How AI models can fight climate change

Artificial intelligence (AI) models such as ChatGPT are well known worldwide. These “foundational models” sparked global excitement and interest in the potential of AI. The next step in this effort is sector-specific AI models that can be tailored to specific industries. Such models can also be used to address development challenges such as climate change.

For example, Agrepreneur has developed an AI-powered Agrypreneur platform that provides real-time advice on farm management to smallholder farmers, including how to optimize available resources and prevent crop diseases. Machine learning algorithms are also used for credit rating. Machine learning is also used to help farmers predict the amount of produce needed for a crop, allowing them to streamline the procurement process.

Viamo is also an AI-driven solution. Available through voice calls, farmers in areas with limited or no internet connectivity can receive guidance on sustainable farming practices. Using natural language understanding technology and pre-trained large-scale language models, text-to-speech and text-to-speech capabilities, farmers can get critical information from the app.

ClimateGPT is another example. Trained on multidisciplinary research to provide users with a holistic understanding of climate change.

Sector-specific AI models are particularly useful in Asia and the Pacific, which are highly susceptible to extreme weather events due to climate change. Users can also respond on a small scale by creating models that tell them the likelihood of flooding or drought occurring in a specific area at a specific time.

So how can this technology be leveraged to improve the planet's resilience? The benefits of sector-specific models are that they require millions of dollars in initial capital, human resources, and government support. Unlike other tools, it has relatively minimal requirements. Whether you develop as an individual or as a representative of an organization, it is possible to build sector-specific models. Your requirements may be higher depending on the size of your dataset and the complexity of your model or application, but you can build simple requirements using a laptop and a small dataset to train your model.

An AI model can be built through a series of steps, including defining the scope and purpose, establishing clear goals and parameters, and preparing and dividing the training data into smaller units. You can then customize it using platforms and frameworks like GitHub and Hugging Face, rather than starting from scratch. Following customization, the model can be trained on the data using feedback mechanisms and metrics for continuous evaluation and fine-tuning to ensure accuracy and consistency. Multiple iterations may be required to optimize the model response. A beta testing phase that involves a diverse group of users can be used to validate the model's functionality and check for bias. This improves the reliability of the model before widespread deployment. Depending on the complexity of the model, available resources and data, and programming language proficiency, it can take anywhere from hours to weeks to be able to deploy a working climate model.

AI, like any form of technology, has drawbacks and risks. We need to adhere to currently developed and mainstream responsible AI frameworks. Over the next year or two, we will see smaller models working under these responsible AI frameworks, similar to what we've seen with guardrails around the internet for e-commerce, social networking, and the dark web. It will be.

Users developing their own sector-specific models should be aware that AI models are highly data-dependent. Poor quality data means poor quality analysis. Additionally, data bias can be reflected in what AI models produce in bulk. For example, using gender-blind her data to train AI models could be detrimental to women, who face unique challenges during a crisis.

AI acts as a power multiplier for development agencies to do their work. In the context of climate change, sector-specific AI models are being used to accelerate progress on climate action, adapt to a changing climate and solve contemporary adaptation problems, and improve the overall impact of climate change across the planet. can be reflected. Depending on the model, this technology can be used to show global trends, or technology developed for one country can be customized for use in other countries. The presence or absence of these models can be the difference between resilience and vulnerability to climate change.

Sector-specific models such as climate change offer a promising path to harnessing the potential of AI. Once developed, these models could become a key aspect of AI solutions that reorient the planet's sustainability and resilience.