Artificial Intelligence (AI) has been widely successful and effective in optimizing tasks across various fields such as smart agriculture, healthcare, and education, positioning it as a key enabler in advancing the Sustainable Development Goals (SDGs) set forth by the United Nations. Understanding the perspectives of industry and academic experts in domains such as AI, data science, biotechnology, natural and physical sciences, can provide valuable multidisciplinary insights into the responsible usage of AI. This study analyses 12 opinion essays […]

Ver mais

A popular topic in Silicon Valley is talking about what year humans and machines will merge (or, if not, what year humans will get surpassed by rapidly improving AI or a genetically enhanced species). Most guesses seem to be between 2025 and 2075. People used to call this the singularity; now it feels uncomfortable and real enough that many seem to avoid naming it at all. Perhaps another reason people stopped using the word “singularity” is that it […]

Ver mais

Farmers in remote areas need quick and reliable methods for identifying plant diseases, yet they often lack access to laboratories or high-performance computing resources. Deep learning models can detect diseases from leaf images with high accuracy, but these models are typically too large and computationally expensive to run on low-cost edge devices such as Raspberry Pi. Furthermore, collecting thousands of labeled disease images for training is both expensive and time-consuming. This paper addresses both challenges by combining neural […]

Ver mais

Large language models (LLMs) are now routine writing tools across various domains, intensifying questions about when text should be treated as human-authored, artificial intelligence (AI)-generated, or collaboratively produced. This rapid review aimed to identify cue families reported in empirical studies as distinguishing AI from human-authored text and to assess how stable these cues are across genres/tasks, text lengths, and revision conditions. Following PRISMA guidelines, we searched four online databases for peer-reviewed English-language empirical articles (1 January 2022–1 January […]

Ver mais

Teilhard casts scientific research as humanity’s next evolutionary pulse—our collective mind awakening to shape its own future. He argues that the modern hunger to know and create is nothing less than evolution reflecting on itself, a divine power working through human inquiry. For him, research becomes sacred: the meeting point where faith in God and faith in human potential fuse into a single, world-transforming spiritual force.

Ver mais

arXiv:2405.19206v2 Announce Type: replace Abstract: Deep neural networks (DNNs) on Riemannian manifolds have garnered increasing interest in various applied areas. For instance, DNNs on spherical and hyperbolic manifolds have been designed to solve a wide range of computer vision and nature language processing tasks. One of the key factors that contribute to the success of these networks is that spherical and hyperbolic manifolds have the rich algebraic structures of gyrogroups and gyrovector spaces. This enables principled and effective […]

Ver mais

Why You Need a Truncated Mean In data analysis, the standard AVERAGE function is a workhorse, but it has a significant weakness: it is highly susceptible to distortion from outliers. A single extreme value, whether high or low, can skew the entire result, misrepresenting the data’s true central tendency. This is where the truncated mean becomes essential. It provides a more robust measure of the average by excluding a specified percentage of the smallest and largest values from […]

Ver mais

We study contextual multi-armed bandit problems where the context comes from a metric space and the payoff satisfies a Lipschitz condition with respect to the metric. Abstractly, a contextual multi-armed bandit problem models a situation where, in a sequence of independent trials, an online algorithm chooses, based on a given context (side information), an action from a set of possible actions so as to maximize the total payoff of the chosen actions. The payoff depends on both the […]

Ver mais

arXiv:2601.04301v1 Announce Type: new Abstract: As frontier AI systems are pretrained on web-scale data, test set contamination has become a critical concern for accurately assessing their capabilities. While research has thoroughly investigated the impact of test set contamination on discriminative evaluations like multiple-choice question-answering, comparatively little research has studied the impact of test set contamination on generative evaluations. In this work, we quantitatively assess the effect of test set contamination on generative evaluations through the language model lifecycle. […]

Ver mais