What’s a full-spectrum AI?

There’s growing demand for AI that goes beyond chatbots and simple tools. As such, we think full-spectrum AI is the future.  It’s why we’ve built comprehensive models, covering LLM, SLM, Time series and NLP. We know that forward-thinking businesses will need an integrated system covering all major AI modalities; a true ecosystem.

Why is full-spectrum AI the future?

The limitations of single-purpose models are pretty clear. You wouldn’t ask a chatbot to do complex forecasting or a research model to solve a tense customer service issue. However, more use cases are emerging where AI needs to reason across data types and tasks. This is especially true in sectors where we work, like traditional farming, controlled environments, research and biotech. So what do each of these AI types do, and why is it so important for them to work together?

LLM

Large Language Models or LLMs are great for general-purpose reasoning, summarisation and generation. When you need to bounce ideas off AI, create things, process documents or follow a reasoning path, LLMs are at the forefront of innovation. Think GPT-4 and Gemini. But they can hallucinate, and many of them are black boxes.

SLM

Small Language Models are faster, more fine-tuned models that do specific things. They’re great for on-device inference, edge AI and specific workflow automation. They have a small pool of data to pull from and, as a result, stay in their lane. For example, on mobile devices, “SwiftKey and Gboard [utilise] SLMs to provide contextually accurate text suggestions, which improves typing speed and accuracy.” But that’s all they can do. Just predict text.

Time Series Models

These models are great for prediction over time-based data, like with crops. This AI is used for forecasting, anomaly detection, demand planning and financial analysis tasks. You’ll find it in Facebook’s Prophet, for example.

Traditional NLP Models

And finally, we’ve got natural language processing. This is a branch of AI good for token classification, entity extraction, sentiment and intent detection used in structured text tasks and pre-LLM pipelines. It’s faster, cheaper and more interpretable for many use cases. But these days, it’s usually layered with an LLM in tools like Siri, Alexa and the Google Assistant.

Why you need full-spectrum AI

In the spaces where we operate, full-spectrum is becoming the norm. Here are two examples of why an all-in-one ecosystem is helpful. In each, imagine the value of all that shared data under a single view:

Traditional farming

LLM

Offers conversational support for questions like “When should I plant corn?” based on local agronomic data, weather and best practices.

SLM

On-device SLM in the farmer’s smartphone gives offline pest identification, even when they’re in rural fields.

Time Series

Using past sensor data, rainfall and satellite imagery, the time series can help them optimise planting and irrigation schedules.

NLP

It’s able to parse extension service reports to detect trends in disease outbreaks locally so they can prepare.

Research 

LLM

It can summarise hundreds of plant genomics papers, identifying potential gene targets or biotech opportunities for researchers.

SLM

Field researchers can securely input structured phenotypic data via voice or shorthand, which is then formatted locally.

Time Series

They can track growth curves over time under various conditions, providing detailed summaries & forecasts.

NLP

This can extract key findings from thousands of lab notebooks, PDF papers and internal reports using text mining.

With so many use cases for integrated data, it’s clear why full-spectrum AI is the future. If you’ve already got a deployment, is it time to look at your AI maturity and explore full-spectrum opportunities? Let’s talk about that.