Core Insights - The article discusses the transformative potential of spatial intelligence in AI, emphasizing its ability to interact with the three-dimensional world through perception, navigation, operation, reasoning, and environment generation [4][6][8] - The integration of various algorithms and technologies, such as computer vision, deep learning, and multimodal learning, is crucial for the development of spatial intelligence [6][7] Group 1: Spatial Intelligence Development - Spatial intelligence is defined as the capability of AI to interact with the three-dimensional world, relying on multiple forms of algorithms and technologies [4][6] - The development of spatial intelligence involves challenges such as integrating diverse data types and executing complex tasks [2][4] - The company is focusing on creating a multimodal fusion spatial intelligence model that aligns with user scenarios, utilizing pre-trained large models and reinforcement learning techniques [6][7] Group 2: Technological Foundations - Key technologies for spatial intelligence include computer vision, deep learning, 3D representation learning, and visual-language models [6][7] - The company has extensive experience in various technical fields, which has been applied to multiple projects and solutions [6][7] - The ability to process and analyze diverse data types, including text, images, sounds, and environmental data, enhances the robustness and generalization of spatial intelligence models [7][8] Group 3: Future Plans and Market Strategy - The company aims to develop specialized AI agents for mobile terminals and smart environments, enhancing the value and competitiveness of Chinese products in overseas markets [7][8] - Short-term goals include creating AI agents with human-like thinking and long-term memory capabilities for wearable devices and robots [8] - Long-term objectives involve evolving from specialized AI agents to general intelligence agents, exploring advanced spatial intelligence and autonomous learning technologies [8]

当前，大模型技术正通过架构革新与多模态融合，重构空间智能发展的底层逻辑，推动其从 实验室走向产业化应用。传统的人工智能方法关注处理结构化数据和遵循预定义的规则。然 而，空间智能的出现就是为了处理物理世界因多样性、复杂性导致的更为细致的空间推理。 通过空间智能，机器可以用类人的方式与周边环境进行3D立体互动，并进行解读；无可争议 的是深度学习模型已在各种计算机视觉任务中有了很多出众的表现，但其面临的挑战，例 如，怎样集成多种数据类型并同时执行复杂任务就显得尤为突出。 我们与 特斯联国际总裁、特斯联首席科学家、AI Lab负责人邵岭博士 ，就空间智能及衍生话 题，诸如多模态数据融合等进行了 探讨 。 邵岭博士在人工智能领域有着数十年的前沿探索经验。 他 表示， 空间智能是人工智能和三 维世界交互的能力，它通过感知、导航、操作、推理和环境生成等多种形式展现，并依赖于 计算机视觉、深度学习、三维表示学习、多模态学习等多种算法和技术来实现，而特斯联正 在将 所有的模态数据统一到同一个语义空间，结合大模型的预训练和强化学习技术， 研发 与用户场景对齐的多模态融合空间智能大模型，并打造 类人思考、长期记忆、个性化 的AI智 ...