How ambient IoT and generative AI can make products talk

It’s not often that two synergistic, paradigm-shifting technologies mature at the same time, yet both generative artificial intelligence and the ambient Internet of Things are ascendant. These symbiotic advances are poised to reinvent industries, protect public health and help fight climate change.

Let’s start with generative AI, which gets all the headlines (and investment) but is already experiencing growing pains. The large language models (LLMs) that underpin generative AI applications like ChatGPT and Microsoft Copilot learn by training mostly on publicly available data — news, information, social media — or licensed sources. 

Some datasets are combined or repurposed to create new training data and they all need appropriate cleaning and analysis to filter out bias, noise and low-quality information. Virtually all the data is human-generated and while some is agreed fact, much is subjective and sometimes false, leading to generative AI “hallucinations” that ultimately undermine users’ trust in generative AI technology.

By some estimates, today’s generative AI could run out of training data as early as 2026. Then what? There’s plenty more untapped data to drive innovation, especially if tomorrow’s generative AI evolves into something different from the generative AI we think of today.

Today’s generative AI is a consumer AI solution, even if consumers use it for work tasks, like creating presentations or summarizing online meetings. Tomorrow’s generative AI is an enterprise generative AI, focused on creating value for organizations in very specific areas, like improving healthcare outcomes, optimizing vaccine delivery, or tracing possible contamination through food chains. This enterprise-class generative AI requires enterprise LLMs – and new, trusted enterprise datasets now being created through ambient IoT.

Ambient IoT Data Augments LLMs

Here's a good way to think about enterprise generative AI and the data required to enable it. If a supply chain manager wants to use today’s generative AI to optimize a supply chain, they may enter a: “How do I?” prompt into an existing chatbot to receive informed answers based on existing LLMs.

With enterprise generative AI, augmented by ambient IoT data, the same supply chain manager can ask how to optimize their supply chain, based on data about their suppliers and the real-time movement and handling of their products and materials through their distribution channels. In other words, the finite universe of existing information about supply chains is augmented with near-infinite ambient IoT data about the actual products and conditions in those supply chains. And the manager isn’t just asking the LLM for information, they’re asking products themselves questions like: “Where are you now? What’s your condition? How can I get you from A to B more efficiently, sustainably and cost-effectively?”

To read the complete article, visit IoT World Today.