The world's small-scale farmers now can see a path to solving global hunger over the next decade, with solutions – such as adopting climate-resilient crops through improving extension services – all culled rapidly via artificial intelligence from more than 500,000 scientific research articles. The results are synthesized in 10 new research papers – authored by 77 scientists, researchers and librarians in 23 countries – as part of Ceres2030: Sustainable Solutions to End Hunger. The project is headquartered at Cornell, with partners from the International Food Policy Research Institute (IFPRI) and the International Institute for Sustainable Development (IISD). The papers were published concurrently on Oct. 12 in four journals – Nature Plants, Nature Sustainability, Nature Machine Intelligence and Nature Food – and assembled in a comprehensive package online: Sustainable Solutions to End Hunger. Ceres2030 employed machine learning, librarian savvy and research synthesis methods to quickly scan a trove of thousands of scientific journals for ideas and websites from more than 60 agencies that can help eradicate world hunger.

Machine learning is an increasingly prevalent buzzword in the media. Its applications in science and the private sector are frequently discussed--but what about global development? Can it also help advance fields like health, agriculture, and financial inclusion? That's because it can help us uncover previously invisible patterns in data, to identify the most effective solutions and target them in the right way. Machine learning (ML) has been around for decades, but now is our chance to apply it to development challenges in new ways, for three reasons.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

When U.N. member states unanimously adopted the 2030 Agenda in 2015, the narrative around global development embraced a new paradigm of sustainability and inclusion--of planetary stewardship alongside economic progress, and inclusive distribution of income. This comprehensive agenda--merging social, economic and environmental dimensions of sustainability--is not supported by current modes of data collection and data analysis, so the report of the High-Level Panel on the post-2015 development agenda called for a "data revolution" to empower people through access to information.1 Today, a central development problem is that high-quality, timely, accessible data are absent in most poor countries, where development needs are greatest. In a world of unequal distributions of income and wealth across space, age and class, gender and ethnic pay gaps, and environmental risks, data that provide only national averages conceal more than they reveal. This paper argues that spatial disaggregation and timeliness could permit a process of evidence-based policy making that monitors outcomes and adjusts actions in a feedback loop that can accelerate development through learning. Big data and artificial intelligence are key elements in such a process. Emerging technologies could lead to the next quantum leap in (i) how data is collected; (ii) how data is analyzed; and (iii) how analysis is used for policymaking and the achievement of better results. Big data platforms expand the toolkit for acquiring real-time information at a granular level, while machine learning permits pattern recognition across multiple layers of input. Together, these advances could make data more accessible, scalable, and finely tuned. In turn, the availability of real-time information can shorten the feedback loop between results monitoring, learning, and policy formulation or investment, accelerating the speed and scale at which development actors can implement change.

Semantic segmentation of land cover classes is fundamental for agricultural and economic development work, from sustainable forestry to urban planning, yet existing training datasets have significant limitations. To generate an open and comprehensive training library of high resolution Earth imagery and high quality land cover classifications, public Sentinel-2 data at 10 m spatial resolution was matched with accurate GlobeLand30 labels from 2010, which were filtered by agreement with an intermediary Sentinel-2 classification at 20 m produced during atmospheric correction. Scene-level classifications were predicted by Random Forests trained on valid reflectance data and the filtered labels, and achieved over 80% model accuracy for a variety of locations. Further work is required to aggregate individual scene classifications for annual labels and to test the approach in more locations, before crowdsourcing human validation. The goal is to create a sustained community-wide effort to generate image labels not only for land cover, but also very specific images for major agriculture crops across the world and other thematic categories of interest to the global development community.

The advent of automation means we went from foraging grub with our nails millions of years ago to regularly flying across nations. Automation has been heralded as the freer of time, opening a world of creativity over mindless drone-like activities. While automation certainly has eased many tasks, it has also offered a new set of complex problems mostly due to adaptation. Humans want changes, but are we ready for the automation of all things? Moreover, what does this mean for the future of electric mobility and how will we use it?

