🖥️ FINISHING MY FIRST MACHINE LEARNING GAME! (3/4) 

9:10 Did the audio desync in the editing phase, since it is the same audioclip from the beginning of the video? :s Then it cuts to music mid-word at 11:48

Jabril your content is extremely high quality. You make computer science interesting to a guy like me, who doesn't know anything about it

you should make another ai to automatically build harder courses any time forest completes them.

Far and away the best , most in-depth (and understandable to a layman enthusiast) insight into an ML programmer's thought process that I've seen on RU-vid. Thanks!

Watching Forrest trying to run the different courses has been surprisingly mesmerizing. I'm gonna miss the little guy

I absolutely loved this series. Thanks for making it buddy. I'm glad I'm not the only one who finds this topic fascinating.

Just realized I couldn't see the comment option because window was too small XD. These vidz are so dope. Clarity, content, reasoning, animation, editing, acting, music choice, dragonball reference, and overall entertainment value are 100. Thanks.

Cheers for making this - it was a good watch! I've played with machine learning and haven't had the chance to delve into deep learning yet but this series helped visualise it better :) Keep up the good work!

Really cool short series here. What's quite awesome is thinking about about additional methods/ways that could have been implemented to complete and make it even more efficient. For example, adding normals analysis onto the raycasts that determines the scores on the 5 directions. Getting the normals returns the directions from which the 3D faces (walls) are facing. That's, after all, the principle behind how we're able to follow a track ourselves : We see the distances and directions from restriction we're facing. Even someone who's blind uses the same principle by using his ears to analyse the direction of hard surfaces and the echos. (sounds doesn't return the same if it bounce at 90 degrees off a surface or less.)

Wow, that race really had me on the edge of my seat!

I'm writing this out before watching the post-mortem, so forgive me if I'm saying things you already went over... I think there are a couple reasons the results weren't as good as they could have been. First, it seems like the turn radius isn't sharp enough to turn around. So when Forrest runs up to a confusing intersection he hasn't seen before, there is no way to guess whether it goes left or right. He just has to pick one, and if it's a dead-end, that simulation loses because he won't be able to turn around. However, that simulation didn't lose because it's genetically unfit -- it only lost because it was genetically predisposed to guess left at the confusing intersection. This means the automatic assumption by the computer is that he was genetically unfit for any race in general, but really he was just guessing that the intersection turned left. In fact, that could have been the best Gump yet, but lost because he rolled the dice wrong. Second, piggybacking off that first point, the A.I. may just be training by memorizing the track. Who knows if it's actually learning how to navigate new areas? All it knows is the specific track. Every time you train it on a new track, it is going to fail in the beginning and then have to readjust to the new track as it memorizes the new one and forgets the old. Ideally, I think, the best way to train it would be to 1) Give it a sharp turn radius, and 2) Randomly generate a new track for every generation. That way, it has to rely on its ability to adapt 100% and not at all on its ability to remember tracks. Of course, then it would be hard to reward it properly for advancing in the track so 3) You'd have to give it an incentive for getting closer to the end goal... maybe draw an A* path to the end and make the distance inverse to the score.

Your Chanel got randonly suggested to me by youtube, and I was glad it was. Very entertaining to watch, and you did a vey good job at explaining the concepts behind the algorithms. A Well-deserve subscription!

Your level of mental energy is out of this world!! I have no words, I'm a fan now!!

I think the random slice works best because it is not limited by the past results, and potentially adds new arguments that could totally work! I loved this video, you have gotten me excited about machine learning! I'm going to talk to my academic adviser about switching my major!

I greatly underestimated how entertaining this series would be, awesome! subscribed and know to check all your other content.

This is genius work. Pure knowledge. You should be writing a paper on this for a game-dev conference!!!

I immediately thought of a GAN. Another network builds harder and harder courses for Forrest to try. That would be a super cool project. Lately I wonder if my life is like that. Reality is presenting me more complex problems to solve. Thank you universe for keeping me challenged !!!

Just saw this series of videos on Machine learning. Very interesting and i'm enjoying them quite a bit. Just wanted to note that I think the Random % learning selection is actually the best way to mimic life.

Dude i am NOT able to do anything with programming, somehow i came across your channel, saw this series, and must say i really really like your way of doing this. Informative and funny. keep it up!

At first I thought your videos were silly. I am a fan now. Subbed 100%. I hope you grow because you're super entertaining. Good luck!

