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.

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

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.

Could the same technology that is used to unlock people's smartphones also help unlock the secrets of the universe? It may sound unlikely, but that's exactly what researchers from Switzerland's science and technology-focused university ETH Zurich are working to achieve. Using a variation of the type of artificial intelligence neural network behind today's facial recognition technology, they have developed new A.I. tools that could prove a game-changer in the discovery of so-called "dark matter." Physicists believe that understanding this mysterious substance is necessary to explain fundamental questions about the underlying structure of the universe. "The algorithm we [use] is very close to what is commonly used in facial recognition," Janis Fluri, a Ph.D. student who works in an ETH Zurich lab focused on applying neural networks to cosmological problems, told Digital Trends.

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.

What if there was no such thing as "real"? What if food could be made from thin air? What if electronics could last forever? These are some of the questions being tackled by Europe's top tech innovators identified by our team here at Sifted, in association with the co-working space Second Home and their Breakthrough event this month. This is not your ordinary innovator list. You may not have heard of these startups.

If you're worried about the malevolent potential of deepfake video, you're not alone – so is Facebook. The company has launched a project to sniff out deepfake videos, and it's pledging more than $10m to the cause. It has pulled in a range of partners including Microsoft for help. Deepfakes are videos that use AI to superimpose one person's face on another. They work using generative adversarial networks (GANs), which are battling neural networks.

Gordon's wine bar is reached through a discreet side-door, a few paces from the slipstream of London theatregoers and suited professionals powering towards their evening train. A steep staircase plunges visitors into a dimly lit cavern, lined with dusty champagne bottles and faded newspaper clippings, which appears to have had only minor refurbishment since it opened in 1890. "If Miss Havisham was in the licensing trade," an Evening Standard review once suggested, "this could have been the result." The bar's Dickensian gloom is a selling point for people embarking on affairs, and actors or politicians wanting a quiet drink – but also for pickpockets. When Simon Gordon took over the family business in the early 2000s, he would spend hours scrutinising the faces of the people who haunted his CCTV footage. "There was one guy who I almost felt I knew," he says. "He used to come down here the whole time and steal." The man vanished for a six-month stretch, but then reappeared, chubbier, apparently after a stint in jail.

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."

Social media sources can provide crucial information in crisis situations, but discovering relevant messages is not trivial. Methods have so far focused on universal detection models for all kinds of crises or for certain crisis types (e.g. floods). Event-specific models could implement a more focused search area, but collecting data and training new models for a crisis that is already in progress is costly and may take too much time for a prompt response. As a compromise, manually collecting a small amount of example messages is feasible. Few-shot models can generalize to unseen classes with such a small handful of examples, and do not need be trained anew for each event. We compare how few-shot approaches (matching networks and prototypical networks) perform for this task. Since this is essentially a one-class problem, we also demonstrate how a modified one-class version of prototypical models can be used for this application.