world's best ai vs geoguessr pro

In this engaging transcript, a human player faces off against a Stanford University-developed AI in the geography guessing game, Geoguessr. The AI, which utilizes a pre-learned model called Westnet and incorporates meta learning and refinement techniques, boasts an impressive 92% accuracy in country guessing and a median error of 44 kilometers. The human player, while initially skeptical, is ultimately impressed by the AI's capabilities, especially its use of both images and text for enhanced accuracy. Despite the AI's proficiency, the human player manages to secure a win in a Laos-only map challenge, highlighting the potential for AI to learn from human strategies. The exchange underscores the ongoing development and potential of AI in gaming and beyond.

Takeaways

• 🧠 The AI, developed by Stanford University students, uses a pre-learned model called CLIP which was trained on billions of images, making it highly accurate in geolocalization.
• 🌍 The AI achieves a 92% accuracy rate in guessing countries and has a median kilometer error of 44 kilometers, translating to an average score of 4525.
• 📈 The AI's efficiency is enhanced by dividing the world into small cells, respecting both political and natural boundaries, and refining guesses within these cells.
• 📚 The AI is fed not only images but also text information like average temperature and climate, which significantly improves its geolocalization capabilities.
• 🚀 The development of the AI was a two-month project by the students, who also used it as a learning opportunity in a class.
• 🤖 The AI does not have access to real-time data such as Google Maps during the game but is trained on a large dataset of images from various locations.
• 🎲 The AI has not seen any of the specific images from the Geoguessr game, ensuring it's making guesses based on its training and not prior exposure.
• 🌐 The AI's guesses are so accurate that it can identify locations even when given a small chance, such as distinguishing between similar-looking areas in different countries.
• 🤷‍♂️ The human player acknowledges the AI's superior performance and finds the experience more impressive than frustrating, appreciating the advancement in technology.
• 🔍 The AI's decision-making process involves complex analysis, picking up on subtle cues that humans might also use, such as phone poles in Taiwan.
• 🏆 Despite the AI's capabilities, the human player still finds joy in the challenge and considers any victory, even in a specialized map like Cambodia or Laos, a significant accomplishment.

Q & A

What is the AI's current accuracy in guessing countries on GeoGuesser?

The AI is currently guessing 92 percent of countries correctly with a median kilometer error of 44 kilometers, which translates to an average score of 4525.

What model is the AI using for its improved performance?

The AI has switched to a pre-learned model called CLIP, which is a large Foundation model trained on billions of images.

How does the AI's training with both images and text contribute to its accuracy?

By training on both images and text, such as average temperature and typical climate, the AI becomes much more accurate at geolocalization as it can incorporate additional information about a given part of the world.

What technique does the AI use to refine its guesses within specific areas?

The AI uses a technique where it splits the world into small cells, respecting political and natural boundaries, and then refines its guesses within these cells.

How long did it take the Stanford students to build the AI for GeoGuesser?

The students worked on the AI for about two months.

What is the AI's approach to identifying different regions, such as being able to distinguish between similar areas like Canada and the United States?

The AI looks at various features like road lines, the structure of street signs, and even the smudges on the camera lens that can be indicative of certain regions.

How does the AI handle text within the images, such as street signs or license plates?

While the AI might not be particularly good at reading text, it does take into account the structure, colors, and shapes of text and signs within the images.

What is the AI's strategy for guessing locations within a country, such as different regions of Canada?

The AI seems to focus on specific details like the type of road signs and the environment to make an educated guess about the location within a country.

What is the AI's performance like when it comes to guessing less common locations or countries that are harder to distinguish?

The AI still performs well, but there are instances where it makes mistakes, especially in regions that are challenging for human players as well, like Cambodia.

How does the human player plan to compete against the AI in the game?

The human player plans to try and make it to the later rounds of the game, hoping to use good multi-off rounds to their advantage and capitalize on any mistakes the AI might make.

What is the potential application of the AI's guessing methodology for human players?

The AI's guessing methodology could potentially be used by human players to learn and improve their own strategies by understanding what the AI focuses on to make its guesses.

What does the future hold for the AI developed by the Stanford students?

While the students believe they have created one of the best AI models for GeoGuesser, they are considering writing a paper on their work and are open to the idea of further improvements and challenges.

Outlines

00:00

🤖 Facing Off Against Stanford's AI in Geoguessr

The speaker discusses their past experiences with AI in a 1v1 game, having won easily before. They are now facing a new challenge from a team of Stanford University students who have built a geographer AI for their class. The AI is described as impressive, with a high accuracy rate of 92% in guessing countries correctly and a median kilometer error of 44 kilometers. The speaker humorously expresses doubt about their chances against such a sophisticated AI.

05:00

🌐 Strategies and Techniques of the Stanford AI

The speaker inquires about the AI's strategy and learns that it's based on a large pre-trained model called CLIP, which has been trained on billions of images. The AI also uses meta-learning and refinement techniques and cleverly splits the world into cells that respect both political and natural boundaries. The AI's use of both images and text data enhances its geolocalization accuracy. The speaker also asks about the AI's development timeline and its performance in the class.

10:01

🎲 The Game Begins: Human vs. AI

The speaker and the AI start playing the game, with the speaker expressing a lack of confidence due to the AI's demonstrated capabilities. They discuss the AI's decision-making process, which includes understanding the difference between similar environments and structures. The speaker also questions the AI's ability to read text and street signs, to which the team explains that the AI focuses more on the structure and appearance of the environment rather than text.

