2 The Artificial Intelligence for Agriculture Innovation (AI4AI) initiative 2.1 Context and approach 2.2 Vision and objectives 2.3 Structure and process 3 AI4AI key findings 3.1 Analyzing the agriculture start-up landscape for use cases 3.2 Intelligent Crop Planning 3.3 Smart Farming 3.4 Farmgate-to-Fork 3.5 Data-driven Agriculture
agriculture ecosystem, including farming activities and supply chains in the same four categories as in the 2021 paper: – Intelligent crop planning: gene editing and use of artificial intelligence (AI), soil testing-based and sowing-window advice – Smart farming: AI and augmented reality (AR) for crop advice and field planning, hyperlocal
2023年8月11日 · data in agriculture innovation and data governance in the agriculture ecosystem, as well as calls upon all stakeholders to work in synergy towards the widespread adoption of DPI for agriculture. TABLE 1 Digital agriculture economic potential for India A recent study by the World Economic Forum’s Artificial Intelligence for Agriculture Innovation
in the next two years generative AI will give the advantage to defenders over attackers. – Approximately half of executives say that advances in adversarial capabilities (phishing, malware, deepfakes) present the most concerning impact of generative AI on cyber. The cyber-skills and talent shortage continues to widen at an alarming rate.
of social innovators applying AI to tackle climate solutions; and economic empowerment, notably prevalent in lower-income countries where 80% of all initiatives aimed at enhancing livelihoods are based. But AI is also revolutionizing practices in other areas such as agriculture through predictive analytics and precision farming, addressing climate
(AI), are being compounded by economic and geopolitical disruptions and growing social and environmental pressures. This fourth edition of the Future of Jobs Report therefore broadens its scope beyond technological change to also consider and address the labour-market impact of a multitude of concurrent trends, including the green and energy
generative AI governance within their jurisdictions. Through this paper, our AI Governance Alliance: Briefing Paper Series, launched in January 2024, and our events and community meetings, we seek to create a tangible impact in AI literacy and knowledge dissemination. Given the international context in which this
2020年7月1日 · 7 2 Guidance on AI Governance from Global Council Members 9 3 AI Governance Eras: The journey to building trust in AI systems 10 3.1 Pre-2010: AI winter and thaw 10 3.2 2010-2016: AI acceleration presents challenges 11 3.3 2016-2019: Principles and guidelines 11 3.4 2019-present: Continued acceleration and governance innovations
– Generative AI is a branch of machine learning that is capable of producing new text, images and other media, replicating patterns and relationships found in the training data.5 – Foundation models are a type of large-scale, machine-learning model that is trained on diverse multi-modal data at scale and can be
2024年8月7日 · water retention. Innovations in precision agriculture, satellite imagery, sensor technology and artificial intelligence (AI) present excellent opportunities to restore and improve soil quality. While grappling with the threats of soil degradation, biodiversity loss and climate change, it is imperative for everyone to get on board and work together.