- So far you've seen how artificial intelligence
can work in a world of data.
Artificial neural networks can recognize cats,
classify music, and make complex connections.
But not all AI technology is limited to the digital world.
It might be true that a personal assistant like Siri
spends most of their time crunching data,
but at the end of the day you're not really interested
in how well they did their homework.
They still need to speak, understand, and respond.
One of the best ways to connect up with humans
is to join us in the physical world.
That's why one of the most interesting areas
in artificial intelligence is the work around robotics.
Robotics is about having machines work on physical tasks.
It's been around for thousands of years.
Inventors have long been fascinated
with finding ways to make machines
behave like living objects.
Up until recently, robotics has been limited
to creating highly specialized machines.
The auto plants near my hometown
employed several specialized robots.
They could do things that even
the strongest humans would find impossible.
Some could easily lift a car and install parts underneath.
As impressive as they were, these robots
were very limited in what they could accomplish.
Unless they were reprogrammed,
they couldn't help a coworker open a car door
or start painting the hood.
They worked best for repetitive tasks.
Combining robotics with artificial intelligence
has the potential to give you many more options.
The machine could adapt to its environment
and learn many new tasks on the job.
A basic example of this
is an intelligent house cleaning robot like the Roomba.
It uses a form of planning AI to create a map
of your different rooms.
Then it goes through and vacuums while you're at work.
It's careful to avoid stairs and other obstacles
that might lead you to come home to its smoldering remains.
In many ways, the Roomba is similar to those
old industrial machines.
Its programming was hard-coded by an expert.
Then there are only a few rules to keep it
from tumbling down the stairs.
In fact, many need an electronic gate
to keep it from tumbling in the same place.
A more complex example is the work
around autonomous vehicles.
The newest vehicles are using
supervised machine learning on an artificial neural network.
These vehicles are outfitted with many different types
of complex sensors.
They feed data into the network
and the machine looks for patterns for successful driving.
That's why you often see these cars
with a human in the driver's seat.
They let the neural network process data
from the outside world.
When it makes an error, the human being
will step in and make a correction.
Then it will typically use a form of back propagation
to help the car learn from its mistakes.
Because autonomous cars are a massive project,
not many organizations are working on these vehicles.
There are many projects around other applications
in artificial intelligence and robotics.
One of the key challenges is figuring out
what type of artificial intelligence
might work best with your robot.
Say you wanted to create a robot that distributes
prescription medication for seniors
in a retirement community.
You wouldn't necessarily want to use
the same technology they use for autonomous vehicles.
Your robots won't run into rain, snow, or trees
in the shiny corridors of a retirement community.
For this robot you might not
want to use machine learning.
There just isn't as much data to analyze.
On top of this, there's a huge cost to making errors.
It could be dangerous if you gave the wrong
prescription medicine to several senior citizens.
There might be a lot more advantages
to using planning AI for this type of robot.
It might be tedious to have an expert program
every second of the robot's day.
But it potentially has a much smaller chance
of making an error.
You don't want this robot to learn from its mistakes.
One of the key things to figure out
is the role data plays with the robot.
Google famously said that they think
of their self-driving cars not as a robotics problem
but as a big data problem.
It's true that to figure out how to get a car
to steer left or right is trivial
compared to having a car understand
when to turn left or right.
These robotic cars will need this level of understanding
to safely drive on the road.