The coronavirus pandemic has taken a severe toll on industries, health systems and lives since the outbreak began with doctors, scientists and ordinary people racing to find ways to tackle the contagion. From robots to a virus-killing snood and a portable isolation capsule, these new prototypes demonstrate what humans are capable of in the face of adversity. Here are some of the innovations developed to combat the current outbreak that has killed more than 217,000 people and infected 3.1 million. COVID-19 attacks people's lungs making it hard for them to deliver oxygen to the blood. Ventilators, which feed oxygen into the lungs, are a crucial tool to keep people with the virus alive.

In the absence of trade shows and other physical preview events, publishers are getting creative with their video game marketing. Today, Ubisoft casually launched a livestream that will reveal the setting of the next Assassin's Creed game. But here's the wild part: instead of a simple countdown, Ubisoft is broadcasting an artist working in Adobe Photoshop. At the time of writing, the canvas shows a mysterious silhouette of a powerful figure (the next game's protagonist, presumably) in front of a split background that contains icy waters and luscious fields. Will it end with some kind of trailer, or a finished poster?

In modern mathematics, many of the biggest advances are great elaborations of theory. Mathematicians move mountains, but their strength comes from tools, highly sophisticated abstractions that can act like a robotic glove, enhancing the wearer's strength. John Conway was a throwback, a natural problem-solver whose unassisted feats often left his colleagues stunned. Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research develop ments and trends in mathe matics and the physical and life sciences. "Every top mathematician was in awe of his strength. People said he was the only mathematician who could do things with his own bare hands," said Stephen Miller, a mathematician at Rutgers University.

This week, we talked to people trying to help stem the hurt of the Covid-19 pandemic--with mixed success. One company, Zipline, is using drones to help deliver virus testing supplies and personal protective equipment in Ghana. It has accelerated efforts to bring the approach to the US, though don't expect to see helper drones in the air before later this year. Farmers, packers, and processors want to get their produce, milk, and meat to consumers, but complex supply chains--and basic economics--are proving hard to hack. Let's get you caught up.

What if we could treat the Earth like a computer, a system with an ever-flowing set of data that can be tracked, analyzed, and potentially even predicted. That's the gist of Microsoft's latest environmental initiative, which it's dubbed a "Planetary Computer." The company foresees a world where we can track just about anything happening in the world -- a forest fire in California, the river tides in Uganda -- and have all of that data readily accessible on a single AI-driven platform. If Microsoft succeeds it could reshape our relationship with the Earth entirely. Lucas Joppa, Microsoft's first chief environmental officer, boiled down the concept succinctly in an interview for the Engadget Podcast: "It's a platform that is intended to accelerate our ability to monitor, model and then ultimately manage Earth's natural systems to ask questions like, 'Where are the world's forests? Where are the world's wetlands? How fast are they changing?' And hopefully, what are the sorts of benefits that we are gaining from those ecosystems? What are the services that those ecosystems provision to people?"

A physician-turned-entrepreneur raised in Kashmir is now part of a team using big data and machine learning to help detect useful patterns in the tsunami of public health data generated world-wide by the COVID-19 crisis and do what he can for those back home. Junaid Nabi, a public health researcher at Brigham and Women's Hospital and Harvard Medical School in Boston, says his experiences with the health system in the developing world drives his current work. "Growing up in Kashmir, a society marred with social, economic, and healthcare disparities, I was exposed to the inherent inequities in my community at an early age," he said, "During the final years of my training, I had an opportunity to work with some non-profit organizations, especially the rescue teams during the Savar building collapse in Dhaka, Bangladesh." "This is when I noticed that clinical medicine does not answer all the questions clinical work asks." Nabi, who is also an Aspen New Voices Fellow, is now working with colleagues at Harvard Medical School and Harvard School of Public Health to develop digital tools that harness big data and machine learning to rapidly evaluate patterns in the data pouring in from clinical research. "I believe machine learning has an important role in COVID-19," he said.

Do you remember watching crime shows where investigating teams used to hire sketch artists to draw the image/face of criminal described by witnesses? And they would then hunt for the person to lock him up. But one might wonder today, are these tactics still common in detecting crime or criminals? With the rise in Artificial Intelligence enabled Face and Image Recognition technologies, the days of sketching criminal are long gone. The process of identifying or verifying the identity of a person using their face has made investigations a lot easier today.

Agriculture finance represents an important element of eradicating extreme poverty and boosting shared prosperity. According to the International Fund for Agricultural Development, smallholders manage over 80% of the world's estimated 500 million small farms and provide over 80% of the food consumed in a significant part of the developing world, making a major contribution to poverty reduction and food security. Most smallholder farms are in Asia and sub-Saharan Africa, and in both regions over 80% of farmland is managed by smallholders. Even though these farmers are generally characterized by limited resources--particularly in terms of land--and dependence on household members for farm labor, they represent a critical part of food systems in developing countries. In light of the size and importance of the smallholder farming sector, the development community has a growing focus on providing these farmers with the funding they need to thrive.

The algorithmic revolution is here, and nations are losing control of not only their understanding of the potential impact of artificial intelligence but also the governance model that enforced accountability on the advances in science and technology over the years at all levels. While each new technology innovation claims its territory for the economic advances in the human ecosystem with significant ramifications across cyberspace, geospace and/or space (CGS), the rise of artificial intelligence (AI) has not only undermined governance, management, and growth models, but it has also broken all barriers to boundaries defined by human decision makers. In addition, it is both blurring the boundaries between human intelligence and machine intelligence, and the boundaries between man and machine and real and fake. As a result, the power dynamics are shifting away from the select few across nations (and is moving away from humans entirely to algorithms)--re-defining the criteria upon which geopolitics was framed--and thereby threatening the foundations of global peace and security. Since the beginning of the technological age, each new idea, innovation, and invention has helped humans across nations usher in a new era of economic growth, changing the fundamentals of respective nations and their security.

Citation parsing, particularly with deep neural networks, suffers from a lack of training data as available datasets typically contain only a few thousand training instances. Manually labelling citation strings is very time-consuming, hence synthetically created training data could be a solution. However, as of now, it is unknown if synthetically created reference-strings are suitable to train machine learning algorithms for citation parsing. To find out, we train Grobid, which uses Conditional Random Fields, with a) human-labelled reference strings from 'real' bibliographies and b) synthetically created reference strings from the GIANT dataset. We find that both synthetic and organic reference strings are equally suited for training Grobid (F1 = 0.74). We additionally find that retraining Grobid has a notable impact on its performance, for both synthetic and real data (+30% in F1). Having as many types of labelled fields as possible during training also improves effectiveness, even if these fields are not available in the evaluation data (+13.5% F1). We conclude that synthetic data is suitable for training (deep) citation parsing models. We further suggest that in future evaluations of reference parsers both evaluation data similar and dissimilar to the training data should be used for more meaningful evaluations.