Hi, I’m Jabril, and welcome to CrashCourse AI!
In the supervised learning episode, we taught John Green-bot to learn using a perceptron,
a program that imitates one neuron.
But our brains make decisions with 100 billion neurons, which have trillions of connections
between them!
We can actually do a lot more with AI if we connect a bunch of perceptrons together, to
create what’s called an artificial neural network.
Neural networks are better than other methods for certain tasks like, image recognition.
The secret to their success is their hidden layers, and they’re mathematically very
elegant.
Both of these reasons are why neural networks are one of the most dominant machine learning
technologies used today.
[INTRO]
Not that long ago, a big challenge in AI was real-world image recognition, like recognizing
a dog from a cat, and a car from a plane from a boat.
Even though we do it every day, it’s really hard for computers.
That’s because computers are good at literal comparisons, like matching 0s and 1s, one
at a time.
It’s easy for a computer to tell that these images are the same by matching the pixels.
But before AI, a computer couldn’t tell that these images are of the same dog, and
had no hope of telling that all of these different images are dogs.
So, a professor named Fei-Fei Li and a group of other machine learning and computer vision
researchers wanted to help the research community develop AI that could recognize images.
The first step was to create a huge public dataset of labeled real-world photos.
That way, computer scientists around the world could come up with and test different algorithms.
They called this dataset ImageNet.
It has 3.2 million labeled images, sorted into 5,247 nested categories of nouns.
Like for example, the “dog” label is nested under “domestic animal,” which is nested
under “animal.”
Humans are the best at reliably labeling data.
But if one person did all this labeling, taking 10 seconds per label, without any sleep or
snack breaks, it would take them over a year!
So ImageNet used crowd-sourcing and leveraged the power of the Internet to cheaply spread
the work between thousands of people.
Once the data was in place, the researchers started an annual competition in 2010 to get
people to contribute their best solutions to image recognition.
Enter Alex Krizhevsky, who was a graduate student at the University of Toronto.
In 2012, he decided to apply a neural network to ImageNet, even though similar solutions
hadn’t been successful in the past.
His neural network, called AlexNet, had a couple of innovations that set it apart.
He used a lot of hidden layers, which we’ll get to in a minute.
He also used faster computation hardware to handle all the math that neural networks do.
AlexNet outperformed the next best approaches by over 10%.
It only got 3 out of every 20 images wrong.
In grade terms, it was getting a solid B while other techniques were scraping by with a low
C.
Since 2012, neural network solutions have taken over the annual competition, and the
results keep getting better and better.
Plus, AlexNet sparked an explosion of research into neural networks, which we started to
apply to lots of things beyond image recognition.
To understand how neural networks can be used for these classification problems, we have
to understand their architecture first.
All neural networks are made up of an input layer, an output layer, and any number of
hidden layers in between.
There are many different arrangements but we’ll use the classic multi-layer perceptron
as an example.
The input layer is where the neural network receives data represented as numbers.
Each input neuron represents a single feature, which is some characteristic of the data.
Features are straightforward if you’re talking about something that’s already a number,
like grams of sugar in a donut.
But, really, just about anything can be converted to a number.
Sounds can be represented as the amplitudes of the sound wave.
So each feature would have a number that represents the amplitude at a moment in time.
Words in a paragraph can be represented by how many times each word appears.
So each feature would have the frequency of one word.
Or, if we’re trying to label an image of a dog, each feature would represent information
about a pixel.
So for a grayscale image, each feature would have a number representing how bright a pixel
is.
But for a color image, we can represent each pixel with three numbers: the amount of red,
green, and blue, which can be combined to make any color on your computer screen.
Once the features have data, each one sends its number to every neuron in the next layer,
called the hidden layer.
Then, each hidden layer neuron mathematically combines all the numbers it gets.
The goal is to measure whether the input data has certain components.
For an image recognition problem, these components may be a certain color in the center, a curve
near the top, or even whether the image contains eyes, ears, or fur.
Instead of answering yes or no, like the simple Perceptron from the previous episode, each
neuron in the hidden layer does some slightly more complicated math and outputs a number.
And then, each neuron sends its number to every neuron in the next layer, which could
be another hidden layer or the output layer.
The output layer is where the final hidden layer outputs are mathematically combined
to answer the problem.
So, let’s say we’re just trying to label an image as a dog.
We might have a single output neuron representing a single answer - that the image is of a dog
or not.
But if there are many answers, like for example if we’re labeling a bunch of images, we’ll
need a lot of output neurons.
Each output neuron will correspond to the probability for each label -- like for example,
dog, car, spaghetti, and more.
And then we can pick the answer with the highest probability.
