
Fuzzy Logic, Fuzzy Ethics - Futurebot
https://reallifemag.com/fuzzy-logic-fuzzy-ethics/
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DonaldFisk
> Cyc still proved incapable of more demanding tasks, like digital image
> recognition, or more complex gameplay, like chess or Go.

As Cyc wasn't designed to recognize objects in images, or play chess or go,
it's hardly surprising that it proved incapable of those tasks.

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skybrian
This article gets history approximately wrong. Yes, Cyc failed at its original
goals, but it's still going, kind of, and as far as I know was never intended
for image recognition. Fuzzy logic isn't used much in science because
probability and statistics are the more usual ways to handle uncertainty.

Given its inaccuracy so far, I'm not sure the article is worth finishing?

~~~
dfrage
To quote Wikipedia it is:

> attempting to assemble a comprehensive ontology and knowledge base that
> spans the basic concepts and "rules of thumb" about how the world works
> (think common sense knowledge but focusing more on things that rarely get
> written down or said, in contrast with facts one might find somewhere on the
> internet or retrieve via a search engine or Wikipedia), with the goal of
> enabling AI applications to perform human-like reasoning and be less
> "brittle" when confronted with novel situations that were not preconceived.

As explained by Douglas Lenat in a talk in the 1980s, he said his AI efforts
had reached a point where there was a mattress on the road blocking him, and
he decided to do something about it. As the article
([https://en.wikipedia.org/wiki/Cyc](https://en.wikipedia.org/wiki/Cyc))
alludes to in passing, it started out as an effort to capture the information
_needed to understand_ all the entries in a 2 volume encyclopedia", "Cyc"
itself is from encyclopedia.

Image recognition? Well, I suppose once you identify an object, Cyc might be
useful to reason about it, but....

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whatshisface
I remember reading about fuzzy logic in a discrete math book. It was exactly
the same as basic probability theory. I think the article is overblowing it a
little...

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DonaldFisk
Not my recollection and I gave a lecture on it (in 1987):
[https://web.archive.org/web/20081202004655/http://web.onetel...](https://web.archive.org/web/20081202004655/http://web.onetel.com/~hibou/ai-
course/lec02.txt)

There are crisp sets, such as the set of French people. People are either
French or not French. It doesn't make sense to say that someone's not very
French, for example. But it does make sense to say that there's an 80% chance
that someone's French, given other information about them.

The set of tall people is a fuzzy set. It does make sense to say that
someone's not very tall. Tallness is a mapping from height to degree of
membership of the set of tall people. So, for example, tallness(1.6m) = 0.0,
and tallness(2.0m) = 1.0. To find out whether someone's quite tall, you could
use the square root of tallness,to find out whether someone's very tall, you
could use the square, and to find out whether someone's not tall, you could
subtract their tallness from 1.0. And you might want to put a value on whether
someone's quite tall but not very rich, for example, or make deductions given
this.

Maybe at some deeper level fuzzy logic does reduce to probability theory, but
the idea is quite different. It's about vagueness rather than about
uncertainty.

~~~
whatshisface
Maybe that example could be reduced to probability by saying you had an
ensemble of different classifiers, each with its own hard cut. Then, you would
ask, "what is the probability that a randomly selected judge would call Sally
tall?"

~~~
jacques_chester
This doesn't really reduce it though, it just changes the problem from degree-
of-tallness to probability-of-opinion.

If I round up a million and people and survey them about whether -1, 0 and 1
are natural numbers, the probability of their opinions don't change whether
-1, 0 or 1 are natural numbers.

The probabilities of judgement might be 0.1, 0.55 and 0.92, with confidence
intervals. But each of these numbers has a set membership function and each of
them is crisp. In fuzzy sets these belong to the set of natural numbers as
0.0, 0.0 and 1.0.

The useful concept that fuzzy sets brought to the table was being able to
reason formally over uncertain values. Classical logic operates on crisp sets:
all men are mortal, Socrates is a man, therefore Socrates is mortal. Fuzzy
sets allows you to retain all the tools of logic over a wider range of
statements.

This is widespread in control systems, where being able to reason formally
about vague concepts like "too fast" is very helpful.

It's also common in caselaw, which is absolutely jampacked with fuzzy sets.
"The purchaser at arm's length without notice". "The reasonable person,
similarly circumstanced". "A duty of care". Different sets of facts in a case
are ultimately resolved into binding rulings by a judge, but the argument
itself has to follow logically from facts, legislation and precedent.

The law is also rightly aware of probabilities, especially in civil cases.
"The balance of evidence" is sometimes called "the balance of probabilities".
In criminal trials, "beyond reasonable doubt" is mostly about deciding on what
follows from the probabilities of the facts presented.

