Deep Learning Cars 

Hey, I’m trying to learn this kinda off machine learning witch course do you recommend?

Please explain how the fitness value of each car was calculated?

@@TuanAnh-mq6sw Each car's fitness value is equal to the percentage of track completion. Since that can't be calculate by simple distance to end point, I placed several "checkpoints" throughout the map. It's pretty straight forward from there.

@@SamuelArzt Thank you. I understand, because i think if fitness value only based the distance, cars has trending to rotate around in their place.

@@SamuelArzt deep learning or just a complex genetic algorithm?

whoever lead turns green

Gr0Us3da4 I know, this is just irony

whooosh?

@@vasco2016 Did you mean joke?

@@johnnyace1086 shut up redditor

Here’s my takeaway: sweet n sour chicken balls with extra sweet chilli sauce, basmati rice and some prawn crackers on the side

@@keir_murray6567 i like your words magic man

The People you have to share the road with are insane

@@kelvinyusuf6658 *of*

@@keir_murray6567 same for me but I don't like prawn crackers

Smashing into walls repeatedly until figuring out how to not smash into walls repeatedly?

Oh Spongebob... Whyyyyyyyy...

Nice. You died 46 times to get a driver license

Galaxy Protector better than me, I died 89 times to get my drivers license

XxNexusxX better than me, i haven't got one yet

And then he died

🎵 dejavu 🎶

Typically because the human programmer can't teach or use logic. Machines are only as smart as their creator.

@@aliensarerealttsa6198 2nd line is untrue. With enough training they'll eventually outperform their creators and the code will no longer recognisable. Ex: RU-vid algorithm.

True, machine learning learn through their mistakes during tests

@@prateekpanwar646 it seem u understand pretty well, I have a question on why don’t the other cars follow the track of the green one

@@hispantrapmusic301 because if the green one dies they all die, they need go as many different ways as possible to have the highest chance of success

Not to ruin the fun but it's just a genetic algorithm bruteforcing all possibilities of the matrix. When and if they had a mind of their own we would have achieved general intelligence... stay tuned

QuickMix wow, really? I thought we were creating and then killing real intelligent species.

Hey, you called them individuals, that means they have their own opinions and that requires intellect... Just sayin'

QuickMix And that was the joke.

I tend to overthink things, pardon my superior neural net.

And this was Cars prequel.

They gained sentience

Creative 😂

Now they pass turing's

Ending is tragic, tho - they've found out they have built a New-Jersey

That's what she said

@@Krawna ur pp = rectangle?

@@goblokidinahui4420 if you censor it

@@goblokidinahui4420 a block head

0:16

lol hehehee

Lmao

DEJA VU

@@citaderu9323 non.

Running in the 90's

Pixels Everywhere that’s what happens when you achieve everything you ever wanted

When you get what you want, but not what you need

"One must imagine Sisyphus happy"

STOP

@@alejandrogarcia-puente6948 wow man real shit so deep bro

Thanks, Destin! Hearing that from you means a lot to me. I really enjoy your videos and have been a fan of your channel for a long time!

@@SamuelArzt it was a great visual. Good work.

@@SamuelArzt Oh! I didn't really realise it was Destin's comment until I read "Been a fan for long time" and then checked. :P

@@johnmctavish1021 yup

Wait why does Destin doesn't even have 100 likes?

The frontmost car always turns green.

Vulcan Viper *whooosh*

Vulcan Viper WOOSH

+Swimming Swampert NOOOO I've always wanted to woosh somebody!!

go onto twitter find some idiot that likes to correct everyone say "go commit die" and bam you got a woosh

They would be able to clear it much faster. If the new track does not introduce any fundamentally new features (such as u-turns or gaps in the walls) they should be able to finish the track right away.

What were you using for the five input nodes? I know they were points, but was it just the distance of these points from the car?

I think they were collision indicators. 5 points ahead of where a collision would happen for reference on guiding.

The five points you are seeing are just the current reading of the five distance sensors of the car. Each car has 5 sensors which measure the distance to the nearest wall. The readings of these sensors are the input of the neural network. The blue crosses are simply there to visualize where the sensors are currently pointing.

Did you use an open source neural network or code your own? I was surprised to see such good results in the first 10 gens. I was expecting it to take longer for even one car to finish the track.

Yes. Yes of course.

Shhhh... don't hurt their feelings.

No, they keep driving on and wondering why it's a wide open world.

it's not wide open, they will find the overflow boarder.

One of them might ask "Hey guys! Do you think this could all just be a simulation?" While the others answer "Pfff... don't be silly!"

Baseer Siddiqui “It’s empty!”

L0L. but, true 😂

Lil U turn first lol

True lol

Without jokes: The A.I is actually unable to detect anything since it only detects walls, so it doesnt know where to go.

