machine learning the IAI machine
learning artificial intelligence machine
learning machine learning or AI machine
learning machine learning AI confusing
right artificial intelligence and
machine learning are often used
interchangeably so they must relate to
each other but how a 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 I
grew up at the time where we were
watching the Jetsons and 2001 a Space
Odyssey where they had how the talking
computer so for me artificial
intelligence is really like a computer
or 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 all of our speech interfaces
for our devices or 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 develop a way of
interacting you can pick up my phone and
I can ask it questions like I can say
Siri say my name your Tom but since
we're friends I get to call you mr
smiley well 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 and
data and try to draw conclusions like
you or I would they're not being
explicitly programmed by people you can
actually give them 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
that's the lifecycle let's ask the
question collect the data train the
algorithm try it out collect feedback
use the feedback to make the algorithm
better so you have increasing accuracy
and performance tada
machine light 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 one of the things that
they're working on is for example the
scanning of tumors so taking x-rays and
taking photographs of when they scan the
outside of the body and they're looking
for tumors and 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 and
if you look at the number of continuous
streaming information from IOT from of
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 so
we can have a chatbot tell us in the
morning well tell me the latest news
okay so it can read the stock market it
can read all the financial numbers that
you need and actually sort not just tell
you the numbers but tell you the story
behind
in a human interpretive away that think
that's really valuable when I think
about what's new and coming up I don't
know if so much mathematics is changing
but the types of things we do there you
have it AI is a science of computers
emulating humans and machine learning is
a 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
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you