- Artificial intelligence has been around
since the early 1950s.
Since then, research has gone up and down.
In between these bursts of interests,
there have been long cooling-off periods.
Sometimes these are called the AI winters.
Right now, machine learning and artificial neural networks
have been in an upswing.
This has led to a lot of activity around
deep learning with large datasets.
But not everyone thinks of data and pattern matching
as the surest way to intelligence.
In fact, many people see this as an improvement
of weak artificial intelligence.
Machines are getting better at very narrow tasks.
They can identify pictures
and look for other patterns in massive datasets.
That's why it's important to really understand
the benefits of different AI approaches.
Don't assume that the most popular AI approaches
like deep learning and artificial neural networks
will be the best fit for your organization.
Remember, there was only a few decades ago
when these approaches were considered
outside the mainstream.
It's likely that in the next few years
some of the most celebrated innovations in AI
will be using deep learning
or other data-centered approaches.
But it's also likely that there'll be
just as many smaller projects that use
a different approach to AI.
You shouldn't assume that because some of the largest
AI companies are using one approach,
that that approach will work the best for your programs.
Imagine if you want to create a program
that would recommend meals at a fast food restaurant.
You could certainly go through and process
millions of different customer orders.
Then you could use machine learning
to find patterns in these orders.
That way, when customer makes a request,
the machine could match it to one of the
other millions of orders.
That's likely the approach that a company like
Google, Microsoft, or IBM would have for the problem.
If you're a smaller organization,
then you might be able to build a very similar program
using symbolic reasoning.
You just have a few cashiers act as experts
to build up a series of possible orders.
You wouldn't have to crunch all that data
to come up with a workable system.
Still, symbolic reasoning has challenges of its own.
Much of what humans are good at
cannot easily be described.
Imagine if you wanted to create an AI program
that could juggle three balls.
It would be very difficult to find experts
that could describe juggling in such detail
that it could be programmed into a machine.
Jugglers don't usually think in terms of
dependent and independent variables.
In these cases, machine learning
is really the only option.
In general, when you're starting an AI project
you can think of it in two ways.
Does the program require abstract reasoning
or does it really just need detailed pattern matching?
For abstract reasoning and problems that can be
easily described, you should stick with symbolic reasoning.
These could be challenges like building software
to help with taxes, looking over a legal contract,
or checking medical prescriptions for errors.
These are all challenges that can be broken down
into decisions, actions, and symbols.
For problems that can not be easily described
or for patterns that are difficult to see,
you should stick with machine learning.
These are some of the common challenges
where you have a neural network classify
massive amounts of data or find key relationships.
The main thing you want to watch out for
is that you apply the right approach to the right problem.
If you use deep learning for an AI program
that helps with your taxes,
then you might run into some difficult challenges.
For one, most of the decision-making
will be done in a black box.
You won't necessarily know the machine's reasoning
when it works on your tax returns.
Also, a system like this would be very difficult to update.
When you have the machine create its own models
and algorithms, it's very difficult for a human
to go into the system to make changes.
The more you think about the strengths and weaknesses
of these different approaches, the more likely
you'll find an approach that works well.
Don't assume that the current trends in AI
will give you the most powerful approach
or even the most valuable.
As of right now, weak AI is a very powerful
but very narrow tool.
The more time you spend figuring out
what you want from this tool,
the happier you'll be with the result.