Jan Krüger's blog

Creative Engineering and randomness


Pseudoscience is significantly worse than what science claims to be, and that’s the problem: science isn’t actually what it claims to be. Many people who boast about their extremely scientifically oriented thinking don’t actually know what science is, and they’re actually thinking religiously or even dogmatically.

Whew. That’s a rather provocative first paragraph, isn’t it? I’ll have to be extremely scientific to avoid getting shouted at by would-be scientists. Don’t worry, dear scientists, I’m not against science! I’m a huge fan of it. Until some “scientist” starts making overly general statements. That’s where it stops making sense. Why? Well, let’s have a brief look at how science works.

Here is a (not necessarily complete) list of philosophical “isms” that I believe in. I don’t believe in “isms” lightly at all, because I feel that adopting an “istic” view is a rather drastic thing to do.

The following list will give you deep insight into the way I understand life, reality and science… if you want to find out, that is.

Okay, so there are problems in knowledge engineering, AGI in particular (to recall, AGI is a machine or program that can demonstrate intelligence on the complexity level of humans). More generally, in every domain of sufficiently complex structure, AI fails, sometimes spectacularly. A well-known example is the board game Go, for which nobody has managed yet to design a computer opponent that can beat players above the level of novice.

Yet humans manage many of these tasks seemingly without any problems. One might be tempted to think that the human brain is the ideal “thinking machine”. In reality, it has a staggering number of bugs which produce incorrect actions or results in a variety of situations.

Given the “right” philosophical attitude about how the world works, the ultimate goals of knowledge engineering, namely obtaining, processing, using and making accessible all kinds of knowledge, can definitely be achieved. This set of bold goals, however, presents researchers with very difficult problems. All attempts that exist today are restricted to small classes of knowledge.

What do life and knowledge engineering have in common? Everything. There, that’s all keywords from the topic. But I guess you’d like a little more detail.