Machine Learning
AI
Machine Learning
Artificial Intelligence
Machine Learning
Machine Learning or AI
Machine Learning
AI
Confusing, right?
Artificial intelligence and machine learning
are often used interchangeably.
So they must relate to each other.
But how?
AI or artificial intelligence is the science
of training machines to perform human tasks.
The term was invented in the 1950s
when scientists began exploring how computers could
solve problems on their own.
Most of the time when you hear AI, you probably
see these ridiculous images.
ANDY RAVENNA: I grew up at the time
when we were watching "The Jetsons" and "2001:
A Space Odyssey" where they had HAL the talking computer.
So for me Artificial Intelligence
is really like a computer or a machines
that are given properties, human-like properties.
We take for granted how our brains effortlessly
calculate the world around us, every second of every day.
AI is the concept that a computer can do the same.
BOB ROGERS: All of our speech interfaces for our devices
are AI.
And it's incredible.
You can have an accent.
You can be speaking a particular dialect
that, as long as there's data on the Internet
with that language, these AI systems can quickly
developed a way of interacting.
TOM SABO: I can pick up my phone and I
can ask it questions like I can say, "Siri say my name."
SIRI: You're Tom.
But since we're friends I get to call you M-R Smiley.
While AI is the broad science of mimicking human abilities,
machine learning is a specific subset of AI
that trains a machine how to learn.
Machine learning models look for patterns in data
and try to draw conclusions like you or I would.
BOB ROGERS: They're not being explicitly programmed
by people.
You can actually give some examples
and they're going to learn what to do from those examples.
That's a huge difference because it's much easier for us humans
to give examples than it is for us to write code.
Once the algorithm gets really good at drawing
the right conclusions, it applies that knowledge
to new sets of data.
BOB ROGERS: That's the lifecycle.
It's ask the question, collect the data, train the algorithm,
try it out, collect the feedback,
use the feedback to make the algorithm better so that you
have increasing accuracy and performance.
Ta-Da!
Machine Learning.
LISA LOFTIS: If you look at the Google car,
it has lasers on the top which are telling it
where it is in terms of the surrounding area.
It has radar in the front, which is
informing the car of the speed and motion of all
the cars around it.
And it uses all of that data to figure out
not only how to drive the car but also to figure
out and predict what potential drivers around the car
are going to do.
And that's almost a gigabyte a second of data
that that car is processing.
ANDY RAVENNA: One of the things that they're working on
is, for example, the scanning of tumors.
Can you just imagine one day where they could actually
do a scan and determine whether a tumor is benign or not
instead of having to go in and take a sample every time
they're trying to figure out what's
going on inside your body?
It's very Star Trek, right?
LISA LOFTIS: And if you look at the number
of continuous streaming information
from IoT, from the beacons and sensors
it gives us the ability to understand our environment
much more intimately than we ever could before.
And that's what AI and ML need.
They need granular data.
They need very large volumes of data.
And they need extremely diverse data sources
to be able to find the patterns and learn.
KIRK BORNE: So if we can have a chatbot tell us in the morning,
tell me the latest news.
Okay.
So it can read the stock market.
It can read all the financial numbers that we need
and actually sort of not just tell you the numbers
but tell you the story behind the numbers in a
in a human interpretable way.
I think that's really valuable.
When I think about what's new and coming up
I don't know if so much the mathematics is changing
but the types of things we do.
There you have it.
AI is the science of computers emulating humans
and machine learning is the method behind how
machines learn from data.
There are so many problems to solve based on data.
Why not let an algorithm take care of a few?