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Overcoming the Barriers of the Physical World with AI

The intricacy of real-world navigation must be addressed in order to integrate AI into the physical world. Delve into the article to find out how AI can be used to overcome obstacles in the physical world.

The rapid advancement of artificial intelligence (AI) is revolutionising our lives and work, making processes more efficient. Technologies like large-scale machine learning and natural language processing models, such as ChatGPT, are pushing the boundaries of what was once confined to the realm of science fiction. However, a significant challenge remains in bridging the gap between technical brilliance and real-world application.

While AI has made significant progress in virtual environments, the introduction of AI-powered general-purpose robots in the physical world still faces substantial obstacles. Why is this the case, and how can we address these barriers? We explore the topic in more detail below. 

Energy efficiency stands out as a primary obstacle. At its core, a robot is essentially a self-propelled computer. Anyone who has used a laptop knows that even the best devices struggle to operate for more than a few hours without recharging. With robots, energy demands are even higher due to internal processes and physical movement. Safety considerations prevent them from relying on tethered connections, necessitating extended battery life. 

Unfortunately, current robot mechanics and autonomous systems lack the energy efficiency required for sustained operation. They require frequent and extended charging periods to perform optimally. While the first generation of robots is utilised in industrial settings for manufacturing, they remain constantly tethered to a power source. Although there are general-purpose robots available, like Sanctuary’s Phoenix humanoid, they are still cumbersome and expensive. It will likely take five to ten more iterations before we achieve a model that is truly independent, freely moving, and capable of performing various tasks.

To bridge this gap, we must start with smaller and simpler applications that gradually lead to full AI integration in the physical world. Cobots, which are robots designed for simple tasks, can play a crucial role in this process. Examples include self-driving wheelchairs, robots cleaning building facades, or autonomous technology performing complex, focused tasks like a smoke-diving robot searching for people or a drone fixing power lines. The key is focusing on single-duty performance, not only to enhance energy efficiency but also to achieve the highest standard of work.

Bringing AI into the physical environment requires addressing the complexity of real-world navigation. Human spatial awareness and navigation involve intricate mental processing, making it challenging to explain to robots. One solution lies in sensors, particularly 3D sensors like depth cameras, which capture the geometry and texture of physical objects. By analysing this data, AI algorithms can develop a better understanding of objects in the physical world. This understanding is vital for tasks such as spatial relationships, object movement, and human interactions. AI-powered mapping and localisation systems that generate maps of the physical environment and track object movements become integral in creating genuinely autonomous robotic assistants.

Mechanical efficiency is another critical aspect. By improving the way robots move, potentially by utilising artificial muscles and joints to mimic human motion, we can reduce their energy requirements. However, achieving fully functional humanoid technology is still a considerable distance away.
In the tech industry, the pursuit of intelligent robots has been a long-standing goal. However, a more nuanced approach is now necessary. Instead of relying solely on overall solutions from industry giants, an evolutionary methodology is called for. Specialist startup companies with relevant expertise can produce individual components that address the multiple challenges faced by developers. Once these components are in place, collaboration can occur, leading to the creation of efficient, functional, and affordable general-purpose robots.

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