Two major advances have facilitated generative AI at the edge: the trend towards making generative AI more accessible in various environments, including those with limited resources, and the availability of affordable, energy-efficient hardware acceleration for inferencing processes. Examples of potential applications include voice-assisted shopper suggestions, interactive Q&A systems, sentiment analysis, and autonomous decision-making in warehouse environments. The author predicts a rapid uptake of innovative applications built on this emerging technology stack.
Key takeaways:
- Generative AI, which creates new content or mimics the characteristics of input data, is predicted to be the next significant AI application to make an impact at the edge.
- Enterprises are moving computing resources close to where users and devices are located and where data is created, making edge locations ideal for generative AI processes.
- Advancements have made generative AI more accessible and usable in various environments, including those with limited computational resources. This includes tuning large language models (LLMs) for specific tasks or domains and the availability of small form factor computers with integrated GPUs.
- Emerging applications of generative AI on the edge include voice-assisted shopper suggestions in retail environments, interactive question-and-answer systems for restaurant staff, sentiment analysis or language translation in customer feedback contexts, and autonomous decision-making in warehouse environments.