Human-like Reasoning

1/2/2008 1:56:50 AM

Human-like Reasoning

My goal in my static analysis work is not to solve general problems like the Four Color Theorem, but rather to simulate closely the human reasoning process to solve problems that are generated, comprehendible, and solvable by normal people. Practical and deep, rather than complete.

Humans write programs to be understood by other humans. Human understanding comes about instantaneously or with relatively little effort compared to the work performed by many analysis tools. We typically don't unroll loops and recursion, but instead "get" the meaning of programs by recognizing a few standard patterns. We look at a program and instantly see a sorting operation taking place, for instance. Programs typically consist of these recurring patterns. In many programs, the only complex data structures used are a standard few like arrays, hashtables, linked lists and stacks. My approach to reasoning adheres closely to the way humans think, even when more efficient computational approaches exist, so as to align my software with human performance; the one exception is when non-human-like approach is strictly better in every way, yet yields identical results.

Stephen Wolfram, inventor of Mathematica, wrote a post Mathematics, Mathematica and Uncertainty touching on some of these themes. Even with the more advanced symbolic capabilities of Mathematica--virtually infinite compared to more than my own software package, Wolfram encountered limits trying to use his own system to verify itself.

Sometimes we use the symbolic capabilities of Mathematica to analyze the raw code of Mathematica. But pretty quickly we tend to run right up against undecidability: there's a theoretical limit to what we can automatically verify.

Yet, I think that our approaches deviate somewhat in that Mathematica isn't geared to testing programs. Also, the Mathematica code base is not typical. Being a program for doing mathematics, it naturally consists of advanced mathematics, difficult for mortals to understand and involving non-linear arithmetic, which is theoretically undecidable.

The code was pretty clean. But it was mathematically very sophisticated. And only a very small number of experts (quite a few of whom had actually worked on the code for us) could understand it.

Another point that Wolfram makes is that, contrary to my approach, Mathematica typically avoids imitating humans but instead uses complicated, non-human-friendly procedures extending hundreds of pages in length, roughly the size of the entire analysis portion of my code base.

Think of almost any mathematical operation. Multiplying numbers. Factoring polynomials. Doing integrals. There are traditional human algorithms for doing these things.

And if the code of Mathematica just used those algorithms, it'd probably be quite easy to read. But it'd also be really slow, and fragile.

And in fact one of the things that's happened in Mathematica over the past decade or so is that almost all its internal algorithms have become very broad "polyalgorithms"---with lots of separate algorithms automatically being selected when they're needed. And routinely making use of algorithms from very different parts of the system.

So even if there's some algorithm for doing something that's written out as a page of pseudocode in a paper or a book, the chances are that the real production algorithm in Mathematica is hundreds of pages long---and makes use of perhaps hundreds of other sophisticated algorithms in other parts of the system.

But such algorithms were not constructed to be understandable---and much like things we see in physics or biology, we can see that they work efficiently, but they're really difficult for us humans to understand.

And in general, the way Mathematica works inside just isn't very "human friendly"... Like when Mathematica does an integral. It doesn't use all those little tricks people learn in calculus classes. It uses very systematic and powerful methods that involve elaborate higher mathematics.






My name is Wesner Moise. I am a software entrepreneur developing revolutionary AI desktop applications. I worked as a software engineer in Microsoft Excel group for six years during the 1990s. I worked on PivotTables and wrote the most lines of code in Excel 97-- about 10 times the median developer. I have a Harvard BA in applied math/computer science and a UCLA MBA in technology entrepreneurship. I am a member of the Triple Nine Society, a 99.9 percentile high-IQ society.

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