วีดีโอ

This is such a great video. As someone who's working with automation and continuous improvements, I'd love to see a followup video with improvements on this. It would also be super fun to see two people try to make the best AI and compete vs each other.

Hi, I'm currently doing a research project at University and this video is very fascinating. I would like to know how you get the training dataset and set up the bot to interact with the game. Also if you can link me some of the research paper, that would be great

Show this to rainbolt

Isn't this a clear case of train data leakage during test as GeoGuesser uses Google Maps data just like your training data?

Do you know to what extent minor image imperfections like humanly imperceptible smudges on the lens are used by the AI? Do you have a way of illustrating which part of a picture provide the most information?

He is just like a me fr fr

Just got smoked by this machine, good job sir

Now this is the content i want to watch, high quality and educational on how to solve a problem

Instead of using squares, could we use semantic geocell creation to leverage regional distinctions?

Watching this knowing Damm well someone will use this to gets somebody’s address

Is it Dream?

This one is gonna be a viral

I didn't hear you say anything about separating you training and testing sets. Without doing that, the AI may just have "memorized" the images it was given during training, and it may not be able to generalize at all. I'd definitely want to hear more about the design of the AI.

Cold

Even if you don’t make a video on it, would really appreciate some sort of public source code on this. Myself and others have spent a lot of time on Geoguessr AI and this could be interesting to learn from.

Nah bro his montage is a 1m + youtuber and he have 1k only 😮

The Toxic Avenger soundtrack is a perfect fit for this video 🤩

But is it just overfitting on the training data or actually generalizing? How did you do your test/train/validation splits?

I mean the model was tested on every location so it's not really an 'ai' it just looks through its testingset for the same image

Crazy

We need an ai vs rain bolt

Wow great video and great project. I am just wondering how did you collect data for training model and how did it take so little time to train it. And do you have some tips for someone starting with ai?

1k

Just save the photo and location every time it gets confused then train it a bit more on just those places once you got maybe 50 stored up.

Basically you are overfitting the training set, imho the model trained like this Is unable to generalize

Vedal's Neuro-sama was faster!

Bruh, the first place was literaly 30km away from me :D

open source when? :P

Imagine making this a tool for reverse searching. Users can upload a photo and know exactly where it was taken

How does this compare against the standard PIGEON GeoGuessr AI? The claim of best geolocation AI is a bold one.

I would be very intrested in a deep dive on how you built all the project 🎉

This is super impressive! As someone with a programming and (some) AI background I’d be super interested to see what the process was of building the dataset, I’ve scraped small google maps/streetview datasets but it got.. pretty large, would love to hear more about your process of building it!

How is this your first video!?!?!?

Yooo the video is sick. I don't know why you dont have millions of views already lol The only thing that in my mind would've improved the video is if you described the technical part in a bit more detailed way, like where you got the dataset, how did you train the AI, etc. But that's just my opinion. Anyway, you got a new subscriber ❤

Bro, make this open source

The CIA is gonna hire you now

what about a more algorithmic predetermined approach for a bot to play it? like recognizing tree species or flowers and cutting the area down to a smaller radius?

albo to przypadek ale grałem z kimś z ameryki z podobnym skinem nie pamiętam najpierw źle trafił ale resztę gry dominowal klikajac prawie idealnie wszędzie

nice would be to change a bit the AI brains to be able to get out useful insights in an automated way of what to look, and what characteristics the AI is using to guess the region. I bet there is dumb stuff that we overlooked that help to guess right. I would love to learn this stuff.

Épico

You should release the model weights

Dox AI

Awesome video! Do you have a computer science background? :)

I have a feeling the AI could become nearly perfect if it could also interact with the Map and this way get more context of the location it is guessing and thus perform better. It may take a lot longer to train and make predictions, but I think the boost in accuracy would be worth it.

amazing video!

I’d like to see some kind of graphic made from this AI that shows some kind of similarity between different blocks or countries. I wonder how much it picks up on camera quality.

rainbolt:

A bit disappointed about the title and what was actually in the video. "How I made the best Geoguesser AI in the world" -> "It's complicated, I just put 600 000 images to the model and trained it 18 hours." .. Yeah, ofc you put a shitload of images and their locations, but that's the part that is interesting, but 3/4 of the video was about playing against it. I really hope you're going to release a more detailed video. What hardware, libraries,, what was working during your test, what wasn't, data normalization, finetuning on geoguesser meta ( poles, roads, cars etc ), how you created the "region grid"All the interesting stuff is missing ! 😁

I was quite literally going to look into doing this myself and you beat me to it. Props to you!

I wish you'd explain the region guessing part a bit better, what is it looking for? how does it work? what specifically did you train it on?