hahaha yes!

Make another ai to confirm every 10 mins that he is recording

@@ryleypalmer Manke another AI that narrates from "the future" if the real AI isn't recording

LMAOO

Lmao this made my day

Where do I know you from? Your name seems familiar

@@itzspiro6047 Do you watch Reid Captain?

@@NStripleseven yes sometimes

@@itzspiro6047 yea roaxial is a part of that community

@@NStripleseven I want to be a part of that community just because it looks gregarious, u guys know each other!

watch GeoWizard's series "guessing the exact locations of my fans using one single photo" his fans send him a photo of themselves and he tries to guess the exact location. it sometimes take 10 minutes, sometimes some hours, but he usually always gets it right.. but yeah, their photos are rarely a random house or something, so it helps. but still !

Yep, most likely, it isn't actually that hard to do but requires some work.

We can. I've found people by the view from their window.

@@Standplaats_Krakow It depends though. Not all pictures are foundable.

@@imutig3451 True, but there's often way more info than people think.

First

Any chance you'll make your code available? I assume this is a tensorflow build?

Is there a tutorial for how you did this?, i'd love to do it but with a worldwide map but am unsure how I would get started with this

i personally would love to see a continuation of this video!!!!

@@austeyen5628 YES PLEASE

Ireland

Vatican City

malta

Tunisia

@@lederp42 Given enough flags in the training set it would definitely be able to. Flags are basically rectangles with pastel colors, super easy to recognize for a computer ;)

I smell some nepotism.

@@Spoof10 what????? lmaooo do you know what nepotism means??? you think this guy has a friend at youtube who gave him views???

@@samcousins3204 You enjoy your time on the ground, while the joke is cruising at 10000 ft above you.

@@Spoof10 or the more virgin way, R/Whooosh

@@Spoof10 wooosh

Guessing from the video results, I also had the impression that this would be more accurate. It seems that the unsure boxes are skewing the guess away from the predicted location. More so when the prediction is close to the edges of the country and there are a lot of unsure boxes to the other sides.

I'm also quite surprised that he didn't at least use a squared average or something

My thought exactly. As soon as he announced his guess averaging technique I was skeptical of that being a good choice due to the nature of coastal areas looking similar...but being on opposite ends of the map.

I was about to comment the exact same thing. You can also see that the prediction sometimes is in a box with a lower confidence score, even though there exists a box more to the edges of the map with a higher score. I assume this is caused by the same bias towards the middle of the map.

Another way to solve this is to include in learning set border parts of Canada and Mexico. When output is in the wrong country it will point to closest location in US, but probably it won't happen as you point out that it's always moving towards center. Problem is with states near ocean tho.

On top of that, this averaging also assumes that the distribution is a single gaussian. It's possible that an image depicts some part of the East Coast, but its features is similar to a region on the West Coast. The distribution is then disjoint, with the network predicting a high probability on both the East Coast and the West Coast, but averaging this distribution will incorrectly produce a guess at the center of the US.

@@zenith5180 well I think this argument is not as strong as one might first think, and I want to explain why: In such a case you suggest a good gest would be either east or west coast getting about a 50% chance to have either a good or a desasterous guess. The AI's guess would be bad in both cases but not desasterous, do you still have a 50% chance being better or worse than the AI's guess. Probably your strategy still is the better, but that is due to the reward function of Geoguessr, which I don't know anything about but probably decreases the reward points exponentially with distance from the target or sth. similar. Now imagine a different case, where you have this becomes even more trickier: you have 3 fields, each have the same likeliness of ⅓. These fields are disjoined and form a equilateral triangle. If you go full risk for one of these fields while the AI guesses for the center of the triangle, the AI has a better guess distance wise in ⅔ of the cases. What this means for the expected value of reward points depends even more on how Geoguessr's internal distance2target->points function. Therefore, I think the general idea of using not just the field with the highest likeliness but something, let's call it 'map awareness' is not wrong. However, a simple averaging is not the best idea, as explained in the previous comments. Personally, I think based on the AI's predicitions one could compute a final regularizes guess coordinate as the guess in a little bit more complicated way: one cross-relates the probability of a field with each of the other fields by considering also the distance between those fields. For example you have field A with a high likelihood and directly east of it field B, also with a high likelihood. This would be a clear cluster, and since the distance between the both fields is minimal the two high likelinesses would push each fields overall likeliness. If field A and B are from the east and west coast, the great distance between them would prevent, that they buff each others likelihood. And if field B has a not so high likelihood, it would buff the likelihood of field A just very weakly. Ofc, the likelihood values are not adding up to 1 anymore, that's why I'm not using the term probability here. However, the higher this value would be, the more likely is this region a good guess. I think that way, one would make improved guesses. Also, one can experiment if these values, likelihood and distance between different fields, should enter the problem just linearly or results improve, if the predicitions depend on these values in a squared/etc. fashion.

+

Me too, then I saw he had under 100 subs

Just need to scream nonsense while failing an easy video game: 1M subs.

