Hello and welcome back to the course on augmented random search.
And today we're going to have an overview of the A.S. algorithm.
In fact we're going to have an overview of what the goal is for that a r s algorithm and this tutorial
is designed to be an intro for those who don't really know much about artificial intelligence or how
these problems work how these benchmarks work how all this is structured.
So if you already are familiar with AI if you've taken artificial intelligence it is that Course then
bear with us as the turtle will dive straight into the important and fun stuff of arrest.
But nevertheless you will probably still find some useful information to.
And if you haven't done any courses before or you're not aware of AI and how it all works then this
tutorial is going to be very very beneficial to lay the foundations of what we're going to be talking
about.
All right so let's get started.
A.S. stands for mentor and research and it is an algorithm that was developed just recently earlier
in 2018.
I believe it was around March 2018 by Horia money and Ereli a guy at the University of Berkeley University.
And what is this algorithm all about.
Well here's a magical figure.
Magical stands for multi joint dynamics with contact.
You may have seen the Google Video or the video of the Google algorithm that controls these musical
figures to successfully train them how to walk and things like that.
That's that's probably where they are famous from.
So Eris also solves the same problem.
And so midget goes like an engine like a physics engine where you have these figures that have different
kind of like landscapes that they need to get across and they have certain degrees of freedom they have
muscles that they can contract and that will allow them to move for instance this humanoid figure has
22 degrees of freedom.
And so the artificial intelligence or the algorithm that you apply to this environment will have control
of 22 degrees of freedom.
And at the same time it will have inputs from the environment and we'll talk about that just in a second.
So for instance in this case the Mujer core humanoid needs to get from where he is now to the end somewhere
there and you know overcomes certain obstacles not just walk in a.
Walking is quite a difficult challenge to master let alone overcoming obstacles.
And just to get to the end of that.
And so a quick note that arrest is not the only algorithm for Mujica.
So when you go is an engine you can apply any algorithm you can apply deep learning you can apply reinforcement
learning and apply A-3 C you can fly a arrays you can apply any kind of algorithm to go.
I can apply machine learning if you like.
That's that's the point.
This is a benchmark test designed to train to facilitate the training of artificial intelligence.
But we will be looking at.
Would you go throughout the theoretical and practical terms because we need some kind of benchmark to
look into things and that's exactly what Israeli money and really a guy looked at as well in their research
paper.
So then moving on for instance here we have another Yuriko example of a figure and its goal is a half
cheetah so you can tell what to half to because it's kind of like two dimensional.
It's not really two image the dimensional but only has two legs.
That's the point.
And so with this hot cheetos you get it like Lance and then it's it's the eyes actually telling the
finger which muscles to contract which ones do you know how to move its muscles in order to move its
joints in order to proceed to progress forward.
And so if we look at any point in time here you'll see that the environment is constantly giving feedback
to the stick figure or to the model.
And that's what is going in as an input for the AI to make the decision.
So for instance you can see that the back leg of the half cheetahs on the ground and therefore there
is certain pressure that it is applied to the ground and therefore the ground is applying the same pressure
as we know from physics from a high school that the ground will be applying the same force back on the
sheet.
And that's exactly what is being inputted into the algorithm that's controlling the cheater right now.
And the same time you can see based on the reflection you can see the reflection of it here that there
is a reflection that it touches the foot but here the reflection ends over here.
So that's where it is.
You see that this foot is in the air and therefore there is no force applied to it.
So it's different in that case and therefore the cheetah also has feedback that there is a forceable
and you will be using this feedback to make a decision then the next job you can see the forces are
different.
Again I just drew them here approximately may not be exactly correct but the point stands at different
points in time.
The environment is giving different feedback and that shit is using that to learn how to walk as we
humans do as well.
Like if you think about how we walk we're actually falling forward and then we're putting our back foot
in front to prevent us from falling.
And that gives us that forward perpetual movement that we get.
So similar thing that cheater's is going to figure it out.
And the point here is that the cheetah or the humanoid or any other mortal is not preprogrammed.
So the algorithm doesn't know how to walk in advance.
We're not teaching them how.
We're not saying you have to put this leg forward in this leg and at this point this leg and so on.
It has to figure out all this on its own kind of like a baby when a baby learns to walk.
You don't give it an instruction manual or you don't you know sit down there and teach you how to walk
and explain you can explain anything to me because it doesn't are Tokyo.
So and things so it just can't.
It can only do it through trial and error.
Same thing here.
Through trial and error from multiple generations of this cheater.
So what are we seeing here.
This running This is after a lot of training at the start is going to follow is going to experiment
is going to try to get around a little bit then for longer and so on.
But eventually they'll get there.
Same thing as with a baby that will keep falling falling falling and then eventually you learn at all.
So this result is actually a trained result.
So that's the point that these models don't get preprogramed.
They have to learn through trial and error.
And so what we get is for now show we'll talk about the whole trial and error situation.
But further down in this course for now what is important for us to understand is that there are certain
inputs that come from the environment and they go into the AI model for now.
For us it's like a black box.
We don't know what happened but we do know that what comes out is our outputs outputs on how to contract
or how to move certain muscles and joints and you know whatever degrees of freedom are available to
the model how to control the model in order for it to achieve the goal that we want.
So that's an overview of how the our air system or any other artificial intelligence works.
In general you have input something happens in his black box he has I have outputs and then the model
actually behaves and we'll explore this further in the course.
For now if you'd like to get a quick head start into the world of Erris.
There's a great blog post by Bednar or act who is the supervisor of our aliar guy and Horia mania.
So we're not going to jump into their paper right away but you can start with his blog post which will
give you a head start an overview of what we're going to be talking about the blog post is called clues
for which I search and choose and it's available on Armyn which I believe is Ben's Web site.
This link will be available in the course notes.
So there we go.
That's how we're going to end today's tutorial with this wonderful blog post.
I personally enjoyed it.
Hope you enjoyed today's Tauriel I know you have a bit of a better overview of these benchmarks and
how AI in general is structured or like the purpose of AI in this case with the Mujica algorithm and
starting from literally going to dive deeper and deeper into the world of Erris and of that to look
for it see you back here next time.
Until then enjoy.