The emergence of the “Artificial Internet of Things” (AIoT) as a technology ecosystem gained traction during the pandemic, leading to the development of Smart Homes.
AIoT integrates connected devices (IoT) with artificial intelligence (AI) embedded within them.
The past year has been marked by significant challenges due to the global pandemic, prompting a shift in mindset towards a long-term coexistence with Covid-19.
Adapting to this new reality, individuals, governments, industries, and businesses are continuously evolving to ensure safe, productive, and fulfilling lives for people.
The pandemic has reshaped work dynamics, with remote work becoming the new standard. The emphasis on the importance of work and homes has surged, sparking discussions on tech-enabled smart homes.
Although the concept of smart homes and associated technologies is relatively nascent, ongoing research and advancements are addressing obstacles hindering the realization of AIoT.
Privacy and security concerns related to AI’s data dependency have been addressed through local data processing within homes, thereby enhancing data protection and reducing the risk of data breaches.
Smart Home Solutions
Implementing robust security features at the device level, such as secure key storage and encryption, enhances data security and decision-making processes within smart homes.
Efforts to address connectivity barriers in AI deployment have led to on-device processing solutions, ensuring efficient operations even in low-connectivity scenarios.
Advancements in AIoT
Scaling concerns have been mitigated by the evolution of IoT scalability benefits, reducing reliance on cloud infrastructure and paving the way for edge technology success.
The market for AIoT has witnessed growth and technical advancements, improving on-device processing capabilities while optimizing power consumption and costs, making AIoT chips more accessible to product makers.
Engineers transitioning to AIoT chips can leverage familiar industry-standard programming methods like FreeRTOS and TensorFlow Lite, streamlining development processes and enabling swift integration of AI capabilities into IoT systems.
Ensuring a unified programming environment capable of meeting computing requirements is crucial for accelerating the adoption of AI in smart homes in the post-pandemic era.
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