Future of Small Language Models
As their functionality increases, SLMs are expected to become more integrated into everyday technology, such as in synthetic personal assistants and more intuitive device interfaces.
Ultimately, the future will provide privacy first, instead of sending all the data to an AI model provider. We'll see on-device usage entirely locally instead of using a lot of computing power on servers.
Uses of small language models
Mobile application
Small language models boost mobile apps by requiring less memory and processing power, making them ideal for smartphones. They enable offline functionalities such as nearly-human chatbots, language translation, text generation, and text summarization. Additionally, they reduce costs by cutting cloud reliance and enhance user experience with faster, on-device processing.
Web browsers
Within web applications, these SLMs can enhance the user experience by providing language-related functions such as auto-completion when typing, grammar correction, and sentiment analysis, which has the capability of spotting emotionally saturated terms and suggesting alternatives. For example, instead of going with a blatant “Just do it..", it could suggest an alternate response, like "Maybe you would consider…”.
IoT devices
In IoT devices, small language models enable functions like voice recognition, natural language processing, and personalized assistance without heavy reliance on cloud services. This optimizes both performance and privacy. Right now, Alexa and other home devices have to consult with international servers to turn your Smart Lights or IoT devices on and off. That should be entirely local, with with no outside consultation, and now it can be.
Edge Computing
Small language models shine in edge computing environments, where data processing occurs virtually at the data source. Deployed on edge devices such as routers, gateways, or edge servers, they can execute language-related tasks in real time. This setup lowers delay and reduces reliance on central servers, improving cost-efficiency and responsiveness.
As businesses continue to navigate the complexities of generative AI, Small Language Models are emerging as a promising solution that balances capability with practicality. They represent a key development in AI’s evolution and offer enterprises the ability to harness the power of AI in a more controlled, efficient, and tailored manner.
The ongoing refinement and innovation in Small Language Model technology will likely play a significant role in shaping the future landscape of enterprise AI solutions.
