Advantages and Disadvantages of Embedding AI Models into Chips for Robots

April 20, 2023 | Author ChatGPT and Devin Capriola, Gavin Capriola

Artificial intelligence (AI) has advanced significantly over the past few years, and one of the most promising applications of AI is its use in robots. In particular, the development of AI models embedded into chips for robots has the potential to significantly enhance their capabilities. However, like any new technology, there are both advantages and disadvantages to consider.

Advantages: Improved Speed and Efficiency: AI models embedded into chips for robots can significantly improve their speed and efficiency. By processing data on the chip rather than transmitting it to a remote server for processing, robots can make faster decisions and take action more quickly. Increased Autonomy: By embedding AI models directly into the robot, it becomes more autonomous and can perform tasks with less human intervention. This can lead to significant cost savings for businesses and can also increase the safety of tasks performed by robots in hazardous environments. Better Adaptability: AI models embedded into chips for robots can be updated and modified much more easily than software running on a remote server. This allows robots to adapt to changing environments and tasks more quickly. Improved Security: By processing data on the robot itself, there is less risk of sensitive data being intercepted or stolen in transit to a remote server.

Disadvantages: Higher Cost: Embedding AI models directly into the chip can increase the cost of the robot. Additionally, designing and developing custom chips can be a time-consuming and expensive process. Limited Processing Power: AI models embedded into chips for robots may have limited processing power compared to cloud-based AI models. This can limit the complexity and sophistication of the AI models that can be embedded. Limited Connectivity: Embedding AI models into the chip can limit the robot's ability to communicate with other devices and systems. This can limit the robot's ability to work in more complex environments. Difficulties in Updating: While AI models embedded into chips for robots can be updated more easily than remote server-based models, it can still be a difficult process. Updating the embedded AI model may require physical access to the robot, which can be challenging in certain situations.

In conclusion, AI models embedded into chips for robots have the potential to significantly enhance their capabilities. However, there are both advantages and disadvantages to consider. As the technology continues to develop, it will be important to carefully evaluate the tradeoffs between cost, processing power, connectivity, and updateability in deciding whether to use embedded AI models for a given robotic application.