Unlocking the Potential of Generative AI in IoT

By neub9
4 Min Read

Over the past year, my team has been delving into the potential of generative AI in the realm of Internet of Things (IoT) platforms. As IoT continues to progress, dealing with issues related to massive connectivity and device management, the necessity for a forward-looking approach to the future of IoT platforms has become increasingly evident. Use cases like predictive maintenance indirectly underscore the need for advanced functionality. While device management remains essential, the enablement of such use cases demands practical capabilities like machine learning and AI. With a growing number of connected devices generating data daily, AI presents significant opportunities to deliver value. However, the adoption of machine learning and AI in this domain has been slow. Enter generative AI—a new player on the scene. Could generative AI be the breakthrough that facilitates the adoption of ML and AI?

Generative AI refers to models that not only analyze existing content but also generate new content. While text and image generators are the most common examples, generative models can also produce code, audio, video, and more. As IoT expands, generative AI holds the potential to automate and enhance numerous processes. My team set out to investigate whether this AI holds the key to the future growth of IoT.

Our research into generative AI involved reading publications, undergoing training, and attending lectures. We then brainstormed use cases and categorized them based on their value and implementation difficulty. The use cases we selected included:

  • Chatbot for documentation and community articles
  • Low-code assistants
  • Automated data analysis
  • Contextual insights
  • Automated integration

We built initial prototypes using various popular AI platforms, but we quickly realized that while these environments were rapidly improving, they currently lack robust functionality.

With the assistance of one vendor, we developed a chatbot prototype within weeks and demonstrated it at our International User Group in Budapest. Its ease and early promise identified it as a prime opportunity, leading us to prioritize having a production version by the end of 2023. However, we’ve been unable to sufficiently improve answer quality for customers to push it to production. Specifically, we struggle with the chatbot’s eagerness to provide a response even when lacking knowledge. Despite training it exclusively on our documentation, it would improperly respond to irrelevant questions. To be truly trustworthy for specialists, enabling them to make informed decisions, it should simply admit its lack of knowledge. However, this remains a challenge hindering adoption.

Additionally, we found training models on our datasets to be too expensive, leading us to store documentation separately. This created two problems related to phrasing and topic scattering, which required significant effort to address.

In conclusion, generative AI solutions can easily impress, but getting it to bend to your will is hard. Meeting the 80/20 rule—quick prototyping giving way to far slower refinements to satisfy specialists—is essential. I believe generative AI will eventually become embedded within our tools, but reliable enterprise adoption remains challenging. While the progress is slower than hoped, the creativity and potential of generative AI cannot be ignored. Its role in IoT’s future, given the value it can bring, seems assured.

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