Machine learning doesn't mean a machine is actually thinking.
Machine learning has more to do with statistics and pattern recognition than it does about
being a thinking robot that's going to take over the world.
Welcome back to GLITCH, your insights into bots, big data, and artificial intelligence.
So machine learning is becoming more and more prevalent in the apps you use today.
So anything from voice recognition, driverless cars, or recommendations that you get on Netflix,
or Spotify.
Machine learning picks up on patterns in data, and then it makes predictions based on those
patterns.
Let me give you an example.
LEGO man, brick, LEGO man, brick, LEGO man...what do you think will come next?
Brick?
Yup!
You've just looked at a pattern and made a prediction based on that pattern.
A machine learning algorithm would do really well with this as well.
Except it would do a lot better with something that we could not even realize is a pattern.
Lots and lots of data, with lots and lots of different types of LEGO pieces.
It could find a common thread where we couldn't.
So why is it considered a part of artificial intelligence?
Well, it is because Machine Learning learns very similar to the way we do.
Think about children.
A parent reads a book to their child and points out a dog in the book.
this is us teaching a baby what a dog is.
Later mom and child are watching a cartoon, and mom points out a cartoon pup and says
to the baby "hey, that's a dog".
Child and grandparent are walking down the street and they see a dog.
Grandparent says "that's a dog".
Our brains will see a new dog, and even though we've never that breed of dog before we'll
still know that it's a dog.
Why?
Because we've seen enough patterns to make an assumption and a prediction.
With machine learning, we teach machines in a very similar way.
Instead of teaching the machine learning model we call it training.
We train the machine learning model.
So we give the machine learning model thousands and thousands of pictures of dogs.
The machine learning model picks up on patterns in the photos of dogs.
Then when we present it with a new picture, it understands that that too is a dog.
What we don't tell the machine learning model is "look out for two eyes, two ears, a nose,
a tail, long hair, short hair".
That type of teaching for a machine is what we know as an algorithm.
Three things I want you to note about Machine Learning.
First.
Machine learning actually still requires a lot of human effort.
We need human labelled data for the machine to interpret.
So we need to feed it images that humans have identified is in fact a dog.
At the second part of machine learning, we need to see what its outputs are, and we need
to validate if it's right or wrong.
Just like if a baby points at a cat and says "hey this is a dog" we need a human there
to say "no child".
We need humans.
And there's a whole industry around humans labelling data for machine learning.
Like this company, or this company, or this company.
Their whole purpose is to have humans annotate data and say "this is a stop sign, this is
a truck" so that driverless cars can then learn to recognize things on the street.
The second thing to note about machine learning is that machines can be quite biased.
So remember I talked about humans feeding data to the machines?
Well if humans input false or biased data, the machine is going to spit very biased data
out.
Amazon had this problem.
Amazon taught a machine learning model to review all the resumes of people that apply
to amazon jobs.
Problem being, is that humans were very biased in choosing some resumes over others.
So the machine, what happened?
It amplified the bias.
And three.
Last thing I want you to remember about machine learning is that it's still a very nascent
industry.
A lot of companies are taking the lead in machine learning.
Specifically, the ones that have lots of data - because you need data for machine learning.
So this includes, Google, Amazon....but machine learning right now isn't a sentient thinking
machine.
It's just looking at patterns and making predictions.
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Thanks so much.
Take care and see you in the next video.