Page 241 - Ray Dalio - Principles

Before I explain why, I want to clarify my terms. “Artificial
intelligence” and “machine learning” are words that are
thrown around casually and often used as synonyms, even

though they are quite different. I categorize what is going on in
the world of computer-aided decision making under three
broad types: expert systems, mimicking, and data mining
(these categories are mine and not the ones in common use in
the technology world).

Expert systems are what we use at Bridgewater, where
designers specify criteria based on their logical understandings

of a set of cause-effect relationships, and then see how
different scenarios would emerge under different
circumstances.

But computers can also observe patterns and apply them in
their decision making without having any understanding of the
logic behind them. I call such an approach “mimicking.” This
can be effective when the same things happen reliably over

and over again and are not subject to change, such as in a
game bounded by hard-and-fast rules. But in the real world
things do change, so a system can easily fall out of sync with
reality.

The main thrust of machine learning in recent years has
gone in the direction of data mining, in which powerful

computers ingest massive amounts of data and look for
patterns. While this approach is popular, it’s risky in cases
when the future might be different from the past. Investment
systems built on machine learning that is not accompanied by
deep understanding are dangerous because when some
decision rule is widely believed, it becomes widely used,
which affects the price. In other words, the value of a widely
known insight disappears over time. Without deep

understanding, you won’t know if what happened in the past is
genuinely of value and, even if it was, you will not be able to
know whether or not its value has disappeared—or worse. It’s
common for some decision rules to become so popular that
they push the price far enough that it becomes smarter to do

the opposite.