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Researchers from the National Aeronautics and Space Administration (NASA) have teamed up with the US Department of Energys Argonne National Laboratory (ANL) to develop artificial intelligence (AI) to enhance the speed of simulations to study the behavior of air surrounding supersonic and hypersonic aircraft engines.

Fighter jets such as F-15s regularly exceed Mach 2 two times the speed of sound during the flight which is known as supersonic level. On a hypersonic flight which is Mach 5 and beyond, an aircraft flies faster than 3,000 miles per hour.

Hypersonic speeds have been made possible since the 1950s by the propulsions systems used for rockets however, engineers and scientists are working on advanced jet engine designs to make the hypersonic flight much less expensive than a rocket launch and more common such as for commercial flight, space exploration, and national defense purposes.

The newly published paper by a team of researchers from NASA and ANL details the machine learning techniques to reduce the memory and cost required to conduct computational fluid dynamics (CFD) simulations related to fuel combustion at supersonic and hypersonic speeds.

The paper was previously presented at the American Institute of Aeronautics and Astronautics SciTech Forum in January.

Before building and testing any aircraft, CFD simulations are used to determine how the various forces surrounding an aircraft in flight will interact with it. CFD consists of numerical expressions representing the behavior of fluids such as air and water.

When an aircraft breaks the sound barrier which involves traveling at speeds surpassing that of sound, it generates a shock wave which is a disturbance that makes the air around it hotter, denser, and higher in pressure causing it to behave very violently.

At hypersonic speeds, the air friction created is so strong that it could melt parts of a conventional commercial plane.

The air-breathing jet engines draw in oxygen to burn fuel as they fly so the CFD simulations have to account for major changes in the behavior of air, not only surrounding the plane but also as it moves through the engine and interacts with fuel.

While a conventional plane has fan blades to push the air along, in planes approaching Mach 3 and above speeds, their movement itself compresses the air. These aircraft designs, known as scramjets, are important to attain fuel efficiency levels that rocket propulsion cannot.

So, when it comes to CFD simulations on an aircraft capable of breaking the sound barrier, all the above factors add new levels of complexity to an already computationally intense exercise.

Because the chemistry and turbulence interactions are so complex in these engines, scientists have needed to develop advanced combustion models and CFD codes to accurately and efficiently describe the combustion physics, said Sibendu Som, a study co-author and interim center director of Argonnes Center for Advanced Propulsion and Power Research.

NASA has a hypersonic CFD code known as VULCAN-CFD which is specially meant for simulating the behavior of combustions in such a volatile environment.

This code uses something called flamelet tables where each flamelet is a small unit of a flame within the entire combustion model. This data table consists of different snapshots of burning fuel in one huge collection which takes up a large amount of computer memory to process.

Therefore, researchers at NASA and the ANL are exploring the use of AI to simplify these CFD simulations by reducing the intensive memory requirements and computational costs, to increase the pace of development of barrier-breaking aircraft.

Computational Scientists at ANL used a flamelet table generated by Argonne-developed software to train an artificial neural network that could be applied to NASAs VULCAN-CFD code. The AI used values from the flamelet table to learn shortcuts about determining the combustion behavior in supersonic engine environments.

The partnership has enhanced the capability of our in-house VULCAN-CFD tool by leveraging the research efforts of Argonne, allowing us to analyze fuel combustion characteristics at a much-reduced cost, said Robert Baurle, a research scientist at NASA Langley Research Center.

Countries across the world are racing to achieve hypersonic flight capability and an essential part of this race are simulation experiments where there is huge potential for the application of emerging tech such as AI and machine learning (ML).

Last month, according to a recent EurAsian Times report, Chinese researchers led by a top-level advisor to the Chinese military on hypersonic weapon technology, claimed a significant breakthrough in an AI system that can design new hypersonic vehicles autonomously.

Moreover, in February a Chinese space company called Space Transportation announced plans for tests beginning next year on a hypersonic plane capable of doing 7,000 miles per hour.

The company claimed that their plane could fly from Beijing to New York in an hour.

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Boosting US Fighter Jets NASA Research Applies Artificial Intelligence To Hypersonic Engine Simulations - EurAsian Times

By Joyjit Chatterjee and Nina Dethlefs, University of Hull Cottingham

Artificial Intelligence (AI) is much more than just a buzzword nowadays. It powers facial recognition in smartphones and computers, translation between foreign languages, systems which filter spam emails and identify toxic content on social media, and can even detect cancerous tumours. These examples, along with countless other existing and emerging applications of AI, help make people's daily lives easier, especially in the developed world.

As of October 2021, 44 countries were reported to have their own national AI strategic plans, showing their willingness to forge ahead in the global AI race. These include emerging economies like China and India, which are leading the way in building national AI plans within the developing world.

Notably, the lowest-scoring regions in this index include much of the developing world, such as sub-Saharan Africa, the Carribean and Latin America, as well as some central and south Asian countries.

The developed world has an inevitable edge in making rapid progress in the AI revolution. With greater economic capacity, these wealthier countries are naturally best positioned to make large investments in the research and development needed for creating modern AI models.

