Meta Changing Scenario With Small AI Language Models

Large language models of artificial intelligence continue to make headlines, but the focus is on small language models. At least that’s what Meta is betting on, according to an article recently published by a group of its research scientists.

Large language models such as ChatGPT, Gemini, and Lama can use billions, even trillions of parameters to produce results. The size of these models makes them too large to work on mobile devices. So, as the Meta researchers noted in their study, there is a growing need for efficient large language models for mobile devices — a need driven by rising cloud technology costs and latency issues.

In their study, the scientists explained how they created high-quality large language models with fewer than a billion parameters, which they believe is a good size for mobile deployment.

Contrary to popular belief, which emphasizes the key role of the amount of data and parameters in determining the quality of the model, scientists have achieved results using their small language model, comparable in some areas to Meta’s Llama LLM.

According to Nick DeGiacomo, CEO of Bucephalus, an AI-powered e-commerce supply chain platform, there’s a prevailing paradigm that ‘bigger is better,’ but this is showing it’s really about how parameters are used. This paves the way for more widespread adoption of on-device AI.

The Meta study is important because it challenges the current norm of cloud-based AI, in which data is often processed in remote data centers, explained Darian Shimi, CEO and founder of FutureFund.

By embedding artificial intelligence data processing into the device itself, Meta is changing the scenario by potentially reducing carbon dioxide emissions associated with data transmission and processing in massive, energy-consuming data centers, and turning device-based artificial intelligence into a key player in the technology ecosystem.

Meta scientists have also taken a significant step in downsizing a language model. Nishant Neekhra, senior director of mobile marketing at Skyworks Solutions, a semiconductor company, says that Meta are proposing a model shrink by order of magnitude, making it more accessible for wearables, hearables, and mobile phones.

One area where small language models could have a meaningful impact is in medicine.

By demonstrating that effective SLMs can have fewer than a billion parameters and still perform comparably to larger models in certain tasks, Danielle Kelvas, a physician advisor with IT Medical, a global medical software development company says, the researchers are opening the door for widespread adoption of AI in everyday health monitoring and personalized patient care.

Kelvas explained that using SLMs can also ensure that sensitive health data can be processed securely on a device, enhancing patient privacy. They can also facilitate real-time health monitoring and intervention, which is critical for patients with chronic conditions or those requiring continuous care.

Meta’s focus on small AI models for mobile devices reflects a broader industry trend towards optimizing AI for efficiency and accessibility. This shift not only addresses practical challenges but also aligns with growing concerns about the environmental impact of large-scale AI operations.

By championing smaller, more efficient models, Meta is setting a precedent for sustainable and inclusive AI development.

Small language models also fit into the edge computing trend, which is focusing on bringing AI capabilities closer to users. Specialized, tuned models can be more efficient and cost-effective for specific tasks.

As on-device AI becomes more capable, the necessity for continuous internet connectivity diminishes, which could dramatically shift the tech landscape in regions where internet access is inconsistent or costly. This could democratize access to advanced technologies, making cutting-edge AI tools available across diverse global markets.

While Meta is leading the development of SLMs, Yashin Manraj, CEO of Pvotal Technologies, noted that developing countries are aggressively monitoring the situation to keep their AI development costs in check. China, Russia, and Iran seem to have developed a high interest in the ability to defer compute calculations on local devices, especially when cutting-edge AI hardware chips are embargoed or not easily accessible.

He predicted that this is not expected to be an overnight or drastic change because complex, multi-language queries will still require cloud-based LLMs to provide cutting-edge value to end users. However, this shift towards allowing an on-device ‘last mile’ model can help reduce the burden of the LLMs to handle smaller tasks, reduce feedback loops, and provide local data enrichment.