Hi! I'm Dion. I'm one of the creators of Forethought AI. At Forethought,
we build artificially intelligent tools that people can use at work to be more productive.
To make a learning machine, early computer scientists looked for clues by studying
other things that are good at learning, and it turns out that nothing is better
at learning than the human brain! Our brains are made up of special cells called neurons.
A neuron has two ends: input signals enter in on one end,
they're combined together inside the neuron, and leave out the other end as a single output.
All of the billions of neurons in your brain are connected to each other, in what's called
a biological neural network. It's how your brain processes information and recognizes patterns.
Early AI scientists decided to mimic human neurons by making their own simple artificial neurons in
software. Nothing fancy, just multiple signals going in as inputs, passing through the neuron,
and getting combined and processed by some simple math into a new signal going out.
It's a good start, but one neuron alone doesn't do much.
The full potential of this idea is only unleashed when the artificial neurons
are connected together to make an artificial neural network. This is what allows computers to
recognize images, drive cars, and make some truly weird art.
To see how a neuron works, let's build a movie recommendation system, that uses critics reviews
to guess how much you'll like a movie. Then, we'll use your feedback to make the system better!
Here are three movie critics: Ali, Bowie, and Casey. Each one rates a movie anywhere
from one to five stars. Now, let's build a single artificial neuron.
Each of the critics ratings enters on this side as input, some calculations are done in here,
and we get a single output. In this case, it's a movie rating.
Here's the first movie. Ali gives it one star, Bowie gives it five, and Casey gives it a four
star review. At first, the critics opinions all carry the same weight, and are counted equally.
The inputs enter, there's some basic math, and out comes a recommendation.
Now, let's watch the movie so we can give it our own rating!
Uh, okay. That was weird! Let's let's pretend you really liked it, and gave it a five star rating.
The rating you just provided is now used to train the neuron. Based on your rating, the
weight of each critic's opinion is recalculated. Your rating is closer to that of Bowie and Casey,
so their opinions get more weight. You didn't agree with Ali's single star review,
so that weight goes down. Now let's train the neuron again.
Here's another movie, and here are new ratings from our critics. And this time,
the neuron will give more weight to these two ratings when calculating its recommendation.
And here's the output! Now let's give it a watch.
Well, at least that was short! Let's give it a rating.
Our new rating adjusts the weights again. This process repeats over and over, until
we've trained a system to know our preferences, and recommend movies that we'll probably enjoy.
In this example, there's just one neuron. That's far more simplistic than most systems.
Powerful neural networks have millions of neurons arranged in layers.
There are input layers, any number of hidden layers, and output layers.
The output of one layer of neurons, becomes the input to the next layer, and so on.
Many real world media music and shopping recommendation systems work like this,
using ratings for millions of everyday users
in those neural networks. Everyone has a hand in modifying the weights.
Neural networks have so many other uses. They're working behind the scenes on big
problems, like growing healthier food, predicting floods and forest fires,
aiding wildlife conservation, and even detecting and curing disease.