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选择您的第一个基因AI 使用案例
要从基因AI开始，首先关注能够改善人类信息体验的领域。
销售商对模型进行了来自其阿波罗戈机器人舰队数百万英里数据的培训。
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Baidu.hapapapa通过Getty Images开发的自驾机器人
Baidu模型显示对于自主车辆等行业来说，物理AI正变得多么重要，但它也突出了与世界模型有关的主要挑战之一：费用。
中国开源 AI销售商，在周二的AI日阿波罗活动上，发布阿波罗世界，一个为训练Baidu的机器人轴等自主车辆而建造的世界型号。 根据Baidu，该模型每天可以产生大约3年的真实世界测试。 它接受了来自阿波罗戈船队的217.48万英里数据的培训。
阿波罗世界反映了AI世界模型在将超越知识任务的基因AI转移到物理世界中的灵活性和重要性。 许多AI模型仍然适用于编码和网络安全等领域；然而，世界模型越来越受欢迎，使企业能够看到AI如何影响自主载体等领域。
称 Lian Jye Su, Omdia的分析师，Informa TechTalget的分部。 他表示：「这是世界模型最合理的应用，“另一个是游戏，因此，在游戏和汽车之间，世界模式的供应商有很多机会使世界模式的使用民主化。”
Su补充说，企业可以将世界模式用于各种目的，从而有可能实现商业可行性和受欢迎程度。 这种可行性体现在世界模式创造者的多样性以及他们对自主驾驶的重视上。
来自Google DeepMind的Waymo世界模型是在Genie 3基础模型之上建造的，并将视频日志转换为3D模拟。 Waabi是另一家依靠其专有世界模式在高速公路上投入实际车队之前训练其AI-First虚拟卡车车队的供应商。 Nvidia Cosmos 开源世界模型使开发者能够将物理世界逻辑放入他们的机器人和自主工作流程中。
尽管世界模式越来越受欢迎，但一项重大挑战是，由于培训和实时运行所需的大量计算能力，这些模式的费用如何高昂。 大多数世界模型还需要每个活跃用户一个GPU，这对用户来说是极其昂贵的。 然而，即使成本很高，模型也值得投资，Su 称。
“世界模型是非常强大的模型，” Su 称。 “你一有正确之处，就掌握了有力的基础数据。 你可以产生非常精确的物理世界虚拟版本的复制品，这通常对非常准确的预测和生成有很大的贡献。”
Esther Shittu是一名Informa TechTalget新闻作家和播客主持人和制作人，负责AI技术。
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The vendor trained the model on millions of miles of data from its Apollo Go robotaxi fleet.
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Tourists in Wuhan, China, hail a self-driving robotaxi developed by Baidu.hapabapa via Getty Images
The Baidu model shows how important physical AI is becoming for industries such as autonomous vehicles, but it also highlights one of the main challenges associated with world models: expense.
The Chinese open source AI vendor, at its AI Day Apollo event on Tuesday, unveiled Apollo World, a world model built to train autonomous vehicles such as Baidu’s robotaxis. According to Baidu, the model can generate about three years of real-world testing per day. It was trained on 217.48 million miles of data from the Apollo Go fleet.
Apollo World reflects the flexibility and importance of AI world models in moving generative AI beyond knowledge tasks into the physical world. Many AI models remain applicable to domains such as coding and cybersecurity; however, the growing popularity of world models enables enterprises to see how AI can influence domains like autonomous vehicles.
“It’s the most reasonable application of world models because the automotive industry is one of the more mature ones,” said Lian Jye Su, an analyst at Omdia, a division of Informa TechTarget. “The other one is actually gaming, so between gaming and automotive, world model vendors have plenty of opportunities to democratize world model usage.”
Su added that enterprises can use world models for various purposes, thereby potentially achieving commercial viability and popularity. That viability is evident in the variety of world model creators and their focus on autonomous driving.
The Waymo World Model from Google DeepMind was built on top of the Genie 3 foundation model and converts video logs into 3D simulations. Waabi is another vendor that relies on its proprietary world model to train its AI-first virtual trucking fleet before putting an actual fleet on the highway. The Nvidia Cosmos open source world model enables developers to put physical world logic into their robotics and autonomous workflows.
Despite the growing popularity of world models, one major challenge is how expensive they are due to the massive compute power they require for training and for them to run in real time. Most world models also require one GPU per active user, which is extremely expensive for users. However, even with the high cost, the models are worth the investment, Su said.
“World models are very powerful models,” Su said. “The moment you get it right … you have strong foundational data. You can generate very accurate duplicates of a virtual version of the physical world, and that generally contributes heavily to very accurate prediction and generation.”
Esther Shittu is an Informa TechTarget news writer and podcast host and producer covering AI technology.
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