Artificial intelligence (AI) isn't just transforming the world -- it's helping protect and preserve the future of the Amazon River. Rapid hydropower expansion has radically altered the Amazon River. When the natural flow of a river is altered, there are often serious, cascading changes. Now, AI and other computer science tools can help reduce these adverse and devastating effects on the environment, according to new research published in Science. FIU researcher Elizabeth Anderson was a part of a collaborative team of scientists from across the United States, Europe and South America who examined how cutting-edge technology can inform more sustainable and strategic planning.

Using artificial intelligence to analyse vast amounts of data in atomic-scale images, researchers answered long-standing questions about an emerging type of rechargeable battery posing competition to lithium-ion chemistry.

With 5G, real-time computing will become a reality. The high speeds, high data throughput, and high number of connections that 5G enables will effectively erase the lag time between when data gets generated to when we can act on it. And, while self-driving vehicles might be the most visible new example of real-time processing most of us see, they are really only the tip of the iceberg, especially as private networks and network slicing roll out to bring the power of carrier-grade infrastructure to more locations and situations. IDC estimates that real-time data will grow by 50 times between 2000 and 2030 and constitute 30% of all data by then. Manufacturing will be one of the first places where a real-time data revolution takes place.

For a long time, digitalization has been at the heart of the transformation of major utility companies, significantly contributing to the reshaping and reinventing of their business processes and business models. This is also because there is a growing urgency to adapt to the new'climate' ecosystem that is environmentally sustainable and economically efficient. As more utility companies continue to face unique challenges, digital transformation initiatives are now a must, considering the demands of changing customer expectations and market needs. Going by this Accenture report, 59% of utility company executives say that the pace of digital transformation for their organization is accelerating. This is likely to further gain momentum as more utilities companies begin to adopt renewable sources of energy.

In nuclear fusion, the atomic nuclei of hydrogen atoms get forced together to form heavier atoms, like helium. This produces a lot of energy relative to a tiny amount of fuel, making it a very efficient source of power. It is far cleaner and safer than fossil fuels or conventional nuclear power, which is created by fission--forcing nuclei apart. It is also the process that powers stars. Controlling nuclear fusion on Earth is hard, however.

Distance measures provide the foundation for many popular algorithms in Machine Learning and Pattern Recognition. Different notions of distance can be used depending on the types of the data the algorithm is working on. For graph-shaped data, an important notion is the Graph Edit Distance (GED) that measures the degree of (dis)similarity between two graphs in terms of the operations needed to make them identical. As the complexity of computing GED is the same as NP-hard problems, it is reasonable to consider approximate solutions. In this paper we present a QUBO formulation of the GED problem. This allows us to implement two different approaches, namely quantum annealing and variational quantum algorithms that run on the two types of quantum hardware currently available: quantum annealer and gate-based quantum computer, respectively. Considering the current state of noisy intermediate-scale quantum computers, we base our study on proof-of-principle tests of their performance.

The issue of environmental sustainability is becoming a more crucial to any organization day by day as the finance world drives this change. It has been observed that all organizations be it large/medium or small tend to have a massive carbon footprint, so it is increasingly important to understand and analyze company's environmental impact first using innovative AI driven tools & technologies to drive a greener and safer planet while "meeting the needs of the present without compromising the ability of future generations to meet their own needs". High energy consumption in industrial applications is one of the key reasons for higher carbon footprints. All organizations need Artificial Intelligence (AI) as partner to plan, design, execute, and recommend the sustainability measures to ensure the future of our planet. AI tools, technologies and techniques will help us to achieve sustainability through defining right data driven climatic control measures more efficiently, effective and efficient use of natural resources, reducing and managing the waste we generate more effectively and effective use of environmental opportunities.

The inside of a tokamak--the doughnut-shaped vessel designed to contain a nuclear fusion reaction--presents a special kind of chaos. Hydrogen atoms are smashed together at unfathomably high temperatures, creating a whirling, roiling plasma that's hotter than the surface of the sun. Finding smart ways to control and confine that plasma will be key to unlocking the potential of nuclear fusion, which has been mooted as the clean energy source of the future for decades. At this point, the science underlying fusion seems sound, so what remains is an engineering challenge. "We need to be able to heat this matter up and hold it together for long enough for us to take energy out of it," says Ambrogio Fasoli, director of the Swiss Plasma Center at École Polytechnique Fédérale de Lausanne in Switzerland.

Fusion reactors promise cheap, abundant and relatively clean energy – if we can get them to work. Now, thanks to artificial intelligence firm DeepMind, fusion researchers are one step closer to extracting power from plasma hotter than the surface of the sun. DeepMind worked with scientists at the Swiss Federal Institute of Technology in Lausanne (EPFL), Switzerland, to create a neural network capable of controlling the magnetic fields within EPFL's Variable Configuration Tokamak (TCV) fusion reactor. These magnetic fields are essential for keeping the plasma generated by the reactor safely contained. If the plasma touches the walls of the reactor it rapidly cools, stifling the reaction and potentially causing significant damage.

The inside of a tokamak--the donut-shaped vessel designed to contain a nuclear fusion reaction--presents a special kind of chaos. Hydrogen atoms are smashed together at unfathomably high temperatures, creating a whirling, roiling plasma that's hotter than the surface of the sun. Finding smart ways to control and confine that plasma will be key to unlocking the potential of nuclear fusion, which has been mooted as the clean energy source of the future for decades. At this point, the science underlying fusion seems sound, so what remains is an engineering challenge. "We need to be able to heat this matter up and hold it together for long enough for us to take energy out of it," says Ambrogio Fasoli, director of the Swiss Plasma Center at École Polytechnique Fédérale de Lausanne. That's where DeepMind comes in.