SHARJAH: The Minister of Tolerance, Sheikh Nahyan Bin Mubarak Al Nahyan, said that UAE's leaders strive to achieve a successful and prosperous future for the country, to keep abreast with the global developments in knowledge and technology, as well as to adopt the cutting-edge technology and practices in all spheres of life. This came during the opening speech of the 18th edition of Dar Al Khaleej's Annual Conference that was held on Saturday under the title "Artificial Intelligence Strategy in the UAE", in the presence of Khalid Abdullah Taryam, Chairman of Dar Al Khaleej for Press, Printing and Publishing, and Dr Yousef Al Hassan, an Emirati writer and thinker. "The conference's theme exactly reflects what the two late brothers (Taryam Omran Taryam and Dr. Abdullah Omran Taryam) were seeking to fulfill by enabling the community to be aware of the future developments and work hard to shape its features, so that the UAE will be able to contribute effectively to all the achievements global development," Sheikh Nahyan said. "The conference's discussion of Artificial Intelligence in UAE embodies what we learned from the founding leader late Sheikh Zayed Bin Sultan Al Nahyan and his wisdom to use the latest in the world in terms of advanced systems and techniques. This approach has been continued under the leadership of President His Highness Sheikh Khalifa Bin Zayed Al Nahyan, His Highness Sheikh Mohammed Bin Rashid Al Maktoum, Vice President and Prime Minister of the UAE and Ruler of Dubai, His Highness Sheikh Mohamed Bin Zayed Al Nahyan, Crown Prince of Abu Dhabi and Deputy Supreme Commander of the UAE Armed Forces and Their Highnesses Rulers of the Emirates," Sheikh Nahyan said.

Advances in sensor technology, cloud computing, and machine learning (ML) continue to converge to accelerate innovation in the field of remote sensing. However, fundamental tools and technologies still need to be developed to drive further breakthroughs and to ensure that the Global Development Community (GDC) reaps the same benefits that the commercial marketplace is experiencing. This process requires us to take a collaborative approach. Data collaborative innovation -- that is, a group of actors from different data domains working together toward common goals -- might hold the key to finding solutions for some of the global challenges that the world faces. That is why Radiant.Earth is investing in new technologies such as Cloud Optimized GeoTiffs, Spatial Temporal Asset Catalogues (STAC), and ML. Our approach to advance ML for global development begins with creating open libraries of labeled images and algorithms.

This week, as leaders gather in Davos, Switzerland, to discuss how to "create a shared future in a fractured world," many of the conversations will center on the role of humans and robots in a future of automation or augmentation. The teaser for a breakfast conversation that Microsoft is hosting on the promise and pitfalls of artificial intelligence captures the challenges and the opportunity well: "AI offers profound potential benefits and the opportunity to help tackle some of the world's most pressing issues including accelerating economic growth, tackling the urgent issues of environmental sustainability, and transforming healthcare," it reads. "But the accelerating pace of technology-driven change is also creating disruption and anxiety. It risks contributing to a sense of a fractured world, between a small group of people who benefit and a broader group of people who fear that they are being left behind. We need to come together to chart a path forward that ensures AI contributes to building a positive shared future for every community."

This week, as leaders gather in Davos, Switzerland, to discuss how to "create a shared future in a fractured world," many of the conversations will center on the role of humans and robots in a future of automation or augmentation. The teaser for a breakfast conversation that Microsoft is hosting on the promise and pitfalls of artificial intelligence captures the challenges and the opportunity well: "AI offers profound potential benefits and the opportunity to help tackle some of the world's most pressing issues including accelerating economic growth, tackling the urgent issues of environmental sustainability, and transforming healthcare," it reads. "But the accelerating pace of technology-driven change is also creating disruption and anxiety. It risks contributing to a sense of a fractured world, between a small group of people who benefit and a broader group of people who fear that they are being left behind. We need to come together to chart a path forward that ensures AI contributes to building a positive shared future for every community."