In contrast, developing countries often have more urgent priorities, such as education, sanitation, healthcare and feeding the population, which override any significant investment in digital transformation. In this climate, AI could widen the digital divide that already exists between developed and developing countries.

The hidden costs of modern AI AI is traditionally defined as "the science and engineering of making intelligent machines". To solve problems and perform tasks, AI models generally look at past information and learn rules for making predictions based on unique patterns in the data.

AI is a broad term, comprising two main areas - machine learning and deep learning. While machine learning tends to be suitable when learning from smaller, well-organised datasets, deep learning algorithms are more suited to complex, real-world problems - for example, predicting respiratory diseases using chest X-ray images.

Many modern AI-driven applications, from the Google translate feature to robot-assisted surgical procedures, leverage deep neural networks. These are a special type of deep learning model loosely based on the architecture of the human brain.

Crucially, neural networks are data hungry, often requiring millions of examples to learn how to perform a new task well. This means they require a complex infrastructure of data storage and modern computing hardware, compared to simpler machine learning models. Such large-scale computing infrastructure is generally unaffordable for developing nations.

Beyond the hefty price tag, another issue that disproportionately affects developing countries is the growing toll this kind of AI takes on the environment. For example, a contemporary neural network costs upwards of US$150,000 to train, and will create around 650kg of carbon emissions during training (comparable to a trans-American flight). Training a more advanced model can lead to roughly five times the total carbon emissions generated by an average car during its entire lifetime.

Developed countries have historically been the leading contributors to rising carbon emissions, but the burden of such emissions unfortunately lands most heavily on developing nations. The global south generally suffers disproportionate environmental crises, such as extreme weather, droughts, floods and pollution, in part because of its limited capacity to invest in climate action.

Developing countries also benefit the least from the advances in AI and all the good it can bring - including building resilience against natural disasters.

Using AI for good While the developed world is making rapid technological progress, the developing world seems to be underrepresented in the AI revolution. And beyond inequitable growth, the developing world is likely bearing the brunt of the environmental consequences that modern AI models, mostly deployed in the developed world, create.

But it's not all bad news. According to a 2020 study, AI can help achieve 79 per cent of the targets within the sustainable development goals. For example, AI could be used to measure and predict the presence of contamination in water supplies, thereby improving water quality monitoring processes. This in turn could increase access to clean water in developing countries.

The benefits of AI in the global south could be vast - from improving sanitation, to helping with education, to providing better medical care. These incremental changes could have significant flow-on effects. For example, improved sanitation and health services in developing countries could help avert outbreaks of disease.

But if we want to achieve the true value of "good AI", equitable participation in the development and use of the technology is essential. This means the developed world needs to provide greater financial and technological support to the developing world in the AI revolution. This support will need to be more than short term, but it will create significant and lasting benefits for all. (This article is syndicated by PTI from The Conversation)

Read more from the original source:
Developing countries are being left behind in the AI race - and that's a problem for all of us - Economic Times

Children and young people are growing up in an increasingly digital age, where technology pervades every aspect of their lives. From robotic toys and social media to the classroom and home, artificial intelligence (AI) is a ubiquitous part of daily life. It's vital therefore that ethical guidelines protect them and ensure they get the best from this emerging technology.

Generation Z, who have grown up with AI, are uniquely placed to offer an insight into the potential issues of AI targeted at children and help create governance guidelines. With that in mind the World Economic Forum has set up the AI Youth Council, a global diverse group comprising young people interested in AI.

Members serve as part of the Generation AI project community and have been central to the creation of the Artificial Intelligence for Children Toolkit, published 29 March 2022. The AI Youth Council is designed to bring together young people from 14 to 21 years of age worldwide to discuss AI ethics and governance.

Born between 1996 and 2012, Generation Z is recognized as the digital native generation. We were raised in an era defined by the internet, a time characterized by massive digitalization: social networks were launched, new technologies were created, and AI began its cross-industry debut.

As a result, Gen Z evolved alongside technology, which impacted our childhood in multiple dimensions. With social media, our methods of interaction changed. Instant connectivity translated to spending time with friends 24/7. We easily absorbed new tech trends, and our education was augmented by the integration of new software.

Similarly, born between 2013-2024, Generation Alpha, the first true AI native generation, is experiencing the effects of AI right now. Kids seamlessly interact with AI chatbots and smart toys, use of IoT devices is second nature, and they are used to real-time information access. The effects of AI on childhood are evident: it makes kids crave optimized experiences and hyper-connectivity, whether at home, in school or with friends.

Jianyu Gao, Columbia University, BS in Computer Science, USA

I was raised on an unregulated internet with minimal literacy in privacy and safety, and the adults around me didnt know how to educate me to protect myself because they were just as ignorant as I was. I did stay out of danger because I knew what I was doing I was lucky, but too many other children were not. The internet has given us the opportunity to connect with people around the world who would otherwise be out of reach, but has also exposed children to disturbing content, harmful ideologies, brainwashing communities and social circles, cyberbullies and online stalkers, predators, or other dangerous elements who might not have had access to them in real life.