Soooo could this be applied to my lawn mower so it'll cut my grass for me?!

lol it's actually strange to see him at the end of the video actually talking haha ... good job man, nice videos

Found this video (and your channel) from a tweet of another programming youtuber and I absolutely love it keep up the awesome work man!

Great video! Just one comment about the graphs. Top fitness charts should have no dips, at no point in time we should lose fitness. In order to prevent this we usually use elitism which basically means that we carry on to the next generation an untouched copy of the most fit solution of the current generation. After that you can follow the same selection, corssover and mutation methods. And finally I'd like to ask: What library do you use for machine learning + Unity? Cheers and keep up the awesome content!

Nature easily marries with your data suggesting processes with randomized values are the most efficient methods of learning. Consider that genetic mutation is practically random - it's not that a species will intelligently adapt to environmental factors, but rather those which develop advantageous traits due to random genetic mutations are more likely to survive and thus pass on the new characteristics. Conceptually it's near identical to your randomized processes recieving priority values, favoring efficient processes and passing the data to the next function. It is interesting all the same that these processes would be so much more efficient when compared to processes that had intelligent guidance.

The link to part 2 in the description actually links to part 1. Also it would be nice if the description had the link to the next video. Or make video end cards, because RU-vid doesn't always recommend the next video and people are not always watching the playlist. That increases views and watch time and retention.

sooooo good, I love how you set up these videos as entertaining and easy to follow along. heck honestly im not even doing this, but now i kinda want to. Im still going to watch the whole series today and i've subscribed. Thank you for peaking my interest again, this coming from a college student going for a Computer Science Degree.

Dude honestly, if you don't continue to make educational videos, it will be a crime. You are amazing. Thank you

Hey Jabrils, Thank you for this great content. This is my fav channel now on :)

I love your videos. First off, you get to the facts in a really fun an interesting way. Your have such a great character (no pun intended, Forrest), and overall your a really cool guy. I give you my subscribe.

6:44 dat explanation of random crossover function, lolol.

Just came across your channel. Have to say that you have an interesting style of editing. Keep up the great work!

Definitely my new favorite channel. You're hilarious.

Man I absolutely love this channel to bits!

dont know if you mentioned it but an age old strategy to visit a labyrinth ( which i used in Wolfenstein ) is to go forth, keeping the right ( or left ) hand on the wall

6:55 that is some risky stuff my friend, and I like that way of thinking

I believe that even if I were not already interested on the subject, I would be after watching you dude. Loved it!

BY FAR THE BEST IVE FOUND FOR GETTING INTO IT THE BEST

I love the progress that you made with this project. I have a few helpful suggestions that may work to help you. First, I think you would need to train Forest to run both ways. Second, you will have to store the information of what forest learnt (in a database of some type) from previous course that he ran so he can apply the knowledge that he learnt for his new course that he's about to run. Hope you see my logic.

What software do you use to animate your videos? p.s. this is a sick video series!

Isn't a touch of randomness in the gene pool how the crossover method actually works in real life? I'm talking about DNA damaging/altering factors from outside of the reproductive process. Things like: cosmic ionizing radiation, and certain kinds of bacteria and viruses that can write changes into another organism's DNA, which sometimes get passed on to descendants. In fact, a much higher intensity version of this mechanism has been used to “genetically engineer” new strains of cultivated life, like plant crops. I think that adding in some randomness to the crossover method is a good route to take, if you want to avoid stagnation. Although, I could have sworn you said in an earlier video that you were actually going to do this. Maybe you just left it to be implied in all future references to using the crossover method; depending on the viewers to remember the previous videos in the series. (Pretty reasonable to expect the viewers to remember stuff, I think, by the way.)

When it comes to crossover, if crossover is performed on the whole population it will effectively make them all "the same species". Every time two networks with different strategies are mated, it is almost certainly going to result in failure (nonviable offspring), so the resulting "species" all work the same so their pieces are interchangeable with slight variation. That is highly consistent with what you are seeing. I see a lot more success in simulations I've ran when I allow speciation to happen if I am using crossover or if I allow species to have "crossover points" that allow them to crossover at some points if it matches with the one being mated with. However, in your case it seems that since there is just one solution to this problem that only one species was required anyways, so it made more sense to just make the whole population quickly be that.

one likely reason for natures adaptation of the slice crossover is that nucleic acids aren't single input values, they come in triplets called codones that correspond with amino acids for protein building. Because of the length of polypeptides, switching a single nucleic acid could have monumental consequences, like accidentally making tons of stop codones which says to stop the polypeptide construction after tens of amino acids for proteins that would normally be hundreds or thousands long

What program did you use to plot the graphs. Doesn't look like excel, but maybe I just don't know how to make it look as good as you do^^?

