Hello and welcome back to the course on augmented random search.
In today's world we're going to talk about the perception and we'll find out exactly how it works and
what it is.
So in the various tutorial this is where we left off.
So let's quickly recap the environment such as the magical environment will give us certain inputs.
For example the forces that are applied to the feet of the model then artificial intelligence comes
in at that point it needs to analyze the inputs.
There might be three of them might be 20 maybe 100 inputs need to analyze needs to analyze the inputs
and perform or output certain decisions on what is is it going to do is it going to tell the model to
contracture muscles it is going to tell it to lift this leg and move this right finger or foot this
way and so on and then it will have certain outputs that will control them.
Again that can be sort of output.
For instance would Jako humanoid model has 22 degrees of freedom.
Then we have the outputs the outputs go back into the environment and they control the model or the
agent in its quest in order to walk successfully or run or whatever it's doing.
So that's overall the structure what hides behind this like box of a work.
What is it.
Well this is where a person perception comes in.
So this is our perception that we hear in the air as we use a simple perception which is basically just
taking these inputs and applying certain weights to them and then adding them up.
So as you can see in this case our output will be the sum of x 1 times w 1 plus x 2 times W2 plus x
three times three.
That's what the sum symbol means.
And we add them up at those Wadewitz get the weighted some of the R inputs and that's our output.
So our inputs and all of those forces all the whole stack situation in the environment can be described
if a vector of inputs is just a vector of numbers and our output is for instance this output might be
connected to the the arm of the model to the right arm.
And so this is what we're going to do with the right arm is basically simply a sum of the weighted inputs.
That's all it is and that's what a person perception is.
And now we're going to we're going to remove these labels are here so that it's not distracting for
us.
Now that's a very simple single output for perception.
Now we're going to increase the complexity a bit.
For instance here we've got a perception with two outputs but the concept still remains the same.
In each case we will have certain weights.
Now they have to be longer of an index so that we know which way applies where in this case the three
weights apply here.
So it's the sum of x X-1 times w 1 1 plus x 2 times w 2 1 plus x 3 times w 3 1.
So the first index means which input this weight is related to the second index means which output it's
related to and for the second output we're going to have a different set of weights.
1 2 2 2 and 3 2 and they're all going to come into this output.
And there we go that's our output.
So for instance this could be the left arm this could be the right arm or the left foot right foot so
basically it will be telling based on this number.
This number will be interpreted by the environment and also want our AI.
So this is our AI.
Basically what our AI once the model to do is it wanted to put the left foot forward with a foot back.
And of course this can get more complex.
You can see here that we can have more inputs.
We can have more outputs outputs can number about can be greater the number of inputs it doesn't really
matter.
And so here again we're going to have the sum of weights the weighted sum of all of these inputs of
course the weights are going to be unique.
So as you can see the number of weights is growing quite fast here.
So in this case we have for each one there are four of these.
Verify these for each one we have for each one of these.
We have five weights.
We have 20 weights in total.
That's why instead of writing them out separately we're writing them out as a matrix as matrix as you
can see their road around out here.
So there are a weights and Basen index you can tell.
So for instance W 2 3 is the way that will be applied to in part number two in the calculation of our
number three in the some of that has been calculated so for in order to calculate output number three
we're going to have to use the column of weights.
So w want 3 will be applied to this there'll be two three applied to this though with this double for
three to this all that will be added up and we'll get the output for this for this output which might
mean something specific which is something specific for the world.
So that's basically how a percent perceptual perception works takes the inputs applies a weighted sum
to get each one of the outputs.
And there's nothing more complex than that.
And that's the beauty of this algorithm there is that there is no this is actually called shallow learning
because we don't have any hidden layers is only an input and output layer.
And because of that it is say it's a sham because it's a shallow learning algorithm.
It's much faster and yet it's so simple and still get some great results as we will discuss further.
And I normally don't go into code that's a LUNs area of expertise but I just wanted to give you a heads
up.
So this is the code that you will see in the practical tutorials and I wanted to show you exactly where
the perception is here because you kind of like miss it in the code and then like look through it.
We don't have a specific name.
Overclass for Perceptor.
But it's really interesting to kind of like search for it on your own and look for it.
When I was watching unflustered Rose I really had fun like relating back to the theory of ERs and understanding
where each component is and where the perception is here.
It's hiding in this line.
So this is the perception.
So this what we just created here which we just describe.
That's our perception.
So that's the inputs.
That's the matrix multiplication and that's the way it's all of the matrix and then return means that
it is outputting the results of that output.
And so what is made why is Matrix matrix multiplication here.
Well because if we go back what we can see here is we've got a vector of inputs.
Then we've got a matrix of weights and then we're going to make a way a vector of outputs.
And so in order in mathematical terms or in programming it's simply a matrix multiplication of this
times this matrix of this vector Tam's matrix to get in order to get this vector of outputs.
And so that is the perception right over there.
So even though it's like it looks like a complex construct when we draw it in reality is just one line
of code so don't miss it.
It's like 53 when you're actually building the AI.
We've Adlon in the practical tutorials.
All right.
On that note we're going to wrap up for today.
That's what the perception is very a very simple concept.
But as you'll see is going to yield some amazing results and you'll actually code this yourself if you
do the practical tural you called this yourself and you'll see how much power the single perception
has in the context of Arison is important.
Understand that Eris is not just the perception it's all it's got some additional things additional
features that we'll discuss as well.
But in the context of Eris a perception turns out to be extremely powerful in solving artificial intelligence
challenges.
On that note I hope you enjoy today's tutorial.
I look forward to seeing you next step.
Until then enjoy I.