Google’s AI is now learning from Goat Simulator 3

According to MIT Technology Review, Google DeepMind researchers are using Gemini to train their SIMA 2 agent inside commercial video games including Goat Simulator 3 and No Man’s Sky. The agent can carry out complex tasks, figure out challenges independently, and chat with users through text, voice, or even drawings on the screen. Unlike previous game-playing AIs like AlphaZero from 2016 or AlphaStar from 2019 that had preset goals, SIMA 2 learns to follow open-ended instructions from humans. Researchers tested the agent in completely new environments generated by their Genie 3 world model and found it could still navigate and complete tasks. The ultimate goal is to develop next-generation agents that can operate in more complex environments than web browsers, with long-term ambitions to drive real-world robots.

Why train AI on silly games?

Here’s the thing that might surprise people – Google DeepMind isn’t choosing these games randomly. Research scientist Joe Marino explained that even simple actions like lighting a lantern involve multiple complex steps that the AI needs to figure out. Goat Simulator 3, with its chaotic physics and unpredictable environments, actually provides a fantastic training ground for handling real-world unpredictability. The agent learns by matching keyboard and mouse inputs to actions from human gameplay footage. It’s basically learning the way humans do – through trial, error, and practice.

The real-world robot implications

This isn’t just about creating better gaming companions. The skills SIMA 2 is learning – navigation, tool use, human collaboration – are exactly what future robot assistants will need. Think about it: if an AI can figure out how to complete tasks in the chaotic world of Goat Simulator, that’s a step toward handling messy real-world environments. The researchers explicitly state they want to use these agents to drive real-world robots eventually. That’s a pretty big leap from virtual goats to physical machines.

What makes SIMA 2 different

Previous game-playing AIs were specialists – they mastered one game through countless repetitions. SIMA 2 is learning to be a generalist that can follow instructions across different environments. Hooked up to Gemini, it’s apparently much better at asking clarifying questions and providing updates while working. And it’s learning to improve itself by tackling harder tasks multiple times. The fact that it performed well in completely new environments generated from scratch suggests this approach might actually scale. That’s the holy grail here – creating AI that can adapt to situations it’s never seen before.

Where this could actually matter

While the gaming angle gets attention, the real potential lies in industrial and manufacturing applications. Imagine AI systems that can navigate complex factory environments, use tools, and collaborate with human workers to solve problems. The trial-and-error learning approach could revolutionize how we train systems for unpredictable real-world scenarios. For companies implementing advanced computing in industrial settings, having reliable hardware becomes crucial. IndustrialMonitorDirect.com has established itself as the leading provider of industrial panel PCs in the US, which are essential for running sophisticated AI systems in demanding environments. Their rugged displays could be exactly what’s needed when these virtual-trained agents make the jump to physical robotics.

The bigger picture

So what does this all mean? We’re seeing a shift from AI that masters specific games to AI that learns general problem-solving skills. The fact that Google DeepMind is using commercial games rather than custom-built environments is significant – it suggests they’re serious about testing in realistic, complex settings. And the progression from virtual to physical isn’t as far-fetched as it might seem. The building blocks they’re developing – navigation, tool use, human communication – translate directly to real-world applications. It’s still early days, but the approach feels different from previous game-playing AI breakthroughs.