- [Instructor] One of the most popular tools in business
related to AI is what's called an expert system.
Expert systems are used across a variety of industries.
In the medical field, expert systems can be used
to help triage patients,
determine what a particular disease is,
which patients need to be seen when,
how urgent their cases are, et cetera.
In the area of agriculture or farming,
expert systems can be used to determine
when seeds should be planted,
what type of fertilizer should be used,
what the likely output of a particular crop
in a particular planting season is going to be.
Expert systems are used in missile guidance, in defense,
for instance, in the Patriot missile intercept system,
or in financial decision-making when it comes
to things like currency trading.
The common factor across all of these industries
and others involving expert systems is the application
of large amounts of data to a problem.
The system has a database that it understands in some sense,
and then it can make decisions or predictions based on that.
When we think about the categories of expert systems,
there's a variety of them out there, but to begin with,
the simplest ones are just decision-making.
We looked at an example of this previously
when we thought about a loan versus no loan type decision.
But we could think about this in the context
of buying or selling a particular asset,
or is there risk or no risk in a particular
business relationship?
In a weather context, rain versus no rain.
So, decision-making like this is fairly straightforward
for an expert system.
We can also think about this in the context
of troubleshooting.
The chatbots that you see on websites all the time,
you know, when you go to a Microsoft site,
the chatbot might pop up and say,
hi, welcome to Microsoft; how can I help you?
And then we go through and talk to it about
what our concern is or why we're contacting
customer service, for instance.
Expert systems are very good at that sort of thing.
Similarly, we think about classification of diseases
or in the medical field.
This is another area where expert systems can be of help.
Even in interacting with the physical environment.
We think about self-driving applications
or robotic controls.
How does the car know that it needs to stay on the road?
We have the information coming in,
so it understands where the boundaries of the road are.
That's great, but just understanding the boundaries
of the road or the fact there's other cars there,
how does the vehicle know that it needs to stay on the road?
The answer is that it requires an expert system.
In every expert system, we have a couple
of different components.
We have an interface and we have a knowledge engine.
That knowledge engine is powered
by some sort of a human behind the scenes
that uploads data to the AI program
and helps the AI system to learn the facts,
and I use the term learn loosely here, of course.
But the AI has to understand what the rules are
and what the relationships between that data mean.
From there, we have some sort of an interface,
which then allows other humans to interact with it.
So, if we're thinking about, say, an obvious application,
like in medicine, we'd need a database of past diseases
and the symptoms of those diseases.
The computer then learns what all those symptoms are
and what the associated diseases are,
and based on that knowledge base,
it could then interact with human doctors
who observe symptoms on a patient
and it could make a recommendation
for here's two or three diseases that are most likely
to be the source of that particular patient's problems.
Expert systems are all around us.
There's a variety of them in commercial use already.
Think about whether they might be applicable
to you in your business context.
Expert systems are not particularly expensive,
and they don't replace humans.
They're really just a tool to enhance the decision-making
of humans in a standard business context.