In the realm of embedded artificial intelligence (AI), tasks that seem straightforward to humans can often prove to be complex and resource-intensive. One such challenge is enabling a robot or AI-powered device to open a door. This seemingly simple task involves several intricate steps that require the AI to:

• Detect the door within its environment.
• Identify the door's type: Is it a push/pull, sliding, or automatic door? Is it a combination of these?
• Locate the handle and determine its type (e.g., knob, lever) and its direction of use.
• Understand the door's movement: does it swing open, slide, or open in some other manner.
• Determine whether the door remains open or closes automatically.

Furthermore, each new embedded AI tasked with interacting with a door would need to go through this same process each time, resulting in a significant amount of trial and error. For each environment, the AI would need to "learn" the characteristics of every door, meaning that it would require new data for each interaction. The AI also likely wouldn't retain memory of doors it has already encountered, which leads to inefficiencies and delays.

But what if there was a more efficient solution? Enter collaborative spatial mapping with the Auki network, a game-changing approach that could streamline the process and save valuable time for embedded AI systems.

Collaborative Spatial Mapping with the Auki network: The Future of Efficient AI Interaction

With the Auki network, a door, along with all its relevant characteristics, can be added to a shared digital map. Devices such as robots, smart glasses, or even mobile phones can detect and catalog the door. Once the door is mapped, all AI devices that use the Auki network can access the shared data, making door interaction faster and more accurate.

The beauty of this system is that it is dynamic and collaborative. Multiple devices or users can contribute additional data to improve the map, including:

• Door Type:
• Handle Position and Type:
• Operational Direction:
• Access Requirements:

The Advantages of Collaborative Spatial Mapping with the Auki network

Embedded AI excels in dynamic, ever-changing environments, where real-time adaptation is crucial. However, many aspects of the physical world, such as doors and other infrastructure, are relatively static. Why should every new AI system analyze the same door repeatedly when it can be cataloged, mapped, and shared across all systems using the Auki network?

Collaborative spatial mapping via the Auki network allows AI to take advantage of static data, reducing redundant analysis and enabling faster decision-making. This shared knowledge base is particularly useful in environments where frequent interactions with common objects, like doors, are necessary.

Additionally:

• Inter-device Collaboration: Every device within a system, whether a robot, mobile phone, or smart glasses, can access and update shared data in real-time, ensuring that the information is always current and relevant.
• Real-time Data Updates: If the door changes state, locked, handle repositioned, or out of service, the Auki network can immediately update and push this new data to all devices, ensuring consistent, up-to-date information for all.

Conclusion: A Smarter Approach to AI-Driven Interactions with the Auki network

By embracing collaborative spatial mapping through the Auki network, the complexity of tasks like opening a door can be simplified for embedded AI systems.

Rather than starting from scratch each time a new device encounters a door, the device can rely on an existing map of information. This not only saves time and reduces trial and error, but also ensures that every device interacting with a door has the most up-to-date and relevant data available.

In environments where collaboration between devices is key to optimizing performance, the use of the Auki network provides a more streamlined, efficient, and intelligent way to handle tasks that might otherwise be unnecessarily complex.

アウキ・ラボについて

Aukiはポーズメッシュという地球上、そしてその先の1000億の人々、デバイス、AIのための分散型機械認識ネットワークを構築しています。ポーズメッシュは、機械やAIが物理的世界を理解するために使用可能な、外部的かつ協調的な空間感覚です。

私たちの使命は、人々の相互認知能力、つまり私たちが互いに、そしてAIとともに考え、経験し、問題を解決する能力を向上させることです。人間の能力を拡大させる最も良い方法は、他者と協力することです。私たちは、意識を拡張するテクノロジーを構築し、コミュニケーションの摩擦を減らし、心の橋渡しをします。

ポーズメッシュについて

ポーズメッシュは、分散型で、ブロックチェーンベースの空間コンピューティングネットワークを動かすオープンソースのプロトコルです。

ポーズメッシュは、空間コンピューティングが協調的でプライバシーを保護する未来をもたらすよう設計されています。いかなる組織の監視能力も制限し、空間のプライベートな地図の自己所有権を奨励します。

分散化はまた、特に低レイテンシが重要な共同ARセッションにおいて、競争優位性を有します。ポスメッシュは分散化運動の次のステップであり、成長するテック大手のパワーに対抗するものです。

アウキ・ラボはポスメッシュにより、ポーズメッシュのソフトウェア・インフラの開発を託されました。

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