Unless you have been living under a rock you will have noticed international relations have not been so peachy recently. Particularly with Russia, as they continue to use brutal and criminal acts in Ukraine, despite universal outcry. One of the most shocking revelations of the war in Ukraine has been Russia's liberal use of hypersonic missiles…

It is no secret that everybody wants to predict recessions. Many economists and finance firms have attempted this with limited success, but by and large there are several well known leading indicators for recessions in the US economy. However, when presented to the general public these indicators are typically taken alone, and are not framed in a way that can give probability statements associated with an upcoming recession. In this project, I have taken several of those economic indicators and built a classification model to generate probabilistic statements. Here, the actual classification ('recession' or'no recession') is not as important as the probability of a recession, since this probability will be used to determine a basic portfolio scheme which I will describe later on.

As artificial intelligence and machine learning deploy across financial sectors, federal government needs a way to ensure standards for stability and inclusion are followed. Measuring risks and setting benchmarks for emerging fintech is top of mind for agencies such as the National Institute of Standards and Technology and the Commerce Department. In her first public engagement since being sworn in earlier this month, NIST Director Laurie Locascio told an audience at Stanford University on Wednesday that the president's 2023 budget request calls for an additional $80 million to expand and strengthen NIST capabilities for targeting critical and emerging technologies. Listing ways the agency is trying to enable trustworthy AI, she said NIST scientists and engineers are developing taxonomies, terminology and testbeds for measuring AI risks. "NIST is developing a resource center of documents, software and standards and related tools that continue to better understanding and better identification of measurement, and management of various risks associated with AI systems," she said during the Artificial Intelligence and the Economy Conference.

China is a country that is known for its huge investments and ambitious plans. Last year it spent more than $250 billion in renewable energy alone. It is a country on the rise, and its citizens believe that the future belongs to them. China has made a great leap forward in artificial intelligence and it is not afraid to share its achievements with the rest of the world. In fact, it is quite the opposite: it sees AI as a way to improve all aspects of life, and it wants to share its technologies with the rest of the world, especially with its biggest trading partners, such as the USA.

Imagine a swarm of terrestrial robots that can explore an environment, and, upon completion of this task, reconfigure into a spherical ball and roll out. This dimensional change alters the dynamics of locomotion and can assist them to maneuver variable terrains. The sphere-plane reconfiguration is equivalent to projecting a spherical shell onto a plane, an operation which is not possible without distortions. Fortunately, soft materials have potential to adapt to this disparity of the Gaussian curvatures. Modular Soft Robots (MSoRos) have promise of achieving dimensional change by exploiting their continuum and deformable nature. We present topology and morphology design of MSoRos capable of reconfiguring between spherical and planar configurations. Our approach is based in geometry, where a platonic solid determines the number of modules required for plane-to-sphere reconfiguration and the radius of the resulting sphere, e.g., four `tetrahedron-based' or six `cube-based' MSoRos are required for spherical reconfiguration. The methodology involves: (1)inverse orthographic projection of a `module-topology curve' onto the circumscribing sphere to generate the spherical topology,(2)azimuthal projection of the spherical topology onto a tangent plane at the center of the module resulting in the planar topology, and (3)adjusting the limb stiffness and curling ability by manipulating the geometry of cavities to realize a physical finite-width, Motor-Tendon Actuated MSoRo. The topology design is shown to be scale invariant, i.e., scaling of base platonic solid is reflected linearly in spherical and planar topologies. The module-topology curve is optimized for the reconfiguration and locomotion ability using a metric that quantifies sphere-to-plane distortion. The geometry of the cavity optimizes for the limb stiffness and curling ability without compromising the actuator's structural integrity.

A team of researchers led by the University of Minnesota has significantly improved the performance of numerical predictions for agricultural nitrous oxide emissions. The first-of-its-kind knowledge-guided machine learning model is 1,000 times faster than current systems and could significantly reduce greenhouse gas emissions from agriculture. The research was recently published in Geoscientific Model Development, a not-for-profit international scientific journal focused on numerical models of the Earth. Researchers involved were from the University of Minnesota, the University of Illinois at Urbana-Champaign, Lawrence Berkeley National Laboratory, and the University of Pittsburgh. Compared to greenhouse gases such as carbon dioxide and methane, nitrous oxide is not as well-known.

Smart People asks the question: How can local government use technology to better engage with Dubliners? It also asks how can governments get people more involved with improving their city? To help answer those questions, the Dublin Government has established initiatives focusing on outreach programs and social media. These programs include consultations, surveys, social media campaigns, and more, to include citizens in decision-making processes and allow their voices to be heard. The government also has a social media analysis using Natural Language Processing (NLP) to help understand the sentiment behind resident's opinions.

Severe wildfires, raging storms and other extreme weather conditions are all indications that the climate is changing and not for the better. Earlier this month, the United Nations Intergovernmental Panel on Climate Change released its sixth report on the assessment of global climate conditions. The report looks at environments that are growing warmer, rising sea levels and species becoming extinct. The warning is clear: Something must be done to save the climate. But some have attempted to use AI to combat climate change.

The world is witnessing rapid technological advances in the fields of data science and artificial intelligence (AI). From helping fight climate change to addressing all the other sustainable development goals of the United Nations, valuable use cases show how cutting-edge data and AI applications can improve our daily lives. At the same time, public awareness initiatives are still behind the curve, leaving many people feeling ambivalent about AI. Moreover, for non-technical readers, disparate definitions of data and AI terms can impede easy understanding of these dynamic fields. Despite global summits, educational publications, and ample media coverage, the fields of AI and data science stand to benefit from an agreed set of accessible definitions and terminologies.

SINGAPORE - A one-man team comprising Singaporean research scientist Wang Weimin beat 469 other teams from around the world in a five-month-long challenge to develop the best artificial intelligence (AI) model for detecting deepfakes, or digitally altered video clips. Mr Wang's model was 98.53 per cent accurate at telling apart genuine clips from those that featured digitally manipulated faces, voices or both. On Friday (April 29), the National University of Singapore graduate was awarded first place and a cash prize of $100,000 in the Trusted Media Challenge organised by AI Singapore, a national AI programme office under the National Research Foundation. Mr Wang, who works at Chinese tech giant ByteDance, which owns TikTok, was also offered a $300,000 start-up grant to commercialise his invention. But he said he is hoping to incorporate his AI model into his company's BytePlus platform and offer deepfake detection as a service to its clients.