Lifeboat Foundation

Then there is the COVID-19 Open Research Dataset (CORD-19), a multi-institutional initiative that includes The White House Office of Science and Technology Policy, Allen Institute for AI, Chan Zuckerberg Initiative (CZI), Georgetown University’s Center for Security and Emerging Technology (CSET), Microsoft, and the National Library of Medicine (NLM) at the National Institutes of Health (NIH).

The goal of this initiative is to create new natural language processing and machine learning algorithms to scour scientific and medical literature to help researchers prioritize potential therapies to evaluate for further study. AI is also being used to automate screening at checkpoints by evaluating temperature via thermal cameras, as well as modulations in sweat and skin discoloration. What’s more, AI-powered robots have even been used to monitor and treat patients. In Wuhan, the original epicenter of the pandemic, an entire field hospital was transitioned into a “smart hospital” fully staffed by AI robotics.

Any time of great challenge is a time of great change. The waves of technological innovation that are occurring now will echo throughout eternity. Science, technology, engineering and mathematics are experiencing a call to mobilization that will forever alter the fabric of discovery in the fields of bioengineering, biomimicry and artificial intelligence. The promise of tomorrow will be perpetuated by the pangs of today. It is the symbiosis of all these fields that will power future innovations.

The reality of COVID19 raises a critical question in the mind of Adam Ethan Loeb a young Belgium boy regarding the extinction of the human person. This questions birthed “Adam’s Dream” which for him will help in “Saving Humanity From Extinction”, by “Availing a Multiplanetary Education for the present and Future Generations“
This 12year old boy highly influenced by Elon Musk and Peter H. Diamandis believes that a multiplanetary existence could have prevented the spread of coronavirus.
This young Space Enthusiast believes that since they are the future of tomorrow, well structure Young Space Education System should be availed because the Future is Faster than we think.

In explaining his vision Adam explained, “Adam’s Dream is my vision about the future with regard to preserving our kind and other living things. This idea struck my mind during this novel coronavirus outbreak. As the spread increases day in and day out, I was scared and asked my mum the question, “mum, do you know that living in space could have saved or preserved humanity better”? My reason is, if we become multiplanetary, it will solve the problem of overpopulation and make the human person more creative and resilient.

Thus, in this project, I will be preparing my generation and the ones to come to become multiplanetary Species. We have many Space Advocates; there is no proper attention giving to the younger generation. The future is obscure for my generation, and I want to lead them to light through the help of those who know better in compliance with “Adam’s Dream” rooted in Saving Humanity from Extinction by Availing a Multiplanetary Education for the present and Future Generations. In this, we can have a Sustainable “Kosmic” Environment for Prosperous Living.

Reading the works of Elon Musk gave me the conviction that as a Multiplanetary Activist, I can do this. Elon started thinking about Space at 14 years; I began at 10years. He is no doubt my number one role model followed by Peter H. Diamandis with my effort in understanding the teachings of Sara Seager – Planetary Scientist, K. Radhakrishnan, Carolyn Porco, Jill Tarter, Neil deGrasse Tyson, Liu Yang, Steve Squyres, Louis Allamandola, and David Spergel. I will have a better approach to harnessing the reality of Multiplanetary for my generation on those to come. The reality of Space is faster than you think.”

Skoltech researchers have offered a solution to the problem of searching for materials with required properties among all possible combinations of chemical elements. These combinations are virtually endless, and each has an infinite multitude of possible crystal structures; it is not feasible to test them all and choose the best option (for instance, the hardest compound) either in an experiment or in silico. The computational method developed by Skoltech professor Artem R. Oganov and his PhD student Zahed Allahyari solves this major problem of theoretical materials science. Oganov and Allahyari presented their method in the MendS code (stands for Mendelevian Search) and tested it on superhard and magnetic materials.

“In 2006, we developed an algorithm that can predict the crystal structure of a given fixed combination of chemical elements. Then we increased its predictive powers by teaching it to work without a specific combination — so one calculation would give you all stable compounds of given elements and their respective crystal structures. The new method tackles a much more ambitious task: here, we pick neither a precise compound nor even specific chemical elements — rather, we search through all possible combinations of all chemical elements, taking into account all possible crystal structures, and find those that have the needed properties (e.g., highest hardness or highest magnetization)” says Artem Oganov, Skoltech and MIPT professor, Fellow of the Royal Society of Chemistry and a member of Academia Europaea.

