The field of soilless agriculture is in a state of perpetual evolution, driven by a convergence of horticultural science, engineering, and information technology. It is a sector defined not by tradition, but by a relentless pursuit of innovation to solve some of the most pressing challenges in modern food production. As the industry matures, the focus of research and development is shifting from simply proving the viability of growing crops without soil to optimizing every facet of the process for maximum efficiency, sustainability, and profitability. This chapter explores the cutting edge of research and innovation that is shaping the future of Controlled Environment Agriculture (CEA). We will begin by examining recent advances in core soilless technologies, from next-generation lighting systems to sustainable substrates. We will then delve into the microscopic world of novel inputs, exploring how nanotechnology and bio-stimulants are revolutionizing plant nutrition and resilience. The transformative impact of artificial intelligence and robotics will be detailed, showcasing the journey towards the fully autonomous, intelligent farm. We will then look to the ultimate frontier of CEA: space agriculture, and consider the future trends it portends for farming on Earth. Finally, we will investigate the crucial role of patents and breakthrough research in protecting intellectual capital and driving the continuous cycle of innovation that defines this dynamic industry.
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...
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
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.
Haonan Qiu, Chaowei Xiao, Lei Yang et al.· arXiv.org· 208 citations· ⚡17
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