Updates from I2SC

In the forestry, ecology, climate change, conservation, and remote sensing fields, accurately defining what constitutes a forest is essential for effective monitoring, dynamics assessment, and policy formulation in areas focused on carbon offsetting and tracking. A clear, general, and comprehensive understanding of forest characteristics is, therefore, necessary for establishing baselines for reforestation, tracking deforestation, mapping ecological dynamics, and accurately quantifying the achievements of carbon offset goals. In different fields, the forest is defined based on structural, ecological, and functional criteria, and some definitions also consider the socioeconomic benefits that forests provide. Most definitions of forests focus on tree cover, canopy density, area covered, and tree heights [7]. For instance, the Food and Agriculture Organization (FAO) defines forests as areas with a tree cover of more than 10%, a minimum height of 5 meters, and an area of at least 0.5 hecta...

In the era of rapid technological evolution, where required employable skills are expected to change rapidly, higher education institutions (HEIs) need to prepare students for an uncertain and dynamic job market. Large Language Models (LLMs) - tools like GPT-4 - are emerging as a promising way to tackle this challenge by assisting with curriculum mapping and design.   Curriculum mapping is the process of aligning course descriptions, learning activities, and assessments with the skills needed for employment. Traditionally, experts carry out this process manually. However, this manual approach requires considerable resources and faces several difficulties. For instance, experts may interpret employable skills differently, and human error or bias can occur. These issues make it harder to update curriculum maps frequently, even though the job market is rapidly changing.  Recent studies have investigated whether large language models (LLMs) can automate this process [1]. Their fin...

Large Language Models (LLMs) have the ability to act as a specific personality based on a given description. Researchers have used this ability in simulating LLMs as replacement for human participants in user studies, annotation tasks, opinion surveys, and Computational Social Science. When researchers are using LLMs in such a way, they are assuming that LLMs are able to represent the perspectives of different demographic identities because of the vast training data they were trained on. However, the paper by Wang et. al. [1], ' Large language models that replace human participants can harmfully misportray and flatten identity groups ' show that there is an inherent flaw in the current approaches of training these LLMs that make them misportray and flatten representation of identity groups. The authors thus urge caution in use cases where LLMs are intended to replace human participants.  There are mainly three limitations in using LLMs as human participants' replacement: Mi...

  Working with georeferenced data is awesome! But, sometimes, things can get a bit messy, especially without in-depth prior GIS knowledge. In these slides, I try to create an understanding why things can get messy and how avoid main pitfalls. Key takeaways are: - Maps are a simplified 2-D representation of our Earth's surface: There are multiple approaches to get there. - Purpose matters: Do you want to calculate distances or areas? Is your analysis global or local? Do you want to visualise it? Happy sliding! -- Written by Till Koebe

Tiny Troupe is a newly released Python library that can simulate people with specific personalities, powered by LLMs. Released by Microsoft, it is useful in understanding human behaviour by simulating artificial agents called ‘Tiny Persons’ who can interact with each other in a ‘Tiny World’ environment. It can be used to investigate human response in various scenarios not limited to evaluating digital ads, software testing, creating synthetic data, project management or brainstorming. As someone interested in human behavior—why people make certain choices or react in specific ways—I was particularly drawn to exploring how TinyTroupe could provide insights into decision-making and dialogue dynamics. I was also curious to test Godwin’s Law, which states: “As an online discussion grows longer, the probability of a comparison involving Nazis or Hitler approaches 1.” Using TinyTroupe, I started simulating various scenarios to better understand how personas would react in polarising discuss...