Leveraging Time-Series Foundation Models in Smart Agriculture for Soil Moisture Forecasting
The recent surge in foundation models for natural language processing and computer vision has fueled innovation across various domains. Inspired by this progress, we explore the potential of foundation models for time-series forecasting in smart agriculture, a field often plagued by limited data availability. Specifically, this work presents a novel application of $\texttt{TimeGPT}$, a state-of-the-art (SOTA) time-series foundation model, to predict soil water potential ($\psi_\mathrm{soil}$), a key indicator of field water status that is typically used for irrigation advice. Traditionally, this task relies on a wide array of input variables. We explore $\psi_\mathrm{soil}$'s ability to forecast $\psi_\mathrm{soil}$ in: ($i$) a zero-shot setting, ($ii$) a fine-tuned setting relying solely on historic $\psi_\mathrm{soil}$ measurements, and ($iii$) a fine-tuned setting where we also add exogenous variables to the model. We compare $\texttt{TimeGPT}$'s performance to established SOTA baseline models for forecasting $\psi_\mathrm{soil}$. Our results demonstrate that $\texttt{TimeGPT}$ achieves competitive forecasting accuracy using only historical $\psi_\mathrm{soil}$ data, highlighting its remarkable potential for agricultural applications. This research paves the way for foundation time-series models for sustainable development in agriculture by enabling forecasting tasks that were traditionally reliant on extensive data collection and domain expertise.
A citizens' assembly is a group of people who are randomly selected to represent a larger population in a deliberation. While this approach has successfully strengthened democracy, it has certain limitations that suggest the need for assemblies to form and associate more organically. In response, we propose federated assemblies, where assemblies are interconnected, and each parent assembly is selected from members of its child assemblies. The main technical challenge is to develop random selection algorithms that meet new representation constraints inherent in this hierarchical structure. We design and analyze several algorithms that provide different representation guarantees under various assumptions on the structure of the underlying graph.
ChatGPT as the Marketplace of Ideas: Should Truth-Seeking Be the Goal of AI Content Governance?
As one of the most enduring metaphors within legal discourse, the marketplace of ideas has wielded considerable influence over the jurisprudential landscape for decades. A century after the inception of this theory, ChatGPT emerged as a revolutionary technological advancement in the twenty-first century. This research finds that ChatGPT effectively manifests the marketplace metaphor. It not only instantiates the promises envisaged by generations of legal scholars but also lays bare the perils discerned through sustained academic critique. Specifically, the workings of ChatGPT and the marketplace of ideas theory exhibit at least four common features: arena, means, objectives, and flaws. These shared attributes are sufficient to render ChatGPT historically the most qualified engine for actualizing the marketplace of ideas theory.
The comparison of the marketplace theory and ChatGPT merely marks a starting point. A more meaningful undertaking entails reevaluating and reframing both internal and external AI policies by referring to the accumulated experience, insights, and suggestions researchers have raised to fix the marketplace theory. Here, a pivotal issue is: should truth-seeking be set as the goal of AI content governance? Given the unattainability of the absolute truth-seeking goal, I argue against adopting zero-risk policies. Instead, a more judicious approach would be to embrace a knowledge-based alternative wherein large language models (LLMs) are trained to generate competing and divergent viewpoints based on sufficient justifications. This research also argues that so-called AI content risks are not created by AI companies but are inherent in the entire information ecosystem. Thus, the burden of managing these risks should be distributed among different social actors, rather than being solely shouldered by chatbot companies.
Why Algorithms Remain Unjust: Power Structures Surrounding Algorithmic Activity
Algorithms play an increasingly-significant role in our social lives. Unfortunately, they often perpetuate social injustices while doing so. The popular means of addressing these algorithmic injustices has been through algorithmic reformism: fine-tuning the algorithm itself to be more fair, accountable, and transparent. While commendable, the emerging discipline of critical algorithm studies shows that reformist approaches have failed to curtail algorithmic injustice because they ignore the power structure surrounding algorithms. Heeding calls from critical algorithm studies to analyze this power structure, I employ a framework developed by Erik Olin Wright to examine the configuration of power surrounding Algorithmic Activity: the ways in which algorithms are researched, developed, trained, and deployed within society. I argue that the reason Algorithmic Activity is unequal, undemocratic, and unsustainable is that the power structure shaping it is one of economic empowerment rather than social empowerment. For Algorithmic Activity to be socially just, we need to transform this power configuration to empower the people at the other end of an algorithm. To this end, I explore Wright's symbiotic, interstitial, and raptural transformations in the context of Algorithmic Activity, as well as how they may be applied in a hypothetical research project that uses algorithms to address a social issue. I conclude with my vision for socially just Algorithmic Activity, asking that future work strives to integrate the proposed transformations and develop new mechanisms for social empowerment.