This is humans in the future,once machines are doing everything for us.

I don't think so. We'll likely just move on to the next non menial thing. The industrial revolution and automation destroyed _jobs_ not the job market itself, and that era compelled an overall expansion, the AI revolution will probably result in the same. There's more to life than Eating, Copulating, and Working 9 - 5.

I'd be fine with the first too if you add sleep :p

"You pass butter"

@Kerimcan Ak(Sionistas Fuera!) That's humanity's goal as far as I can tell

Thank you for the kind words! That means a lot to me.

Get in there Lewis.

@@XenophonSoulis pls lewis, dont get in there anymore.

@@wwee1r951 It's not like I like Lewis winning, but that phrase is pretty iconic.

@@XenophonSoulis i know man just kidding xD

"where... where is road???"

Thank your for your nice comment! I am actually planning on making more videos explaining neural networks in general for a long time now and I would also like to put the source code of this project on github. Unfortunetaly I am quite busy at the moment, but hopefully I get around doing it next month. So feel free to keep an eye on my channel ;)

Cool, I would love to see it. I'll look forward to it :)

Unfortunately, I think I still won't be able to upload new videos this month... But at least I finally came around to upload the project on github. You can now find a link to the repository containing the entire source code at the top of my website: arztsamuel.github.io/en/projects/unity/deepCars/deepCars.html

Samuel Arzt Thanks so much dude ! I start learning deep learning and it is really cool from you to share it. If you upload explanations videos I will watch them :)

@@SamuelArzt i just see your comment this year, i am new, is your video available sir? thanks

Simple?

Neural networks actually are really simple, but the concept is a bit difficult to grasp. It is basically just trial and error, where each 'node' is a variable that it is trying to maximize or minimize to try to maximize whatever the final expectation is.

it was dancing from happiness

Thank you for your kind comment! That truly means a lot to me. I am glad that my project was able to help you.

Had to drop a like and sub when I seen you gave out the source code 🙌

Under rated comment

Thanks for the kind words!

Not always. I too thought so, but have seen some instances where even slower cars crashed earlier. I think it's an optimized speed that matters.

@@ibknl1986 да он тупую русскую пословицу перевел на англ, не обращай внимания

They will first do 200 iterations on virtual cars and then implement the algo on actual car.

@@zeeshanahmadkhalil8920 just 200 I bet they will simulate 1000+ times with all the possible roads available and traffic then only it can be practical Because if only few accidents happen because of this then then everywhere it will be banned 😂

Order from us more often or we'll crash your house

@@random-0 I think they'd censor the news and try fix the holes in the AI while selling it as usual

lol!

Lmao

Fairytale Overworlds trying to soberize

Are you a Nihylist?

and the "X" are the people..

Hmm I see it more like a bunch of cars thrown on a road until one of them doesn't crash

@@sf8262 F evolution bs tired with these liers

Are you contractor tho?

Some of these cars are just built different ig

Thanks for your in depth comment! You are right that the non-linearity of neural network layers is very important. However you can achieve non-linearity with single layer networks. Kolmogorov famously proposed a theorem in 1965, stating that a neural network with only a single hidden layer comprising enough hidden neurons can approximate any multivariate continuous function. However, many expirements and studies have shown that generally deeper architectures are superior to less deep architectures, as far as their performance and generalization capability is concerned. I did use a squashing function, however I prefer the term activation function. I don't know why you thought I didn't, I'm sorry if I didn't state that clear enough. The network shown in the video (which is an older version) uses the commonly used sigmoid function. After a lot of research I changed the network to use the "softsign" function instead. The softsign function is similar to the hyperbolic tangent, which you mentioned, with some additional advantages. The hyperbolic tanget is also a better function than the sigmoid (at least for this application). If you are interested in the softsign function and its advantages and why the sigmoid function seems unfitting for this particular application, I recommend reading Bengio and Glorot's paper from 2010 called "Understanding the difficulty of training deep feedforward neural networks". It's not that long and I think it is quite interesting. You can find it on Google-Scholar. I don't remember testing it with a single layer, however I recall testing it with one more and one less layer and I did indeed get very different results. However, I have to admit that back then I did not run enough test cases to jump to a clear empirical conclusion.

Same...

TEACH ME WHAT YOU KNOW seriously, you got discord? Good add me Boostio#5047

@Samuel Arzt, I think the huge difference in performance of testing with one more or less layer might be because you use an genetic algorithm for the training. Most of the research focus on back-propagation, not evolution, since the evolution is really slow to converge in comparison to back-propagation. For an evolutionary approach the best "neural network" could possibly be [input] -> [output] without any hidden layer in between, since you still have some weights. This result in fewer parameters to tweak, and the evolution could speed up. For more complex data it might not be possible to solve it using only a single hidden layer (within reasonable time and computational power). Face recognition for example use several hidden convolutional layers, where each layer creates an intermediate representation of the image. The choice of tanh or softsign should not really change the performance anything if you are using evolution for the training. As long as you use a non-linear function you will benefit from having multiple hidden layers.

