Sep 2026· AI-Driven Plant Biotechnology· pp. 279-306
Abstract
With plant nanotechnology advances, artificial intelligence (AI) models are becoming more and more important, especially when it comes to optimizing nanomaterials for use in agriculture and enabling advancements in precision agriculture, plant biology, and sustainable food production. With an emphasis on the design, delivery, and safety profiles of nanomaterials, this abstract explores the incorporation of AI-driven methodologies to improve their effectiveness in plant systems. Researchers can improve nutrient delivery systems, optimize nanoparticle synthesis, and forecast interactions between nanoparticles and plant tissues by utilizing machine learning algorithms. In the face of global challenges, this synergistic relationship between AI and nanotechnology promises to produce creative solutions for crop improvement, pest resistance, and sustainable agricultural practices, ultimately enhancing food security. AI models are being used to optimize nanomaterials for plant growth, disease resistance, and environmental adaptation.
This paper investigates developing real-world perception for active agents, proposes Gibson Environment for this purpose, and showcases a set of perceptual tasks learned therein.
F. Xia, Amir Zamir, Zhi-Yang He et al.· 2018 IEEE/CVF Conference on...· 1,069 citations· ⚡112
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...
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An algorithm is proposed which leverages disentangled semantic factors to generate adversarial perturbation by altering controlled semantic attributes to fool the learner towards various "adversarial" targets.
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This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.
This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.
This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...
Masoud Mohseni, Artur Scherer, K. Johnson et al.· arXiv.org· 121 citations· ⚡9
