- Recently it's become a lot less expensive
to add connectivity to everyday things.
Nowadays there are thermostats and smart watches
that connect with each other and the world.
There are also cheap sensors that check room temperature,
their location,
and some can even upload your medical data.
This new way of connecting is commonly
called the Internet of Things,
sometimes you'll see it as IoT for short.
These IoT devices can be a massive new source of data.
They'll give AI assistants a new way
to interact with you.
Some of the classic examples are embedding small devices
in food packaging
so your system can tell you when you're nearly out.
Maybe your smart watch will tell your thermostat
when you're on your way home.
IoT also allows these devices
to communicate with each other.
You could unlock your doors as you approach
or have your computer turn on when you sit down.
All of your different things could be buzzing around you
like mosquitoes on a humid day.
This flurry of activity will create even more data
for machines to process.
In some ways, these new devices will make it much easier
to create data than it will be
for humans to analyze the data.
That's why many IoT companies also invest
heavily in AI programs.
Not only will AI allow organizations
to see new patterns,
it will also give them the ability to quickly react.
One area that has a lot
of potential is IoT medical devices.
You can now purchase an electrocardiogram,
or EKG sensor,
that's nearly as accurate
as the ones in your doctor's office.
These devices can check your heart's electrical activity.
They're now inexpensive enough
that many companies are embedding them
into smartphone cases,
there are even watches.
These companies can also find patterns in the data
using unsupervised machine learning
on an artificial neural network.
The network could go through the EKG data
of thousands or maybe even millions
of different participants.
They could use this data to find patterns
that might accurately predict
if you're having an upcoming health issue.
They can use regression analysis
to compare their EKG readings
to many other independent variables.
It might show that an increased heart rate in the morning
is a warning sign for different health issues.
The challenge with this approach is
that your neural network may only find patterns,
but not necessarily breakthrough cures.
It would simply notice that people
who had your EKG patterns were more likely
to run into trouble,
then it would encourage you to see your doctor.
That might be an obstacle if you're looking
to prevent illnesses.
Remember that machine learning
and artificial neural networks create a black box.
No one except for the network really knows
how the machine finds patterns.
You could react and predict,
but humans don't necessarily understand.
It's certainly helpful to have machines
find predictable patterns,
maybe they find something like people are more likely
to have health events on a Wednesday,
but good doctors should always look for causes.
That's why some companies might decide
to use symbolic reasoning instead of machine learning.
Here, doctors could clearly map out patterns
of different EKG results.
They could tell machines if they see these patterns,
then the user should probably see their doctor.
You've already seen the Internet of Things
having an impact on physical fitness.
It's very common to see people
with fitness bands tracking their heart rate
and other key metrics.
Once these IoT sensors become more sophisticated,
then we'll have a lot more data
for our AI programs.