Supermodelling

Critics sometimes see the AI enterprise as merely the application of the latest tech fad — given a shiny new toy like a computer, we just try clumsily to make a metaphorical connection: mind is like a computer.

That is of course completely backwards: actually, computers are like the mind — and purposefully so. Arithmetic, algebra, logic, and algorithms of various sorts were dreamed up long before computers came along to automate them. Our ancestors invented them hundreds and thousands of years ago to provide quantitative models of bits of the world they cared about, usefully predicting and explaining things.

Since then, we’ve found other uses for computers — as media for communicating and presenting information, for example — but modelling the world and modelling mental processes have always been a major motivation behind the technology.

The sheer number-crunching capacity of computers makes it feasible to model the world with methods not directly derived from introspection — physics simulations and whatnot. And some methods start out with introspective inspiration but quickly get sharpened to a narrow point to such an extent that they no longer bear a close resemblance to their conceptual and linguistic origins.

Consider chess as an example. Starting from the introspective view that a person might think: if I do this then he’ll do that and then I can counter with such-and-so… This one little observation about a small part of the mind’s modelling of the game led to interesting techniques for tree search, and then got taken to an extreme… in this case culminating in Deep Blue and the defeat of Garry Kasparov.

When this narrowing and optimization of mind-inspired methods occurs, we typically say it is no longer AI, which actually seems appropriate to me.

Whether the source of computer models is introspection, physics, statistics, or ad-hoc empirical observation, modelling is the pervasive basis for much of the usefulness of computers. Consider a word processing program: much of its value derives from models of paper documents and other old structured comunication forms, models of language, models of printing machinery (printer driver interfaces), and so on.

Years ago I did some work for a friend of mine who owned a business that developed and sold a complex software package to help manage fleets of concrete trucks (the big ones with the spinning drums on them). Such a package is full of models, from models of general business activities like invoicing and accounting to models of delivering megatons of concrete on a customer’s desired schedule — which involves geography and roads, time required to load and unload the trucks, etc. These models make forecasting and resource allocation much more accurate.

These kinds of things are the meat and potatoes of computer applications. Web browsing, playing games, and presenting media like music and movies are probably bigger uses for most people these days, but when you scratch the surface you’ll frequently find modelling underneath: HTML is a document model, and game graphics are probably the clearest example of computer modelling you’ll ever see.

The first “real” computer, Eniac (1946), was built to calculate artillery firing tables (a pure modelling task). Today’s most powerful computer (the IBM Roadrunner, over a million gigaflops) is used primarily to simulate how nuclear materials age, modelling the safety and effectiveness of nuclear weapons (not the happiest purpose, but better than testing by exploding them).

All told, computer models comprise a hugely important industrial technology. And building them is hard, a labor-intensive and specialized hugely expensive process. The universe reflecting itself is not a trivial accomplishment! There are many clear economic benefits from: specific model construction, the invention of new modelling techniques, and improving the modelling process. Society can be served, and money made, by digging deeper into the roots of how it all works.

Our minds are really amazing modelling engines. So in a practical sense it isn’t far off to say that Artificial Intelligence == Automatic Modelling. That’s why — besides the interesting philosophical investigation of our own nature — many of us think AI is such a fascinating task. Not because we think “Wow, brains are like computers!” but because modelling modelling may be the key to the future.

I’ll end this post by listing a few computer modelling methods:

• Differential equations and approximations thereof
• Finite element analysis
• Cellular automata
• Function fitting
• Fourier transforms
• Neural networks
• Simulated annealing
• Genetic algorithms
• Hidden Markov models
• Bayes networks
• Finite state machines
• Computational algebra
• Ad-hoc programmatic models
• Object-oriented decomposition
• Monte Carlo methods
• Production systems
• Logic of many flavors
• L-systems and other generative models
• Constructive solid geometry, NURBS, etc
• Case-based reasoning
• Control theory

1. 
   Vladimir Nesov said...
   3:02 pm - June 27th, 2009

   …Abstract interpretation, Kripke structures, Process algebra, …

2. 
   Derek said...
   12:10 am - June 28th, 2009

   Thanks for the suggestions, Vladimir. It would be an interesting if exhausing exercise to catalogue literally every modelling technique I can find, with an eye toward gaining insight into what they have in common (or even the dimensions under which their commonality can be described) and the circumstances under which one is more appropriate than another.

   Regarding your specific suggestions, it is indeed particularly fascinating to consider the different ways that a program might model itself. Isn’t that playing with matches though? :)

3. 
   Brian said...
   12:55 am - August 13th, 2009

   How does the more recent “management approach” to problem solving of ‘pattern matching’ fall into this?

   In this, problems or projects of dissimilar type are mapped against potential outcomes, building up experience sets. As those projects exhibit behavior of success or failure, direction or attribute, they are then redirected. Nurtured, promoted, or killed, based on early trajectories of following prior behavior to predict outcome.

   I think this type of modelling - taking prior experience or outcomes and making “course corrections” based on similarities to those attributes, most closely aligns to how (some of us) think. apologize for lack of familiarity with all the modelling techniques, but isn’t this a part of the puzzle? As brains, we don’t process “all the info” to make our decisions, we just match up similar experiences, whether driving a car, or playing chess, or walking…why should computers do all that? Because it can? Or are we following what it can do, vs what it should do?

   Curious stuff though - thanks for musing!