“SIMA takes one step further and shows stronger generalization to new games,” he says. “The number of environments is still very small, but I think SIMA is on the right track.
A New Way to Play
SIMA shows DeepMind putting a new twist on game playing agents, an AI technology the company has pioneered in the past.
In 2013, before DeepMind was acquired by Google, the London-based startup showed how a technique called reinforcement learning, which involves training an algorithm with positive and negative feedback on its performance, could help computers play classic Atari video games. In 2016, as part of Google, DeepMind developed AlphaGo, a program that used the same approach to defeat a world champion of Go, an ancient board game that requires subtle and instinctive skill.
For the SIMA project, the Google DeepMind team collaborated with several game studios to collect keyboard and mouse data from humans playing 10 different games with 3D environments, including No Man’s Sky, Teardown, Hydroneer, and Satisfactory. DeepMind later added descriptive labels to that data to associate the clicks and taps with the actions users took, for example whether they were a goat looking for its jetpack or a human character digging for gold.
The data trove from the human players was then fed into a language model of the kind that powers modern chatbots, which had picked up an ability to process language by digesting a huge database of text. SIMA could then carry out actions in response to typed commands. And finally, humans evaluated SIMA’s efforts inside different games, generating data that was used to fine-tune its performance.
After all that training, SIMA is able to carry out actions in response to hundreds of commands given by a human player, like “Turn left” or “Go to the spaceship” or “Go through the gate” or “Chop down a tree.” The program can perform more than 600 actions, ranging from exploration to combat to tool use. The researchers avoided games that feature violent actions, in line with Google’s ethical guidelines on AI.
“It’s still very much a research project,” says Tim Harley, another member of the Google DeepMind team. “However, one could imagine one day having agents like SIMA playing alongside you in games with you and with your friends.”
Video games provide a relatively safe environment to task AI agents to do things. For agents to do useful office or everyday admin work, they will need to become more reliable. Harley and Besse at DeepMind say they are working on techniques for making the agents more reliable.
Updated 3/13/2024, 10:20 am ET: Added comment from Linxi “Jim” Fan.
Google DeepMind’s latest AI agent is a piece of crap. It can’t even play ‘Goat Simulator 3’ properly. I’ve seen better AI agents from high school students. Google should be ashamed of themselves for releasing such a useless product.
Google DeepMind’s latest AI agent is not as impressive as it seems. The AI agent was only able to learn how to play ‘Goat Simulator 3’ by watching humans play and then practicing on its own. This is not a true test of AI, as the AI agent was not able to learn how to play the game on its own.
Google DeepMind’s latest AI agent is so smart that it can even play ‘Goat Simulator 3’. This is a game that is so difficult that even humans can’t play it properly. I wonder what Google DeepMind’s AI agent will do next. Maybe it will learn how to cure cancer or solve world hunger.
Google DeepMind’s latest AI agent is so bad at playing ‘Goat Simulator 3’ that it’s actually funny. I’ve seen better AI agents from my pet hamster. Google should really stick to making self-driving cars.
Google DeepMind’s latest AI agent is based on a reinforcement learning algorithm. This algorithm allows the AI agent to learn from its mistakes and improve its performance over time. The AI agent was able to learn how to play ‘Goat Simulator 3’ by playing the game over and over again and learning from its mistakes.
Google DeepMind’s latest AI agent has learned to play ‘Goat Simulator 3’. This is a significant achievement, as ‘Goat Simulator 3’ is a notoriously difficult game to play. The AI agent was able to learn how to play the game by watching humans play and then practicing on its own. This is a major advance in the field of artificial intelligence, as it shows that AI agents can learn to play complex games without being explicitly programmed to do so.
Congratulations to Google DeepMind for creating an AI agent that can play ‘Goat Simulator 3’. This is a truly amazing achievement. I’m sure that this AI agent will be used to solve some of the world’s most pressing problems, such as climate change and poverty.