10 Things Every BA Must Know About Small Language Models (SLMs)

As the world marches into Large Language Models (LLMs) such as ChatGPT from OpenAI, a new type of model has emerged, which can be a much more cost-effective option for organizations. These are called Small Language Models (SLMs).

Small Language Models (SLMs) are compact, cost-effective, and privacy-oriented AI models that can run directly on personal devices like phones and smart glasses, unlike larger AI models that require cloud-based infrastructure. This is a game changer in that LLMs require significant investment into large data infrastructure. You are helping Mother Earth with less carbon emission.

SLMs are tailored to specific tasks, offering efficient, localized AI assistance for tasks like translation, coding help, or fitness tracking, without needing constant internet connectivity. Applying Pareto’s principle, we would perceive SLMs to be more effective in 90% of cases than LLMs.

The portability of SLMs allows them to be used in diverse environments, such as while traveling or in remote areas, making them more accessible and practical for real-time applications. This ability to work without being connected to the internet is a huge benefit in many parts of the world, where the cost of accessing the internet remains considerably high even today.

SLMs are becoming increasingly important in industries like education, healthcare, and retail, offering businesses the ability to fine-tune AI models for specific customer needs at a fraction of the cost of large models. This is a huge benefit for underdeveloped countries, especially in Africa and Asia.

They offer enhanced privacy by processing data locally, ensuring that user data stays secure on personal devices without being shared with cloud services, making them ideal for privacy-sensitive sectors like healthcare and finance. Remember, the data you share with LLMs becomes public information, and there is always a possibility that the data will be used for purposes unknown to the users. SLMs offer much better privacy.

Unlike large language models, SLMs are energy-efficient and environmentally sustainable, reducing the carbon footprint associated with AI operations. The need for energy consumption by data centers keeps going up, and SLMs offer value at a minimal cost.

In the field of education, small language models like Khan Academy's Khan Migo are revolutionizing learning by providing personalized, 24/7 tutoring tailored to individual student needs. This is, again, an amazing boon for the entire learning community.

SLMs can be used in wearable devices like Meta's Ray-Ban smart glasses, allowing users to interact with AI for tasks like language translation and real-time assistance while on the go. This makes SLMs far better options for implementing on phones and wearables.

These models can handle tasks like logic puzzles, coding, and memory-constrained operations, making them useful for developers and businesses to run AI locally on devices with minimal resource demands. This is indeed very helpful for focused, productive work.

The future of AI is not just about scaling up models, but rather scaling down to deliver more personalized, accessible, and privacy-conscious solutions through small language models.

What are your thoughts on the SLMs?