In 2017 the World Economic Forum's Centre for the Fourth Industrial Revolution teamed up with the Government of Rwanda to draft the world's first framework for governing drones at scale. Using a performance-based approach that set minimum safety requirements instead of equipment specifications, this innovative regulatory framework gave drone manufacturers the flexibility to design and test different types of drones. These drones have delivered life-saving vaccines, conducted agricultural land surveys, inspected infrastructure and had many other socially beneficial uses in Rwanda.

On fields across the world, phones, tablets, drones, and other technologies are changing how food is grown. Through these devices, artificial intelligence (AI)--technology able to perform tasks that require human intelligence--may help farmers use the techniques they already know and trust on a bigger scale. And Big Data--data sets that reveal telling patterns about growth, yield, weather, and more--may help farmers make better decisions before crises strike. According to the report Refresh: Food Tech, From Soil to Supper released in 2018, AI and Big Data may help produce more food, use less water, limit resource consumption, redirect food waste, and lower food prices--all while improving the lives and incomes of farmers and food producers. "Recent advances have the potential for big breakthroughs in the ways we grow, store, transport, distribute, and consume food," says the Refresh Report.

By the end of this decade, a milestone is reached in artificial intelligence, with computers now routinely passing the Turing Test.** This test is conducted by a human judge who is made to engage in a natural language conversation with one human and one machine, each of which tries to appear human. Participants are placed in isolated locations. For several decades, information technology had seen exponential growth – leading to vast improvements in computer processing power, memory, bandwidth, voice recognition, image recognition, deep learning and other software algorithms. By the end of the 2020s, it has reached the stage where an independent judge is literally unable to tell which is the real human and which is not.* Answers to certain "obscure" questions posed by the judge may appear childlike from the AI – but they are humanlike nonetheless.*

On the other hand, Tropical race 4 (TR4), the virulent strain of fungus Fusarium oxysporum cubense that is threatening banana crop globally with the fusarium wilt disease has killed off millions of bananas in Africa and Asia (from the 1980s onwards). It had surfaced in the Cavendish group of bananas in parts of Bihar and is now spreading to Uttar Pradesh, Madhya Pradesh and even Gujarat, which could spell havoc for the country's banana industry. Even though India is the largest banana producing country in the world and third-largest orange-producing country, our exports for these crops are mainly to the Middle East and some neighbouring countries. Very little, if not any, is exported to the US or EU. In spite of such high production, India's banana exports generate a meager$49.8

--Electromagnetic stimulation of the human brain is a key tool for the neurophysiological characterization and diagnosis of several neurological disorders. Transcranial magnetic stimulation (TMS) is one procedure that is commonly used clinically. However, personalized TMS requires a pipeline for accurate head model generation to provide target-specific stimulation. This process includes intensive segmentation of several head tissues based on magnetic resonance imaging (MRI), which has significant potential for segmentation error, especially for low-contrast tissues. Additionally, a uniform electrical conductivity is assigned to each tissue in the model, which is an unrealistic assumption based on conventional volume conductor modeling. This paper proposes a novel approach to the automatic estimation of electric conductivity in the human head for volume conductor models without anatomical segmentation. A convolutional neural network is designed to estimate personalized electrical conductivity values based on anatomical information obtained from T1-and T2-weighted MRI scans. This approach can avoid the time-consuming process of tissue segmentation and maximize the advantages of position-dependent conductivity assignment based on water content values estimated from MRI intensity values. The computational results of the proposed approach provide similar but smoother electric field results for the brain when compared to conventional approaches. In electromagnetic dosimetry applications, the use of computational models that imitate human anatomy is an essential process [1].

Propaganda aims at influencing people's mindset with the purpose of advancing a specific agenda. Previous work has addressed propaganda detection at the document level, typically labelling all articles from a propagandistic news outlet as propaganda. Such noisy gold labels inevitably affect the quality of any learning system trained on them. A further issue with most existing systems is the lack of explainability. To overcome these limitations, we propose a novel task: performing fine-grained analysis of texts by detecting all fragments that contain propaganda techniques as well as their type. In particular, we create a corpus of news articles manually annotated at the fragment level with eighteen propaganda techniques and we propose a suitable evaluation measure. We further design a novel multi-granularity neural network, and we show that it outperforms several strong BERT-based baselines.

A team of scientists led by researchers at the University of Georgia Center for Food Safety in Griffin has developed a machine-learning approach that could lead to quicker identification of the animal source of certain Salmonella outbreaks. In the research, published in the January 2019 issue of Emerging Infectious Diseases, Xiangyu Deng and his colleagues used more than a thousand genomes to predict the animal sources, especially livestock, of Salmonella Typhimurium. Deng, an assistant professor of food microbiology at the center, and Shaokang Zhang, a postdoctoral associate with the center, led the project, which also included experts from the Centers for Disease Control and Prevention, the U.S. Food and Drug Administration, the Minnesota Department of Health and the Translational Genomics Research Institute. According to the Foodborne Disease Outbreak Surveillance System, close to 3,000 outbreaks of foodborne illness were reported in the U.S. from 2009 to 2015. Of those, 900 -- or 30 percent -- were caused by different serotypes of Salmonella, including Typhimurium, Deng said.

This year, Europe's banks plan to make technology investments worth in aggregate $77 billion, according to consulting firm Celent. That compares with $105 billion for their U.S. rivals. Faster, more seamless trading systems have long been a priority, but tech spending has shifted across business lines and from back office to front office. It can cover everything from maintaining decades-old systems to cutting-edge artificial intelligence. Unfortunately, European lenders are more focused on patching old systems.

While Americans celebrated a long Labor Day weekend, millions of people in China enrolled in a giant experiment in the future of fake video. An app called Zao that can swap a person's face into movie and TV clips, including from Game of Thrones, went viral on Apple's Chinese app store. The app is popular because making and sharing such clips is fun, but some Western observers' thoughts turned to something more sinister. Zao's viral moment was quickly connected with the idea that US politicians are vulnerable to deepfakes, video or audio fabricated using artificial intelligence to show a person doing or saying something they did not do or say. That threat has been promoted by US lawmakers themselves, including at a recent House Intelligence Committee hearing on deepfakes.

At more than seven decades old, the United Nations has often been criticized for being too slow to respond to crises. But behind the scenes, a high-tech team is harnessing the power of big data and artificial intelligence to predict, monitor and respond to emergencies. CGTN's U.N. correspondent Liling Tan has an inside look at how U.N. Global Pulse is keeping the organization up to speed in the 21st century. Three blocks from the United Nations headquarters in New York, a veritable geek squad of data scientists, analysts and engineers are using big data and artificial intelligence for global good. Or, as U.N. Global Pulse's Director Robert Kirkpatrick puts it, "Our job is to help superheroes find out where people are in trouble, so they can rescue them."