previous post, I discussed multiple, independent, simultaneous discovery in science and technology, which supports the claim that science evolves. The authors of Multiple Discovery devote a chapter to arts and literature, but their main focus is science. I was thinking about multiple, independent, simultaneous creation in the arts, and I recalled several cases where two movies on the same theme were released at about the same time, apparently by coincidence:

1. Deep Impact (1998) and Armageddon (1998)
2. Capote (2005) and Infamous (2006)
3. Dangerous Liaisons (1988) and Valmont (1989)
4. The Matrix (1999) and The Thirteenth Floor (1999)

A Scientific Approach to Morals and Ethics

Ethical axioms are found and tested not very differently from the axioms of science. Truth is what stands the test of experience. — Albert Einstein

The traditional view is that science has nothing to say about ethics and morality. Science tells us what is and morality tells us what ought to be. You can’t get ought from is. Science can help us predict the consequences of our actions, but it cannot tell us which consequences we should seek and which we should avoid. How, then, do we decide what is ethical? We must look in our hearts and souls; we must turn to religion or spirituality. We must seek ethical axioms to which all can agree (except psychopaths, sociopaths, deviants, and people who are not like us) and make sure that our actions conform to those axioms. I disagree with this view.

How to Maximize Citations

The Seven Secrets of Highly Cited Scientists

A couple of years ago, I discussed with some colleagues the topic of maximizing citations for academic research papers. Here is a summary of the discussion.

Why should we want our papers to be highly cited? I assume here that we want our work to influence other researchers, and that citation count is a reasonable estimate of influence.

Survey/review papers and methodology papers are often highly cited, but the focus of this discussion is on papers that present original work, although there is certainly merit in survey, review, and methodology papers. (By the way, there is evidence that citation counting is biased towards survey/review papers.)

Language Affects Perception

Here’s an update to my post on the Sapir-Whorf Hypothesis. Lera Boroditsky answered the question, “What have you changed your mind about?” as follows:

I used to think that languages and cultures shape the ways we think. I suspected they shaped the ways we reason and interpret information. But I didn’t think languages could shape the nuts and bolts of perception, the way we actually see the world. That part of cognition seemed too low-level, too hard-wired, too constrained by the constants of physics and physiology to be affected by language.

And here is why she changed her mind:

Three Levels of Thought

Peter Gärdenfors proposes that there are three levels of abstraction for modeling thought:

1. Symbolic: logic, expert systems, Prolog, Cyc, good old-fashioned AI, theorem proving
2. Spatial: geometry, feature spaces, conceptual spaces, semantic spaces, information retrieval, vector space models, latent semantic analysis, machine learning
3. Connectionist: neural networks, Hebbian theory, associationism, perceptrons, neuroscience

These levels might be compared to modeling physics at (respectively) the molecular, atomic, and subatomic levels.

Artificial Intelligence Considered as Heavier-than-air Flight

“With admirable can-do spirit, technological optimism, and a belief in inevitability, psychologists, philosophers, programmers, and engineers are sure they shall succeed [in creating human-level artificial intelligence], just as people dreamed that heavier-than-air flight would one day be achieved. … After more than 50 years of pursuing human-level artificial intelligence, we have nothing but promises and failures. The quest has become a degenerating research program …” — Peter Kassan, computer scientist, 2006.

“It is apparent to me that the possibilities of the aeroplane, which two or three years ago were thought to hold the solution to the [flying machine] problem, have been exhausted, and that we must turn elsewhere.” — Thomas Edison, inventor, 1895.

Readings in Analogy-Making

Here are some links to interesting books, papers, people, and websites related to analogy-making and metaphor. If I’m missing a link that you would recommend, please leave a comment. Look here for a list of quotations about analogy-making and metaphor. There is no particular order to these lists.