Hey, Whats your machine specs? Looks like you are using Dell XPS 15" inch for the simulations?

Really loving this series so far, really informative and funny too.

I am super enjoying this. Awesome stuff! (I did a BSc in artificial intelligence 25 years ago - I wish I had cool tools like these to play with!)

WTF 1500?! Your channel is highly underrated. You get all of my views. Thank you for vids, I love 'em

I believe you said the random/probability method worked out the best with training. Did you try this method multiple times and was it typically the best? If so have you considered seeding a random generator to keep the data consistent and tested it against other seeded random generators to see how they compared. Very cool series and channel.

Hi. I'm interested in what programming software you used as I'm planning on making a neural network for my A-Level Project and I can't think of anything better than C# or python (python I've never used before). Thanks

6:53 I removed my upvote just to give it again.

this man has the hand of god, I'm now blessed with machine learning.

I am loving this forest machine learning series. By the way what resources did you use to learn ML. Have you completed any Andrew Ngs coursera courses? Also have you considered making a healthy eating or fitness app? Taking a step towards combatting obesity with AI\ML sounds like a good project. I wonder if there is some data base of foods and their calories. If so than the app could be trained so when you take a picture it approximates calorie of food.

I'd love to see Forrest run on a course that has some dead ends, and also some that have shortcuts 😊

Confusing intersections.. bad idea... because your network just has local inputs, he can't remember which intersection he took last etc... the only "memory" he has, is the direction he's looking in right now which is determined by where he came from. So your networks can't actually learn for generic "complicated intersections", but just specialize on the courses you offer it by accident. The guy needs some kind of memory or just courses without branching

Way to call the election.

Could you change the probability method halfway through? If you start with the random one and get a long high then a big drop, could you take the series of high ranked numbers and use the slice on them?

Hey Jabril! Awesome videos man. Keep up the good work!

This series is so awesome. Do you think I'd be able to implement a similar environment for my end of the year project to build a robot that can get through any cardboard maze?

Your style is so unique! I love it

Dude, first time watching your youtube. Hilarious brother. Thank you 😂

Hey, question. What would happen if you put logical bounds on the output of the neural network. Like if you're about to be too close to a wall to avoid a collision, override the output and turn to avoid the collision, then give control back to the ANN when you're out of harms way? A hybrid approach. Use the neural network for navigation, but have an emergency hard-logic fallback for imminent collisions? Let me know what you think. Overall, great series. I subbed. Well worth watching, will recommend to friends.

See how you get around the whole decision making for random or precise functions is using your random function as a optimization side loader that runs a real-time micro SIM of the scenario going on using its top performing data and communicating it back to the precise function that operates the main character or scenario. And what you'll see in the graph is an exponential rise in performance basically you're giving me AI the ability to self-simulate and communicate the best data to the motor control function

What computer hardware are you using?

That ff7 music though

Dude this series is amazing, would love to see some more stuff like this. also liked for ff7 music

How did you make the hex courses? Is it in unity? Or something else?

I love this, thank you for sharing your knowledge and making such awsome content. This really motivates me to learn about machine learning.

Just a quick question how did you teach yourself machine learning? Did you use any online courses or books?

When you do crossover, you should have a random chance of mutating the output, as this does happen in nature, maybe with that it could beat the random splicing approach :)

what if you make/add a lot of forrests run in one go... like a competition... Will it make training faster? I know they also take computational power, But, will it be more efficient?

Also whos to say that slicing this away is used in nature? It doesn't actually matter which weights / biases are selected, because the weights and biases can be arranged any way you really want to.

you really inspire people to learn machine learning

I have a suggestion: voice synthesis. Like from the movie Her. You type in text and the Ai reads what you type. Basically, to improve the english of Forrest. Inputs can be length of time spent on different letters, number of syllables in each word, when to rise and lower tone, and minor tweaks based on if sentence is a question or statement.

Can you explain why you chose such an architecture of the ANN? I mean I'd consider trying adding an extra hidden layer for complexity.