Thanks for the sub! I collected all of the images using Google's Street View Static API developers.google.com/maps/documentation/streetview/overview and collected images from random latitudes and longitudes within the US. The resource I linked in the description developers.google.com/machine-learning/practica/image-classification is probably the best resource I found for understanding the fundamentals of CNNs and feature extraction. Its exercises are also pretty useful for seeing how to implement CNNs using TensorFlow. For this project in particular, the Keras Applications documentation keras.io/api/applications was useful for finding and using a pre-trained model for feature extraction.

Now this is wholesome

@@adumb_codes Thank you for sharing!

lmao

Similar to Poland with less greenery and more occasional mountains

Hahaha. The "official" United States map on Geoguessr is notoriously bad and those are the usual locations you get in the middle of nowhere. The "United States (no blurry)" map is so much better.

There is some mathematics ionvolved in picking the avertage of a set of points on a sphere. In particular, it is not necessarily unique. Think for example of the mean of the north and south pole, every point at the equator is equally reasonable as a mean, anyway the average of two opposite points does not make that much sense. A way around this problem would be first picking the continent which is most likely and then taking the average over this landmass, islands would still be a problem. So a better solution is to just take a region around the box with the highest probability and consider only boxes lying in that region. Also the shape of the boxes is not square anymore, so we should multiply the weight by the size of the box. The problem of training data can be adressed by automatization. Google street view should provide a suitable interface that can be used to automatically choose a random spot and generate some pictures. Using multiple pictures would have a great potential for improvement.

maybe some kind of repeated grid patterns in finer detail with the center of the most confident box would work well

That's difficult, because what we train here is an image classifier and therefore we need predefined classes (our boxes) before (!) we start training. Since we want to use the same AI for each location we need the same boxes everywhere.

@@jakobm.4183 for a sphere, you could take the coordinates of a sphere, then turn them into a vector from the center of earth and average that - but since not all grid spaces are going to be used (for example, oceans), then there's going to be an implicit bias. Your method is likely better.

im sorry if this is dumb but couldnt you just use a slightly incorrect mapping? there would be bias towards some areas but you could just use a flat map of the earth then relate that point to the same point on the sphere

I think with more training data the AI should figure that out sometime

@@mst8819 The AI will not learn to do so given enough time, in it's current setting anyway. It outputs confidence values for each of the boxes. The output of each box is just that, it won't be able to adjust towards roads just like in the animal example it will not suddenly output what part of an animal is in the picture, e.g. "ear of fox", unless you trained it that way in the first place

I had a similar thought, but for a different reason. Using averaging will negatively impact locations near the borders, since it can only average it away from the border. Would be interested to see how a "naive" AI (center of most probable square) would compare. Still a good video though!

I think the same. Average can't be used with terrain. Pictures of the coast would average to the middle of the country. Sides of a mountainous region would average to the mountainous region.

Using weighted average does make it statistically better than the average, though. Seeing as the lowest score possible would be to choose a point on the coast when the correct location is on the opposite coast, this possibility is eliminated when the AI will only choose a point closer to the center. Therefore, even when the AI is wrong, the way the point is calculated will still give it a better score on average. In conclusion, using a weighted average is a msitake because it's sorta like cheating, and makes it hard to see if the AI is actually doing well.

And won't using this average method having a result always in the 8 middle squares ?

Very interesting conversation here! I think you all bring up reasonable points. Which method is preferred also depends on how the score in geoguessr is calculated. Take the opposite coast example: if the distance from the middle to one coast is too far to let geoguessr give you any points, you might as well make a guess between one of the two coasts and have a 50% chance for a lot it points, instead of 0% chance at any points when taking the average. What I keep thinking of is that fact that this question should actually be optimized *by the AI itself*. If you could train it based on the actual score it would get in a game, that would be awesome. Not sure how that would work technically, but from a theoretical view it would be really interesting!

yeah I predict it'll get 100k - 1m views, give it a few weeks or days

I'm guessing they don't have an API that can do it for that exact reason lol. You could use google maps API but afaik that costs money.

Just feed the ai like 50 images where ur navigating the environment, then u can create a weighted average of it's guesses for them.

@@matthewbadger8685 Well yeah but how many pictures would you need for that ._.

@@prav2568 50

@@chippyconqueror bruh i Don't know about Geosquare but as I assume it includes a whole lot of area , you would need more than that

the hero we didnt deserve

Unironically though: those recaptchas actually have grain added to them to confuse the AI, its some very specific grain too.

@@RubyPiec Good, good... as long as the humans are ahead of the AI then I am all for back to captchas.

Yes, moar geoguessr please!

I was just about to write the same thing. The prediction always tends to be more in the center than the actual location, due to the weighting

Results may improve without any weighting

It's not about the edge vs the center, it's in general that it cannot sensibly represent ideas such as Florida and California being likely and Texas unlikely. It assumes a uni-model distribution of confidence over the map.