:p

haha

C mamo :v

NOOOOOOOOOOOOO...THANKS!! FOR RUINING IT!!!!!😒

Seriousity alert: the winning one becomes green.

Of course they do, cars of a new generation are random mutations from best performing car(s) of last generation. The control network is mutated completely randomly, most of the time it does not result in beneficial changes, no matter how many generations you evolve it for.

@@aleksandersuur9475 so just like human beings right?

@@tamjidterrorblade Well sure it's just like stillbirth in mammals. Of course in software the mutation rate is a free choice of the programmer, so it can be set much higher than it naturally is in animals. Simpler GMO techniques for grains and such work much the same, you irradiate your batch as seeds, and sure many of them fail to even sprout, but few specimens get a beneficial mutation. And you really only care about the best performer, the tens of thousands of bottom performers don't matter in such a case, the faster they eliminate themselves from the race the better. It's basically sped up version of normal breeding, in the end you get the same result, but with less generations.

nah they make a path for the bot

Yeah, ovepoupulation will end

I'd say they're learning the track

You mean the philosopher who never wrote anything?

@@edmundironside9435 He never wrote anything himself but his students wrote down his thoughts and lessons

then they would hate diplomacy cause we humans are idiots i think we woul all die then

I knew there would be at least one person who would say or think that.

...I even typed "sper" in Google Chrome search to find comments like that.

that's one way to describe it....

Suddenly i remembered that one game where u r a sperm and trying to race to the egg

Yes, the tracks could be generated procedurally and also yes, there is a chance (a very high one even) that the agents are simply learning this particular track by hard. After all, if you only train them on one track then that's what you want them to do: learn how to navigate this particular course in the best possible way. If you want the agents to generalize to other tracks, if you want them to be able to complete tracks they have never seen before, you have to train them on many different tracks. Otherwhise they get overfitted (or overtrained) on a small amount of tracks (which they become quite good at) but their generalization capability decreases. Still, the cars shown in the video are not overfitted at that point (at least not substantially overfitted). You can even see how the cars, which were able to leave the course, learned to maintain a certain distance from walls, in order to not crash. Of course it could be that this particular distance only works on this track, or that the car only learned to keep a distance from walls to the left of it, etc. But that's exactly why you would then take that neural net and train it on other tracks as well (usually: the more, the better).

I myself did similiar simulation with 8 input neurons with values of distances to walls around the car ...as far as i can tell, there is no way this approach would make agent memorize track. I mean it learns how to steer to balance distances from walls so that none of those gets close to 0 ...there is no reason why that wouldnt be general solution because all that agent learn is rules like : "if there is wall on the left, steer right"

Well this works by giving punishment when they do wrong and an compliment when they do correct It is just human ai

@@allanhanan reinforcement learning you mean?

@@ahsanulhaque4811 yes but its slightly different

@@allanhanan this isnt a reinforcement alg. It says in the description its a evolution alg.

Enter the Jouz better said: how your brain works:))

Naughty boy.

Exactly thought the same xD

Lol

Sperm would just send almost endless cars off the track hoping 1 would finish.. also a few crashed cars would "widen" the track

Very late comment, but this showed up on my stream just today. As opposed to what he content creator is claiming, this network is not deep in the slightest. What you are describing is called over-fitting, but due to the limited amount of information containable in this network that will not be a problem.

*THANKS FOR SKYNET; BASTARD!*

Or... thanks for a nano-scale autonomous probe that can wiggle harmlessly through human intestines to find and remove a dangerous tumor or something.

Why did you make a link that skips the first second?

Haha, didn't even notice that, thanks!

The only Question I really have is: Do these cars really learn how to drive on streets or do they just learn how to drive on this specific street? Like would they be at some point so intelligent that you could give them another street? (after like gen. 1000)

I watched the video and read this comment while listening to The Beat of The Rising Sun

eurobeat but its a midi file

brought to you by Piano Sheet Boss

it would be amazing if someone did an AI like this.

Exactly what I was thinking too! Not sure if anyone else has made something like this..

ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-QtVbch-02Fs.html There's actually a thing like this. It's called roborace.

It's there, but they just left it at gen 5

the topic is deep learning, python is a very good language to start coding it, it is actually the best one for dl and ml, this algorthms are neural networks but with evolutionary algortithms or also genetic algorthms

​@@bernardoolisan1010 Do you think it's easy to encode what's in this video or does it take some effort?