pro geoguessr player vs ai, it didn't go well...
TLDRIn a fascinating showdown, a professional Geoguessr player faces off against an AI in a game of geographical deduction. The AI, developed by a dedicated programmer, has been trained on approximately 30,000 to 40,000 images to classify places on Earth from brief street view snapshots. Using a browser plugin, the AI analyzes five screenshots covering 360 degrees of a location, identifies relevant image features, and guesses the location. Despite not always focusing on the same aspects as humans, the AI demonstrates remarkable accuracy. The human player initially wins three out of three games, but when playing at a disadvantage with pixelated images, the AI still manages to compete effectively. The developer plans to improve the AI's accuracy through continuous data collection and learning, hinting at a potential rematch in the future. The video is both entertaining and insightful, showcasing the current capabilities and future potential of AI in a fun and interactive way.
Takeaways
- 🧐 The former director of AI at Tesla expressed interest in professional Google Maps players and their high accuracy in classifying places from a street view image.
- 🤖 A developer is building an AI to compete in Geoguessr, a game where players guess their location from a street view image.
- 📚 The AI learns from a dataset of 30-40,000 images and uses a browser plugin to take screenshots and analyze them for relevant features.
- 🌍 In the first game, the AI performed well, with the human player only winning by a small margin.
- 🏆 The human player won the first two games, but the AI showed surprising accuracy and understanding in its guesses.
- 🎮 The AI's training involves playing the game, collecting data, and learning from that data over time.
- 👀 The AI does not always focus on the same features as humans do, yet it can still make accurate guesses based on its analysis.
- 🤓 The human player tried to give themselves a disadvantage by playing with a pixelated image, but the AI still managed to compete effectively.
- 🌐 The AI made some impressive guesses, such as correctly identifying Thailand and North Macedonia, despite the human player's handicap.
- 📈 The developers plan to continue improving the AI's accuracy by allowing it to learn from more gameplay and data collection.
- 🔄 The video suggests that there will be a follow-up in the future to see how much the AI has improved.
Q & A
What is the context of the video described in the transcript?
-The video is about a professional Geoguessr player competing against an AI in the game Geoguessr, where the player must guess the location of a given street view image.
Who is the person that tweeted about professional Google Maps players?
-The former director of AI at Tesla.
How does the AI in the video learn to play Geoguessr?
-The AI learns from a dataset of approximately 30,000 to 40,000 images, using a browser plugin to take screenshots and analyze them to determine the location.
What is the process the AI uses to guess the location in Geoguessr?
-The AI takes five screenshots covering 360 degrees of the street view, sends them to a backend system, and uses the image and location data to identify relevant features to make a guess.
How did the AI perform in the initial games against the professional player?
-The AI performed surprisingly well, even though it didn't always look at the same things as humans. It managed to guess locations with a high degree of accuracy.
What was the outcome of the first game between the professional player and the AI?
-The professional player won the first game.
How did the professional player fare when playing with a pixelated image?
-The professional player still managed to win two games in a row, even with the pixelated image disadvantage.
What is the plan for the AI after the initial competition?
-The plan is to increase the AI's accuracy by allowing it to continue playing the game, collecting data, and learning from it. A follow-up video is planned for when the AI has improved.
Who developed the AI that was used in the video?
-A developer named Lion, who has his own channel, developed the AI.
What was the final outcome of the best of five series between the professional player and the AI?
-The professional player won three out of three games in the best of five series.
How does the AI determine which features of the image are relevant for location guessing?
-The AI uses a backend system to analyze the images and figure out which features are relevant for determining the location based on the image and location data it has been trained on.
What was the professional player's strategy when playing with a pixelated image?
-The player tried to make educated guesses based on the limited visual information available in the pixelated image, but it was a significant disadvantage compared to the AI's usual process.
Outlines
🤖 AI vs. Human in Geoguessr: The Challenge
The video script describes a competition between a human player and an AI developed to play the game Geoguessr. The AI, trained on thousands of images, uses a browser plugin to take screenshots of the game environment and analyze features to predict locations. The human player, a former Tesla AI director, engages in a series of rounds against the AI, discussing the AI's performance and its ability to recognize and classify places from street view images. The human player also handicaps themselves by using a pixelated image to see if the AI can still outperform them. The video concludes with the human player winning the match but acknowledging the potential for the AI to improve with more training and data.
📈 Future of AI in Geoguessr: Continuous Learning
The second paragraph discusses the potential for the AI to get better at Geoguessr through continuous learning. The AI's developer plans to increase its accuracy by allowing it to play the game more, collect data, and learn from that data. The human player expresses excitement about the possibility of the AI improving and challenges it to a rematch in the future. The video ends with a call to action for viewers to subscribe for updates on the AI's progress and a thank you to the AI developer for creating the AI and collaborating on the video.
Mindmap
Keywords
💡Geoguessr
💡AI (Artificial Intelligence)
💡Tesla
💡TikTok
💡Machine Learning
💡Google Chrome
💡360-Degree View
💡Back End
💡Pixelated Image
💡Nerf
💡Data Collection
Highlights
A professional Geoguessr player competes against an AI in a game of guessing locations from street view images.
The AI was developed by a former director of AI at Tesla, showcasing the potential of machine learning in geolocation tasks.
The AI analyzes images and classifies places on Earth with high accuracy from brief street view image presentations.
The AI has been trained on approximately 30,000 to 40,000 images to learn from past experiences.
A browser plugin is used to load the game and take screenshots, which are then analyzed by the AI.
The AI uses five screenshots to cover a full 360-degree view of the location.
The AI does not always focus on the same features as humans when determining location.
In the first round, the AI guessed Slovakia, showing impressive accuracy.
The AI scored 3,400 points in round two, indicating a strong performance.
The AI made a close guess in round four, only losing 110 points.
The professional player won the first game, but the AI demonstrated a surprising level of skill.
In the second game, the AI continued to show its ability to guess locations accurately, even in challenging rounds.
The AI's performance was so strong that the player decided to give himself a disadvantage by playing with a pixelated image.
Despite the disadvantage, the player still managed to win two games in a row.
The AI's guess of North Macedonia in round three was impressive, even though it was incorrect.
The AI's ability to guess Thailand accurately in round three demonstrated its learning capabilities.
The AI's incorrect guess of South Africa in the final round showed that human players still have an edge.
The developer plans to continue improving the AI's accuracy through more data collection and learning.
A follow-up is planned for the future to see how much the AI has improved.
The video concludes with a call to action for viewers to subscribe for updates on the AI's progress.