The researchers first figured out that it was possible to build an abstract chemical space so that compounds that would be close to each other in this space would have similar properties. Thus, all materials with peculiar properties (for example, superhard materials) will be clustered in certain areas, and evolutionary algorithms will be particularly effective for finding the best material. The Mendelevian Search algorithm runs through a double evolutionary search: for each point in the chemical space, it looks for the best crystal structure, and at the same time these found compounds compete against each other, mate and mutate in a natural selection of the best one.

Brain injuries can vary greatly in their severity, but assessing the extent of the damage is far from a simple undertaking. Scientists in the UK have developed a new AI algorithm that could help narrow the margin for error, with the ability to detect and categorize different types of brain lesions to gauge the impact of an injury.

One of the tools doctors use to assess brain injuries is a CT scan, which can reveal signs of damage, such as lesions, on the brain. But analyzing these scans to reach a diagnosis is a time-consuming process for radiologists, and given the complex nature of the organ, it can see tell-tale signs often overlooked.

“CT is an incredibly important diagnostic tool, but it’s rarely used quantitatively,” said Professor David Menon, from the University of Cambridge and senior author of the new study. “Often, much of the rich information available in a CT scan is missed, and as researchers, we know that the type, volume and location of a lesion on the brain are important to patient outcomes.”

That strategy was unveiled in a directive on Wednesday by the Ministry of Industry and Information Technology (MIIT), which called on local authorities in 23 provinces, five autonomous regions and four municipalities to support the establishment of these new big data centres, which will help bolster efforts to upgrade the country’s manufacturing sector.

The Ministry of Industry and Information Technology has called on local authorities in 23 provinces, five autonomous regions and four municipalities to support the establishment of new ‘industrial big data’ centres, which would bolster the digital transformation of various industries.

The present paper addresses the high Reynolds number, two-dimensional, steady laminar flow separation phenomenon near an interior (concave) corner. A very fast Alternating Direction Implicit finite difference approach is used to solve the Interacting Boundary Layer approximation to the Navier Stokes equations in a conformal plane. Solutions are presented for corner angles up to 18° for Reynolds numbers (based on forebody length) up to 10⁸. Convergence properties and accuracy levels are identified in order to provide reliability estimates of the results. Limitations to the numerical algorithm for large separation regions at high Reynolds numbers are identified.

Circa 2012

Quantum computational algorithms exploit quantum mechanics to solve problems exponentially faster than the best classical algorithms^1,2,3. Shor’s quantum algorithm⁴ for fast number factoring is a key example and the prime motivator in the international effort to realize a quantum computer⁵. However, due to the substantial resource requirement, to date there have been only four small-scale demonstrations^6,7,8,9. Here, we address this resource demand and demonstrate a scalable version of Shor’s algorithm in which the n-qubit control register is replaced by a single qubit that is recycled n times: the total number of qubits is one-third of that required in the standard protocol^10,11. Encoding the work register in higher-dimensional states, we implement a two-photon compiled algorithm to factor N = 21. The algorithmic output is distinguishable from noise, in contrast to previous demonstrations. These results point to larger-scale implementations of Shor’s algorithm by harnessing scalable resource reductions applicable to all physical architectures.

In the week of April 12–18, the top 10 search terms on Amazon.com were: toilet paper, face mask, hand sanitizer, paper towels, Lysol spray, Clorox wipes, mask, Lysol, masks for germ protection, and N95 mask. People weren’t just searching, they were buying too —and in bulk. The majority of people looking for masks ended up buying the new Amazon #1 Best Seller, “Face Mask, Pack of 50”.

When covid-19 hit, we started buying things we’d never bought before. The shift was sudden: the mainstays of Amazon’s top ten—phone cases, phone chargers, Lego—were knocked off the charts in just a few days. Nozzle, a London-based consultancy specializing in algorithmic advertising for Amazon sellers, captured the rapid change in this simple graph.

It took less than a week at the end of February for the top 10 Amazon search terms in multiple countries to fill up with products related to covid-19. You can track the spread of the pandemic by what we shopped for: the items peaked first in Italy, followed by Spain, France, Canada, and the US. The UK and Germany lag slightly behind. “It’s an incredible transition in the space of five days,” says Rael Cline, Nozzle’s CEO. The ripple effects have been seen across retail supply chains.

An AI trained to spot objects hidden against a background is able to see through camouflage and outperforms existing algorithms at the task.