15:01

🏆 A Close Game with Surprising Outcomes

The speaker continues to play against the AI, noting that it has not made any significant mistakes. They express a desire to see the AI score below 5000 points. Despite some humorous banter and self-deprecation, the speaker remains engaged and competitive. The AI's consistent performance is highlighted, with the speaker acknowledging the AI's skill even when they guess incorrectly.

20:02

🤝 Teaming Up with AI for a Comeback

The speaker enlists the help of a teammate, Traverse, to play against the Stanford AI. They strategize and make guesses together, sometimes successfully, and other times missing the target. The speaker suggests playing on a Cambodia-only map to leverage their extensive knowledge of the area. Despite the challenge, the AI continues to perform well, but the speaker and their teammate manage to win some rounds through coordinated efforts.

25:03

🏆 Victory Against All Odds

The speaker focuses on winning a game set in Laos, where they have spent considerable time and effort learning the geography. They express confidence in their knowledge and are determined to win at least one game. The AI makes some mistakes, and the speaker capitalizes on this to secure a win. They celebrate their victory and acknowledge the AI's potential for future improvement.

🎉 Wrapping Up and Looking Forward

The speaker humorously suggests that their career in geoguessing is over and asks for suggestions on what to do next. They express enjoyment in playing against the AI and appreciate the experience. They also hint at the possibility of future challenges and games, suggesting a match between multiple human players and the AI. The video ends with a call to action for likes and a goodbye to the audience.

Keywords

Geoguessr

Geoguessr is an online geography game where players are dropped into a random location on Google Street View and must guess their location based on visual cues. In the video, the game is central to the competition between the human player and the AI, showcasing the AI's ability to analyze and understand geographical data.

AI (Artificial Intelligence)

AI refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. In the context of the video, the AI developed by Stanford University students competes against a human player in the game of Geoguessr, demonstrating its advanced capabilities in image and text analysis.

Machine Learning

Machine learning is a subset of AI that involves the use of algorithms to parse data, learn from that data, and make informed decisions based on what they've learned. The Stanford AI uses a pre-learned model, which implies it has been trained on a large dataset to improve its performance in the game.

Stanford University

Stanford University is a prestigious institution known for its research and development in various fields, including computer science and AI. In the video, the students from Stanford are credited with developing the geographer AI that the human player competes against.

Google Maps

Google Maps is a web mapping service that provides satellite imagery, street maps, and route planning. The human player in the video is described as someone who likes Google Maps, which is relevant because the game Geoguessr uses Google Street View, a feature of Google Maps.

Pre-trained Model

A pre-trained model in machine learning is a model that has already been trained on a large dataset. The AI in the video uses a pre-trained model called CLIP, which has been trained on billions of images, to enhance its ability to guess locations accurately.

Geolocalization

Geolocalization refers to the process of determining the geographical location of an object or object's position. In the video, the AI's advanced geolocalization skills are highlighted, as it uses both images and text to predict the location within a certain range of accuracy.

Meta Learning

Meta learning is a process where a machine learning algorithm learns how to learn, improving its ability to learn new tasks with less data. The video mentions that the AI uses meta learning as one of its 'tricks' to improve its guessing accuracy in the game.

Computer Vision

Computer vision is a field of AI that focuses on enabling computers to interpret and understand visual information from the world, in a similar way that humans do. The AI's robustness in computer vision models is mentioned as a key factor in its ability to analyze images effectively.

Large Language Models

Large language models are AI models that are designed to process and understand large amounts of natural language data. The AI's focus on large language models suggests that it has been trained to comprehend not just images but also textual information, which contributes to its high accuracy in guessing.

GPS (Global Positioning System)

GPS is a satellite-based system that provides location and time information in all weather conditions, anywhere on or near the Earth where there is an unobstructed line of sight to four or more satellites. The video does not explicitly mention GPS, but the concept is implied in the discussion of geolocalization and location accuracy.

Highlights

AI has previously won twice against a human in a 1v1 AI match.

Stanford University students built a geographer AI for their class.

The AI has improved by switching to a pre-learned model called WestNet.

The AI's current accuracy is 92% in guessing countries and has a median kilometer error of 44 kilometers.

The AI uses a clip model trained on billions of images and adds meta learning and refinement techniques.

The world is split into small cells that respect political and natural boundaries for more accurate AI guessing.

The AI model also uses text information, such as average temperature and climate, to improve geolocalization.

The AI was trained on around a million images from 250k locations, with a very low probability of overlap with the game's images.

The AI's strategy involves picking up on minute details that humans might miss, such as smudges on the camera.

The AI's performance is so accurate that it guesses the right country even when the chance is as low as one percent.

The AI was developed as a two-month project by the Stanford students.

The AI does not read text like street signs but takes into account the structure and visual aspects of the environment.

The AI's guesses are based on a complex representation of the image that includes a lot of information.

The AI's ability to use both images and text makes it more accurate at geolocalization.

The human player suggests that the AI's potential lies in helping human players learn and improve their guessing strategies.

The human player is impressed by the AI's performance and acknowledges the advancement in technology.

The AI's development team considers their product possibly the best in the world and are considering publishing a paper on it.

The human player proposes a future challenge where multiple human players team up against the AI.