Ockham versus Darwin

Contemplating the comments on my last post, I began thinking about Ockham’s Razor versus Darwinian Evolution. Both of them can be used as heuristics or algorithms for creation, invention, and discovery. In 1964, Ray Solomonoff proposed A Formal Theory of Inductive Inference (Parts I and II). His theory is an Ockhamian algorithm for searching through [...]

Ockham’s Razor is Dull

Ockham’s razor is the principle that entities must not be multiplied beyond necessity. There are many different interpretations of Ockham’s razor. For me, the idea that simplicity is a guide to truth is the core of Ockham’s razor.
For any set of observations, there are an infinite number of theories that can fit the observations, with [...]

The Seductive Power of Mathematics

I believe that math is very important: My first paper was mathematical (How many ways can an N-dimensional hypercube be unfolded into (N-1)-dimensional space?) and my most recent paper was mathematical (How can a very large tensor be decomposed with limited RAM?). But medan agan: everything in moderation; nothing in excess. In machine learning and [...]

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:
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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 [...]

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 [...]

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 [...]

Three Levels of Thought

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

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

These levels might be compared to modeling physics at [...]

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 [...]

Open Problems

There was an interesting article about Einstein in The New Yorker, discussing his annus mirabilis, 1905, when he published a series of fundamental papers. One thing that was new to me in this article was that Einstein was inspired by a book by Henri Poincaré:
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