While governments are trying to handle the situation, how could technology innovations help prevent starvation and improve agriculture management in the future? For example, the drone can't fly "Beyond-Line-of-Sight" (BLOS), meaning it must not move away from the drone pilot more than 100 meters, except if its weight is under 2 kg (4,4 lb) or if you have a special exemption. As we map thousands of hectares thanks to solar energy to address conservation issues, we are directly progressing towards SDGs 7, 9 and 15 (cf.: We are currently raising funds and starting the drone marketing abroad, in Africa and Brazil.

An international, interdisciplinary research team of scientists has come up with a machine-learning method that predicts molecular behavior, a breakthrough that can aid in the development of pharmaceuticals and the design of new molecules that can be used to enhance the performance of emerging battery technologies, solar cells, and digital displays. The work appears in the journal Nature Communications. "By identifying patterns in molecular behavior, the learning algorithm or'machine' we created builds a knowledge base about atomic interactions within a molecule and then draws on that information to predict new phenomena," explains New York University's Mark Tuckerman, a professor of chemistry and mathematics and one of the paper's primary authors. The work combines innovations in machine learning with physics and chemistry. Data-driven approaches, particularly in the area of machine learning, allow everyday devices to learn automatically from limited sample data and, subsequently, to act on new input information.

AI for Earth, a program that offers access to Microsoft's artificial intelligence (AI) technologies and cloud resources to researchers and organizations that are addressing environmental challenges, is now turning its attention to the world's oceans. When AI for Earth was first announced in July, its focus was on agriculture, biodiversity, climate change and water scarcity. Now a different sort of water management, that of the oceans, is in the company's crosshairs. The new AI for Earth European Union Oceans Award provides cloud computing resources to researchers focused on oceans and problems affecting them, which may include pollution, rising sea levels and increasing ocean acidity. "Covering nearly 70 percent of the Earth's surface, oceans play an outsized role in the health of our planet," said Lucas Joppa, chief environmental scientist at Microsoft, in an Oct. 6 announcement. "They generate much of the oxygen we breathe, provide food and livelihoods for billions of people around the world, and support a vast and incredible array of species, many of which have not yet been discovered or described."

Algorithmic decision making process now affects many aspects of our lives. Standard tools for machine learning, such as classification and regression, are subject to the bias in data, and thus direct application of such off-the-shelf tools could lead to a specific group being unfairly discriminated. Removing sensitive attributes of data does not solve this problem because a \textit{disparate impact} can arise when non-sensitive attributes and sensitive attributes are correlated. Here, we study a fair machine learning algorithm that avoids such a disparate impact when making a decision. Inspired by the two-stage least squares method that is widely used in the field of economics, we propose a two-stage algorithm that removes bias in the training data. The proposed algorithm is conceptually simple. Unlike most of existing fair algorithms that are designed for classification tasks, the proposed method is able to (i) deal with regression tasks, (ii) combine explanatory attributes to remove reverse discrimination, and (iii) deal with numerical sensitive attributes. The performance and fairness of the proposed algorithm are evaluated in simulations with synthetic and real-world datasets.

In science news around the world, a deadly plague epidemic spreads through Madagascar, Japan's economy ministry announces a successful first test of seafloor mining for metallic ore deposits near hydrothermal vents, the World Health Organization releases a new strategy for fighting cholera, and the U.S. Environmental Protection Agency moves to roll back limits on greenhouse gas emissions from power plants. Also, economist Richard Thaler of the University of Chicago in Illinois wins the Nobel economics prize for his study of irrational human economic behavior, scientists discover evidence of rice domestication in South America, and a Carnegie Mellon University roboticist describes how his robotic snakes combed through rubble of the 19 September earthquake in Mexico.

There have been 26 significant'space weather' events affecting Earth over the last 50 years. These solar events can severely disrupt the Earth's magnetosphere (the boundary between the Earth's magnetic field and the solar wind), and pose a direct threat to electrical infrastructure - knocking out technologies that we rely on every single day, like GPS signals, electrical grids, computers and satellites. To put it lightly, if a major event were to happen tomorrow, it's likely to cost at least $2 trillion in damages in the first year alone. So, we're all doomed - right? Luckily for us, NASA has founded a Solar Space Team, which sits within the Frontier Development Lab (FDL).

Direct observation of chemical reactions is notoriously difficult. Reaction rates tend to be too fast for chemists to be able to see how molecules move as they combine and change, and individual electrons -- the species that are directly involved with reactions-- are subject to the laws of quantum mechanics that make direct observation of their position impossible. It'd especially difficult to observe reactions between organic molecules involving catalysts, because the reactions can take place at extreme temperatures and pressure, often proceed via very short-lived and unstable intermediates formed by combinations of the reactants with the catalyst. This makes it difficult to determine the mechanism of the reaction, which in turn complicates the design of improved catalysts. An interdisciplinary team of chemists, physicists and computer scientists at the US Department of Energy's Brookhaven National Laboratory in New York State and nearby Stony Brook University have devised a method to analyse data from X-ray crystallography to decipher the three-dimensional nanostructures that form during catalysed reactions.

Conventional seismic techniques for detecting the subsurface geologic features are challenged by limited data coverage, computational inefficiency, and subjective human factors. We developed a novel data-driven geological feature detection approach based on pre-stack seismic measurements. Our detection method employs an efficient and accurate machine-learning detection approach to extract useful subsurface geologic features automatically. Specifically, our method is based on kernel ridge regression model. The conventional kernel ridge regression can be computationally prohibited because of the large volume of seismic measurements. We employ a data reduction technique in combination with the conventional kernel ridge regression method to improve the computational efficiency and reduce memory usage. In particular, we utilize a randomized numerical linear algebra technique, named Nystr\"om method, to effectively reduce the dimensionality of the feature space without compromising the information content required for accurate detection. We provide thorough computational cost analysis to show efficiency of our new geological feature detection methods. We further validate the performance of our new subsurface geologic feature detection method using synthetic surface seismic data for 2D acoustic and elastic velocity models. Our numerical examples demonstrate that our new detection method significantly improves the computational efficiency while maintaining comparable accuracy. Interestingly, we show that our method yields a speed-up ratio on the order of $\sim10^2$ to $\sim 10^3$ in a multi-core computational environment.

We are living in a world facing unprecedented global challenges. The Earth has been in a state of relative stability for the last 10,000 years, enabling civilisations to thrive. In a short space of time, however, this stability has been put at risk. Scientists at the Stockholm Environment Institute have identified that four out of the Earth's nine'Planetary Boundaries' have been crossed, namely climate, biodiversity, land-system change and biogeochemical cycles. Risks will only heighten as population swells to a projected 9 billion by 2050 increasing food, materials and energy needs.

In the world of business and design, we have started using terms like "algorithm" and "machine learning" as magic calculations for problems we would like to gloss over. These terms often become blockers for deeper problem solving and can stall even the most worthwhile projects. "We'll figure it out with an algorithm" used generally is similar to the fantastic conversations my daughter and I had when she was six. She would come up with inventions for amazing things like solar power in a backpack and magic windows. If she wanted to skip over something, she included, "and then you put some potion on it."