- Examples of machine learning and AI take zero.
(clapping)
(upbeat music)
This is a quick video that's a followup to
what is machine learning and what is AI.
So examples of machine learning,
the two most common examples,
facial recognition and speech to text.
And then examples of AI,
lets do personal assistant, okay.
So lets say Siri.
- [Male] Self driving.
- Well yeah we could, self driving car would be
a perfect one right.
I'm gonna use personal assistant
so that we can use both of,
but personal assistant so this would be Siri or Alexa.
We wanna drive home the difference between
machine learning and AI.
So examples of machine learning is facial recognition.
The machine learning algorhithim for facial recognition
is only concerned with one thing,
and that is accurately identifying a face.
What it does is over the course
of time, when I, every time I scan my face on my iPhone here
I use facial recognition to unlock my phone.
The machine learning algorhithim is only concerned
with acquiring the knowledge of what my face is.
Now I gain weight, I lose weight, I get a tan,
I lose a tan, I have a different haircut,
I wear sunglasses, I grow goatee,
I don't grow a goatee,
so when the machine learning algorhithim
originally identifies the points on my face
that say okay I think I know what face I'm looking at,
well that's a snap shot in time.
Over the course of many, many times scanning my face,
the machine learning algorhithim is concerned
with just determining accurately
whether or not the face I'm looking at right now
is a face I looked at previously, okay.
That's what it's concerned with learning.
It is acquiring the knowledge of that face
and all of its variations.
As it changes over time.
Speech to text.
When I say hey Siri, hey Siri, what time is it?
- [Siri] It's 11:59.
- So Siri is concerned with two things.
Number one, identifying my voice.
I'm the only person who's
supposed to be able to activate
this specific Siri on this specific phone.
She's concerned with two things.
Identifying my actual voice
and accurately determining what it is I said.
And she uses data over the course of time
to determine is it the voice I heard before?
And what is that voice saying?
That's what machine learning does.
That's an example of machine learning.
Here's another example of machine learning.
Waze is a perfect example of machine learning.
Waze determines my travel habits.
Here's a little know fact.
The GPS that I have in my phone, okay.
That GPS that I have in my phone
sometimes doesn't work.
Sometimes my phone determines I'm in a place
I'm not actually in.
The machine learning algorhithim is gonna determine
whether a set of GPS coordinates
that was plotted in my database in here is an anomaly
or whether machine learning can trust those data points.
In the very beginning days of GPS,
if you had gone to google.com/location
while you were logged into your Google account
and you had an Android phone,
you would be able to see every location
you had ever been plotted on a map.
And occasionally you would see a plot
Google had determined you had been
in a place you had never been.
Well it was because their machine learning algorhithim
hadn't learned enough about you yet
to determine that you definitely
had not been in that place.
So it was actually inaccurate.
Over time it became more and more accurate.
That's machine learning.
AI on the other hand is concerned with optimal outcomes.
So it uses information that's gleamed from machine learning,
things like what's Walker's face
and what did he actually say.
So here's an example.
I would say something like hey one of you two
where should I go to dinner tonight?
So not give me directions to such
and such restaurant,
but where should I go to dinner tonight?
The personal assistant's going to make a recommendation
to me based on the previous restaurants
I've gone and eaten at.
And reviews that I've submitted on Yelp,
or information that I've,
previous times that I've asked for directions
to certain types of restaurants.
The personal assistant, the AI is going to make
the optimal recommendation for where
I should go to dinner.
So in manufacturing, a question we might ask AI is
hey personal assistant, which of my machines
should I turn off?
And what the personal assignat is going to tell us
is the one that will cost us the least amount.
We will lose the least amount of money
by shutting down this machine.
So lets say I'm a steel facility
operating in Upstate New York,
and we have a tenuous power grid, okay.
And this is common.
I actually was an engineer for Nucor Steel for many years
and in the summer time when air conditioning,
when people had all their AC on on 90 plus degree days,
Nucor Steel, because they had an electric arc furnace,
would get a phone call from NYSEG saying
hey listen you have to shut down for
10 or 12 hours tomorrow.
Or you have to get your power consumption
down below X for 10 or 12 hours tomorrow.
Well engineers, individual engineers
would have to make the determination
which pieces of equipment to shut down.
Well invariably we would always decide
to turn off the electric arc furnace
because that was the biggest consumer of power.
But what AI would be able to do
is we could ask AI the question.
We have to get below X tomorrow,
which of our equipment should we shut down?
And based on the information that AI has learned from
which part of our process is the most expensive,
least expensive?
Which ones have the biggest variables?
Which one consumes the most amount of power?
Which are most profitable?
The AI personal assistant has information
the engineer didn't have,
and it can tell us hey wait no
it's not the electric arc furnace
that you should shut down tomorrow,
it turns out you should shut down the stacker
and you should run this product
that only needs to use three
of the stands in the rolling mill
and not 12 stands in the rolling mill.
If you shut down nine of the stands
of the rolling mill you're gonna get
the same power savings.
And if you run product A as opposed to product X,
which requires all 12 stands,
it may spit out a more optimal decision,
which is more profitable for the business.
So that, again, those are examples
of machine learning and AI.
Machine learning is accurately predicting
which face you're looking at,
accurately predicting which voice you're listening to
and what they're saying.
And AI is the personal assistant telling you
which restaurant I should go to.
Making the decision for me.
The optimal decision based on
the accurate information that's been collected
by machine learning.
Okay.
All right those are the examples.