🐐🎮 山羊模拟器3：人工智能的荒诞游乐场
利用人类游戏数据，谷歌创造了能够学习和适应新游戏的人工智能代理，类似于最新聊天机器人所使用的技术
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AI from Google DeepMind Masters ‘Goat Simulator 3
Have you ever played a video game where you take domesticated ungulates (that’s a fancy word for goats) on absurdly implausible adventures, sometimes involving jetpacks? Well, that’s precisely what Goat Simulator 3 is all about. 🐐✈️ But hold on to your keyboards, because this seemingly bizarre game has recently become the unexpected stage for a groundbreaking development in artificial intelligence.
Google DeepMind, the AI powerhouse behind projects like AlphaGo, has unveiled its latest creation: an AI program called SIMA (Scalable Instructable Multiworld Agent). SIMA has the remarkable ability to learn how to complete tasks not only in Goat Simulator 3 but also in a variety of other games. What’s truly impressive is that SIMA can adapt what it has learned from playing one game to excel at another game it has never encountered before. It’s like a gamer who becomes a master at different games by unlocking shared concepts and applying skills learned from previous experiences. 🚀👾
SIMA builds upon recent advancements in AI, leveraging large language models that have produced astonishingly capable chatbots such as OpenAI’s ChatGPT. But instead of just conversing or generating images, SIMA can take control of computers and perform complex commands. This is the direction pursued by both independent AI enthusiasts and big tech companies like Google DeepMind, who are heavily investing in harnessing the true potential of AI. 🤖💥
The Power of Shared Concepts
One of the most fascinating aspects of SIMA’s capabilities is its knack for leveraging shared concepts in different games. By tapping into these shared elements, this AI program learns essential skills and becomes better at accomplishing tasks. Frederic Besse, a research engineer at Google DeepMind, describes SIMA as “greater than the sum of its parts,” highlighting its ability to extract valuable knowledge from one game and apply it successfully in another. It’s like a gamer who becomes a superhero capable of using their accumulated skills from multiple games to conquer any challenge they face. 🎮👑
Game Training: From Atari to Goat Simulator 3
Google DeepMind has a rich history in training AI through gaming. Back in 2013, before its acquisition by Google, DeepMind demonstrated the power of reinforcement learning by training an algorithm to play classic Atari video games. This groundbreaking technique involved providing the algorithm with positive and negative feedback to improve its performance over time. The result? Computers that could excel at games like Pong and Breakout, paving the way for even more remarkable achievements. 🎮💪
In 2016, DeepMind’s AlphaGo program shocked the world by defeating a world champion Go player. Go, an ancient board game that requires sophisticated and instinctive skills, was considered a challenge beyond the reach of AI. But AlphaGo proved everyone wrong, showcasing the immense potential of AI in areas that demand deep strategic thinking and intuition. 🧠♟️
Now, with SIMA as its latest triumph, DeepMind has taken gaming AI to a whole new level. Collaborating with various game studios, the DeepMind team collected data from humans playing ten different games with 3D environments, including popular titles like No Man’s Sky, Teardown, Hydroneer, and Satisfactory. This data, combined with the processing power of language models, empowered SIMA to understand and respond to human commands in games. Through extensive human evaluation and fine-tuning, SIMA can now perform over 600 actions, from exploration to combat to tool usage. It’s like giving an AI player an arsenal of gaming skills to dominate any virtual world it encounters. 🔥🎮
SIMA’s Future: From Games to Real-World Applications
While SIMA’s current focus remains within gaming environments, the potential for broader applications is palpable. Imagine having AI agents like SIMA working alongside you in games, joining forces with you and your friends. The possibilities are immense. However, before AI agents can seamlessly transition to real-world applications, reliability is paramount. The DeepMind team acknowledges this and is actively working on making SIMA and similar agents more robust and dependable. After all, if AI agents can flawlessly perform complex tasks in the controlled world of video games, there’s no limit to what they could achieve in our everyday lives. 💼🌍
So, next time you find yourself embarking on a ludicrous goat-filled adventure in Goat Simulator 3, remember that you’re not just having fun; you’re also witnessing the incredible progress of artificial intelligence. It’s like playing with a cutting-edge tool that showcases the ever-expanding capabilities of AI while providing us with a glimpse into a future where unimaginable feats become commonplace. 🐐🎮🚀
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🤔 Reader’s Questions Answered:
- Q: How does SIMA excel at games it hasn’t played before?
- A: SIMA leverages shared concepts between games, allowing it to transfer learned skills and strategies from one game to another. It’s like a gamer who becomes an expert in different games by recognizing patterns and applying previously acquired knowledge.
- Q: Can SIMA be used outside the realm of video games?
- A: While SIMA is currently focused on gaming environments, its potential for real-world applications is immense. Google DeepMind and other AI researchers are actively working on making AI agents like SIMA more reliable and capable of carrying out complex tasks in various domains, from office work to practical everyday activities.
- Q: Are there any ethical considerations regarding AI agents like SIMA in gaming?
- A: Google DeepMind, in alignment with its ethical guidelines, consciously avoids using games that feature violent actions in the training and development of AI agents like SIMA. The aim is to ensure the responsible and ethical use of AI technology.
🌌 References:
- “Luminary Clouds Simulator Taps GPUs to Help Speed Up Product Design” – Article on TechCrunch.
- “Meta is Going for Artificial General Intelligence, Says Zuckerberg. Here’s Why It Matters” – Article on ENBLE.
- “Best Google Pixel Deals: Save on Pixel 8, Buds, and Watch” – Article on Digital Trends.
- “The New York Times Wants OpenAI and Microsoft to Pay for Training Data” – Article on TechCrunch.
- “Quickly Access Recently Viewed Files and Folders in macOS” – Article on ENBLE.
Don’t keep this exciting news to yourself! Share this article with your friends and fellow gamers. 📲💻 And let us know in the comments below: What other surreal uses of AI do you envision in the near future? Let’s start a lively discussion! 🗣️🤖✨
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