I love this, I never thought of making a neural network AI for my game and now I am excited. You make it easy and fun to learn this hard topic and I appreciate it, sub for life. I look forward to more of your videos like this or programming in general. I may have missed it but what language are you using, C++, Python, C#. I dont watch the video completely before I comment just to give you a heads up so if this question is answered I will edit this comment. I am so excited, its crazy I never thought I would be this way about Neural Networks, I know you must think I am crazy but it was like BOOOM!!! my mind expanded and I saw so many things to do with this. Keep up the great work. I look forward again to more.

Interesting. The random approach tests more possibilities because it sometimes just doesn't follow a particular form of logic...well besides the logic that dictates the generation of said randomness. That makes me wonder if different seeding methods for random number generation would have an impact.

First of all, very nice series, you got yourself one more sub! Some questions: I might have missed it, but did you apply any mutation to your genetic algo? If not, that might be why a riskier one is better, as in nature mutation is the way to get out of local minima. Why only use one layer in your neural net? It would be interesting to compare performance of a few Forests with different number of layers. Maybe the model is a bit harsh. Forest can only run left or right. If he also knew how to stop, but was rewarded for running a course fast, then I think he would do better. As it stands I think he can enter a dead end which he can't see the end of and thus have no chance to turn back, regardless how smart he is. Once again, had a lot of fun watching the series, hope to see more game related ML!

Did you used python for machine laearning or C#?? Plz reply, I wanna know that if we can use machine learning in unity C#

how'd you make those beautiful graphs? and what program do you use to train forest?

Could you give some advice about how you got from, wanting to program too making A.I. like where to started and the steps afterwards. I see a bunch of videos and bootcamps but none of them tell what I should learn first they are all about what they are selling.

lol the end segment is always so funny looking after watching you talk with your mouth closed all video

What I was wondering, how is forrest accessing the information? In the first video you said using your eyes, which signal the brains, which give commands to your fingers to move the joystick. You base everything on visual information in real time. How is forrest doing it though? Does he have a position and in real-time the forementioned 5 numbers? I am not understanding this part, because if he had all the numbers, his task would only be not hitting 0 for everyone of those 5 numbers and that shouldn't be too hard since backtracking doesn't seem to be allowed, should it? Is this even a task where forrest has perfect information!? I am quite confused as you might realize reading this, hopefull you can help me and answer this question. Kind regards.

I love his way of speaking😍

This is the first time i hear about these stuff but i think if u put a timer on forest ghosts(i call them that) and reward the one who runs the course in less amount of time it can be trained better! i luv the way u make these Videos keep up to good work :) Peace

What is stopping you from executing a random/probability method initially and switching to a different algorithm later so as to get results quicker and retain accuracy later? Maybe I don't know what I am talking about?

This guys is a Damn Genius!

just found your channel, this is top quality content keep it up!

You are a programming savant! Anyway, I wanted to say, this is why nature has parental units for the more complicated creatures. Imagine if you parented Forrest through the maze. Taught him how to traverse a maze. He would pick it up MUCH faster, and he might even be able to then teach/parent others. You wouldn't have to depend on all this "genetic algorithm," random-direction, brute-force ridiculousness. Nature relies on coaching. Why don't our AIs? Just a thought. ;-) (Of course, this idea is seriously challenged by the fact that AI now beats us at Go and chess. Uh oh.)

I am jealous of your brain. Well done!

"Take a second to let that register...." 😂😂😂 I’m weak

"the data says A is the best way forward, but let's also try B" Spoken like a true scientist! ALWAYS seek to disprove things you observe, especially when you are looking at an emergent field

What software did u use for generating his course....?

What is the name of the music that played while the Forrests ran, starting at 10:55? Amazing series btw, great work

I couldn’t complete that course that many times with out touching the walls damn, Go Forrest!!! Lol

I've never used this game, and am new to Neural network machine learning, so sorry if this is a dumb question. Now that you have forrests that can sucessfully complete the course is it possible to train forrest to complete the course in faster times?

I understand the logic behind machine learning but i dont even know where to begin when it comes to coding one. Any advice?

In order for Forrest to become one with the track; the track needs to also "breed". Have Forrest and the track locked in mortal combat until they become one with each other.Why not go for a: Generative adversarial network

I really liked that part at the end where we got to see you move your mouth! I know its weird but it just feels so much better to see your mouth move than not..... :P great videos btw

🎵This score though, you are awesome bro