The key to neural networks -- and really all of AI -- is math.
And I get it.
A neural network kind of seems like a black box that does math and spits out an answer.
I mean, those middle layers are even called hidden layers!
But we can understand the gist of what’s happening by working through an example.
Oh John Green Bot?
Let’s give John Green-bot a program with a neural network that’s been trained to
recognize a dog in a grayscale photo.
When we show him this photo first, every feature will contain a number between 0 and 1 corresponding
to the brightness of one pixel.
And it’ll pass this information to the hidden layer.
Now, let’s focus on one hidden layer neuron.
Since the neural network is already trained, this neuron has a mathematical formula to
look for a particular component in the image, like a specific curve in the center.
The curve at the top of the nose.
If this neuron is focused on this specific shape and spot, it may not really care what’s
happening everywhere else.
So it would multiply or weigh the pixel values from most of those features by 0 or close
to 0.
Because it’s looking for bright pixels here, it would multiply these pixel values by a
positive weight.
But this curve is also defined by a darker part below.
So the neuron would multiply these pixel values by a negative weight.
This hidden neuron will add all the weighted pixel values from the input neurons and squish
the result so that it’s between 0 and 1.
The final number basically represents the guess of this neuron thinking that a specific
curve, aka a dog nose, appeared in the image.
Other hidden neurons are looking for other components, like for example, a different
curve in another part of the image , or a fuzzy texture.
When all of these neurons pass their estimates onto the next hidden layer, those neurons
may be trained to look for more complex components.
Like, one hidden neuron may check whether there’s a shape that might be a dog nose.
It probably doesn’t care about data from previous layers that looked for furry textures,
so it weights those by 0 or close to 0.
But it may really care about neurons that looked for the “top of the nose” and “bottom
of the nose” and “nostrils”.
It weights those by large positive numbers.
Again, it would add up all the weighted values from the previous layer neurons, squish the
value to be between 0 and 1, and pass this to the next layer.
That’s the gist of the math, but we’re simplifying a bit.
It’s important to know that neural networks don’t actually understand ideas like “nose”
or “eyelid.”
Each neuron is doing a calculation on the data it’s given and just flagging specific
patterns of light and dark.
After a few more hidden layers, we reach the output layer with one neuron!
So after one more weighted addition of the previous layer’s data, which happens in
the output neuron, the network should have a good estimate if this image is a dog.
Which means, John Green-bot should have a decision.
John Green-bot: Output neuron value: 0.93.
Probability that this is a dog: 93%!
Hey John Green Bot nice job!
Thinking about how a neural network would process just one image makes it clearer why
AI needs fast computers.
Like I mentioned before, each pixel in a color image will be represented by 3 numbers --- how
much red, green, and blue it has.
So to process a 1000 by 1000 pixel image, which in comparison is a small 3 by 3 inch
photo, a neural network needs to look at 3 million features!
AlexNet needed more than 60 million neurons to achieve this, which is a ton of math and
could take a lot of time to compute.
Which is something we should keep in mind when designing neural networks to solve problems.
People are really excited about using deeper neural networks, which are networks with more
hidden layers, to do deep learning.
Deep networks can combine input data in more complex ways to look for more complex components,
and solve trickier problems.
But we can’t make all networks like a billion layers deep, because more hidden layers means
more math which again would mean that we need faster computers.
Plus, as a network get deeper, it gets harder for us to make sense of why it’s giving
the answers it does.
Each neuron in the first hidden layer is looking for some specific component of the input data.
But in deeper layers, those components get more abstract from how humans would describe
the same data.
Now, this may not seem like a big deal, but if a neural network was used to deny our loan
request for example, we’d want to know why.
Which features made the difference?
How were they weighed towards the final answer?
In many countries, we have the legal right to understand why these kinds of decisions
were made.
And neural networks are being used to make more and more decisions about our lives.
Most banks for example use neural networks to detect and prevent fraud.
Many cancer tests, like the Pap test for cervical cancer, use a neural network to look at an
image of cells under a microscope, and decide whether there’s a risk of cancer.
And neural networks are how Alexa understands what song you’re asking her to play and
how Facebook suggests tags for our photos.
Understanding how all this happens is really important to being a human in the world right
now, whether or not you want to build your own neural network.
So this was a lot of big-picture stuff, but the program we gave John Green-bot had already
been trained to recognize dogs.
The neurons already had algorithms that weighted inputs.
Next time, we’ll talk about the learning process used by neural networks to get to
the right weights for every neuron, and why they need so much data to work well.
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And if you want to learn more about the math behind neural networks, check out this video
from Crash Course Statistics about them.