Science

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ClimateEvidence-backed problem

South Park has AI datacentres, sentient penises and a billionaire problem – in more ways than one

A little over a year ago, South Park creators Trey Parker and Matt Stone renewed their deal with Paramount after a contentious negotiation that held up production of the 2025 season. That $1.5bn agreement raised questions about whether Paramount, newly owned by David Ellison, would really offer the creators creative freedom – or was the billionaire class solidifying its hold over a comedy series as cover to meddle in other divisions, like CBS News? The deal also made Parker and Stone likely billionaires themselv…

The Guardian

Both readings

Making scents: could AI help perfumers take pressure off endangered plants?

Evidence-backed gain

Machine Learning and Statistical Models for Genomic Selection of Phytophthora Fruit Rot Resistance in Watermelon

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Exploring Conversational Dynamics in Scientific and Pseudoscientific Health Communities on YouTube: Process Mining and Network Analysis Study

Both readings

Bias and Reliability of AI-Based Peer Review: A Comparative Study of ChatGPT and Claude Evaluating Scientific Abstracts

Background The use of artificial intelligence (AI) models as reviewers of scientific content raises concerns about potential biases related to author identity and about the reproducibility of their evaluations. We assessed whether AI-based reviewers exhibit gender or geographic bias and evaluated the reproducibility of their scoring of scientific abstracts. Methods We randomly selected 10 general internal medicine journals indexed in the Journal Citation Reports (impact factor ≥ 1.5). For each journal, five orig…

Both readings

Petal to the metal: The slow road to automating large-scale phenology labeling for herbarium specimens

Premise Herbarium specimens represent critical historical records of plant phenology, yet automating annotation of reproductive structures remains challenging given the diversity of floral morphologies, specimen age and quality, and image quality. Methods Here, we present a machine learning pipeline that uses an ensemble modeling approach to detect flowers on herbarium specimens and deliver these data to the phenology research community. After testing multiple strategies for generating training data, we found th…

Evidence-backed gain

Machine Learning Outperforms Deep Learning for Atmospheric Attenuation Prediction in Free-Space Optical Communications under Iraqi Weather Conditions

Free-space optical communications systems offer high bandwidth, increased security and license-free operation but are highly affected by the performance degradation due to the atmospheric attenuation caused by scattering and absorption. The prediction of attenuation accuracy is even more important in Iraq where the environment is hot, dusty, foggy and rainy in a random fashion. The aim of this study is to assess the performance of machine learning, deep learning and hybrid modeling techniques for the prediction…

Evidence-backed gain

Beyond replacement: human-machine collaboration in the age of AI

Purpose The purpose is to advance the understanding of human-machine (H-M) collaboration in service industries, conceptualizing a framework that structures the research space and proposing a research agenda to guide future studies on optimizing collaboration dynamics, outcomes and ethical governance. Design/methodology/approach The authors use an artificial intelligence (AI)-based systematic literature based on the SERVSIG Literature Alert database to identify articles related to H-M collaboration. Insights from…

Science

From Rhizosphere to Resistance: Microbe-Plant Interactions in Eco-Smart Biocontrol

The increasing limitations of chemical pesticides such as environmental pollution, pathogen resistance, and threats to human and ecosystem health have increased the demand for sustainable, biologically based crop protection methods. Eco-smart biocontrol has emerged as a game-changing paradigm that uses beneficial microorganisms associated with plants to suppress phytopathogens, boost plant immunity, and make agroecosystems more resilient over time. Moving beyond traditional single-strain biocontrol, eco-smart bi…

Science

Predicting Hormesis Effects of GenX in Zebrafish via Interpretable Machine Learning: Insights From SHAP Analysis

Hexafluoropropylene oxide-dimer acid (GenX), a prominent alternative to legacy per- and polyfluoroalkyl substances (PFAS), poses a significant challenge to traditional linear risk assessment models due to its ability to induce hormesis-a biphasic "low-dose stimulation, high-dose inhibition" response. This study established an interpretable machine learning (ML) framework to identify and predict GenX-induced non-monotonic dose-response (NMDR) relationships in zebrafish (Danio rerio). By integrating 263 independen…

Climate

Artificial Intelligence and Climate Risk Shocks

Artificial intelligence (AI) has been widely applied across various fields and has demonstrated effectiveness to some extent. However, some scholars have raised concerns about its ethical implications and potential rebound effects, particularly in the context of climate issues. To address these debates, we obtained cross-national panel data for 51 countries from 1996 to 2023 through the ISETS Energy Finance Network, WIPO, and World Bank databases. A two-way fixed effects model was used to examine the relationshi…

Science

Artificial Intelligence in Organic Synthesis

Artificial intelligence (AI) is rapidly reshaping organic synthesis; nevertheless, currently most laboratory practice still remains driven by human intuition, trial-and-error optimization, and manual interpretation of analytical data. Here, we synthesize recent advances that move AI from isolated demonstrations to a practical toolkit spanning the full experimental cycle: molecular design and prioritization, computer-assisted synthesis planning and route selection, catalyst and condition optimization, and AI-enab…

Science

AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences

The AlphaFold Database Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk) has significantly impacted structural biology by amassing over 214 million predicted protein structures, expanding from the initial 300k structures released in 2021. Enabled by the groundbreaking AlphaFold2 artificial intelligence (AI) system, the predictions archived in AlphaFold DB have been integrated into primary data resources such as PDB, UniProt, Ensembl, InterPro and MobiDB. Our manuscript details subsequent enh…

Science

On the average-case complexity of learning output distributions of quantum circuits

In this work, we show that learning the output distributions of brickwork random quantum circuits is average-case hard in the statistical query model. This learning model is widely used as an abstract computational model for most generic learning algorithms. In particular, for brickwork random quantum circuits on n qubits of depth d , we show three main results:– At super logarithmic circuit depth d=ω(log⁡(n)) , any learning algorithm requires super polynomially many queries to achieve a constant probability of…

Science

Artificial muses: Generative artificial intelligence chatbots have risen to human-level creativity

A widespread view is that Artificial Intelligence cannot be creative. We tested this assumption by comparing human-generated ideas with those generated by six Generative Artificial Intelligence (GAI) chatbots: alpa.ai, Copy.ai, ChatGPT (versions 3 and 4), Studio.ai, and YouChat. Humans and a specifically trained AI independently assessed the quality and quantity of ideas. We found no qualitative difference between AI and human-generated creativity, although there are differences in how ideas are generated. Inter…