- In the very beginning, artificial intelligence
used something called the symbolic systems approach.
This approach allowed machines to act in a way
that seemed intelligent,
but in reality it was just complex pattern matching.
The symbolic systems approach allowed early programmers
to create expert systems.
These machines could do things
that were normally left to experts.
They could diagnose illnesses, give you a credit score,
and even help you with your taxes.
The problem with these systems is that they
created long lists of matching patterns.
They also required experts to create these patterns.
You needed someone who knew Chinese
to match every possible phrase.
Maybe you'd need a doctor who would
come up with an answer
to every one of a patient's questions.
You had to create these long lists of matching patterns.
Sometimes this is called combinatorial explosion.
This type of effort made it extremely difficult
to create robust expert systems.
There was also the very real possibility
that you wouldn't find a match.
A nurse might go through a dozen questions
and only at the end find out that there's no match.
Or a Chinese speaker might write a statement
that was not included in any of the long lists of responses.
These challenges made it really difficult
for expert systems.
Because of that, they started to disappear
in the late 1980s.
Still, the disappearance of expert systems
didn't entirely eliminate the symbolic systems approach.
Instead, you can still commonly find this approach
in what's called planning artificial intelligence.
It's called planning artificial intelligence
because you still have to plan out
how to match your symbols and patterns.
It tries to solve the problem
of the long lists that you get with the expert systems.
Instead, it uses something called heuristic reasoning.
This reasoning gives artificial intelligence
a form of common sense.
It tries to limit what patterns
the program has to match at any one time.
This is sometimes called limiting the search space.
Think about how heuristic reasoning
might work for the Chinese room argument.
If you're trying to create a program with planning AI,
then you might use heuristic reasoning
to limit the possibilities of the first note.
The program might expect something like
"Hello" or "How are you?"
Maybe even something like "Do you speak Chinese?"
If the program is expecting these as a first note,
then it limits how far it has to search
to match the pattern.
One common place that you'll see AI planning
is with navigation systems like Google Maps.
Here you'll put in your starting location
and your destination, and then the system
will step through all the possibilities
to find you the best route.
It's still using a symbolic systems approach.
All the roads and street names
have been carefully mapped out.
The system matches your destination
with the patterns that it has in its list.
You may have even seen one of the Google mapping cars
driving on the road, collecting the street names
of all the best routes.
These Google cars were creating their own lists.
If you turn right at the stop sign,
then you want to turn left when you reach
the end of the road.
The big difference is that a planning AI system
like Google Maps uses this heuristic reasoning.
Google Maps knows that there are only a few possibilities
each time you make a turn.
You won't be able to turn right
onto a street that's hundreds of miles away.
Even though it's considered old-fashioned AI,
the symbolic systems approach and planning AI
are still used in many new projects.
Planning AI performs really well in systems
that have predefined symbols and patterns.
You can see this with driving directions,
but it also works well with legal contracts,
logistics, and even video games.
The trick is to make sure
that your heuristic reasoning is accurate.
Like most common sense, if you turn out to be wrong,
then pretty much everything you assume aftewards
will also be incorrect.
Think about how your mapping software might react
if early on you turn onto a road that hasn't
been added to its list.
Even though you might use your mapping software every day,
it still comes from symbolic system ideas
that came out in the 1950s.
A lot of newer AI projects just assume
that they should use newer approaches.
The newer approaches might not be
the best fit for your organization.
If you have a new AI project,
don't be quick to dismiss the benefits
of good old-fashioned AI.