As we transition into a post-pandemic world that not only lives with the internet but lives on the internet, I reflect on my childhood as a girl with a laptop with worries for the future, but also with the resolve to do better for the youth that will grow up with AI. If we expect AI to be just as human as we are, then we must learn from my generations experiences growing up with technologies such as the internet and prepare for the prospect that AI will not always learn from the best of us.

Guido Putignano, Bachelor of Engineering and Biomedical Engineering, Politecnico di Milano

One of the first times I got in contact with technology was when I was 12. I went to the shop and I saw strange objects that could make you go into other dimensions. At that time, these devices were a one-hour distraction after my evening homework. From the beginning, I remember that technology being harmful to me. I wasted many days watching videos without being intentional about what I was learning. When I turned 16, I started to use these devices proactively. I started making these devices work for me, rather than the opposite.

I think that the best way to predict the future is to create it yourself. I am an optimist, and I think that personalisation and high-speed connectivity will make anything far better than it is today. In this case, AI systems go from being objects to being subjects. One example is in the fields of education and healthcare. Imagine how awesome it would be to have an AI system that could help you personalise many parts of your life and be at the centre of your growth. That system could track thousands of parameters, making astonishingly accurate predictions of your future self. Those opportunities will be a reality in the future.

Joy Fakude, University of Johannesburg, South Africa

Personally, growing up in South Africa, technology didnt have that great of an impact in my life because I didnt have access to it. The closest I came to a laptop was a toy laptop where I practised maths questions, English sentence construction and played some games. Then I advanced to my first cell phone a BlackBerry which I thought was the coolest thing on planet Earth. I then had access to the internet and social media, however access to WiFi became a serious problem and unfortunately that is the reality for a lot of South African youngsters today.

Not having access to data never mind smart phones or laptops in a world that is speeding into a digital era, many South African teens are left behind not even knowing what AI is or what a digital footprint is, or not even knowing how to protect your data. My biggest concern for future generations of South Africans is that the rapid developments of AI technologies leave them stuck in the mud. That they arent taught and because they arent taught cant adapt and, according to Darwins theory of evolution, dont survive.

Born in 2003, my childhood was situated in the transitional stage from floppy disks and BlackBerry phones to social media powerhouses and streaming services. Most of my early interaction with technology was limited to my Sony camera and Nintendo S4. By the time I was 11, I relented to peer pressure and created an Instagram account. As I pondered how to use my new platform, it seemed natural to present the version of myself that fit my current interests. I used the profile Gracie Dancer to perform self-choreographed dance routines or rave about my new tap shoes. Gracie Singer was where I posted all my off-pitched covers of the latest pop songs.

But what was on the surface an apparently innocuous search for a sense of community began affecting me in a way I didnt expect. As my interests evolved, I felt I was wrong for wanting to try different things. The uncertainty of not knowing who there was behind the screen made me feel as if though I was constantly being watched and judged. I began to fear mistakes at a time in my life when they should have been the most welcomed.

While technology has undeniable potential, I worry that the coming generation of children are growing up in a society where we are understood by others solely through our internet personas. Genuine relationships, interests, and activities will come second to keeping up the illusion of perfection, which so often means conformity.

Ecem Yilmazhaliloglu from Turkey, studying at Stanford University

As the last generation to learn navigating bulky, old system units in computer lessons at school and the first generation to grow up having a Facebook profile, I belong to what Id like to call the transition generation. As the new technologies social media, touch screens, cloud storage systems, and AI rapidly made their way into our world and our homes, we learned to adapt and experiment though trial and error, as there was no previous generation to show us the ropes.

When I got my first computer and opened up my first social media profile at eight, to play online games with my school friends, none of us thought about the consequences of our actions. Starting to engage in these technologies with the purest of intentions, in our attempt to fulfill the basic human need to socialize, to connect, we made ourselves vulnerable to the dangers that lurked in the technologys shadows. The world has since realized its mistake in being unprepared against these dangers, but many of us had already become victims of catfishing, hacking, and even stalking.

While technology offered us many benefits there was always equal amounts of risk involved. Having learnt this in the past decade, both adults and children, it is our responsibility to provide guidance, protect the next generations and help navigate these technologies responsibly and for the good of all. It is our job to take action on minimizing the risks and maximizing the benefits, and provide the necessary wisdom and support, which we, the transition generation, lacked.

Kathleen Esfahany, Computer Science & Neurology, MIT, USA

I was born in 2000 to two computer scientists. Although technological innovation dramatically changed my life with each passing year, the shift into a technology-filled world felt entirely natural to me. The phenomenon of mirrored growth helped cement my identity as a digital native: for much of my childhood, each milestone in my own cognitive development was mirrored by technological advances and a deepening immersion in technology as an educational and social tool.

As my curiosity about the world grew, online news and social media proliferated, making it possible for me to follow events and connect with others across the globe. I can also thank my parents, who used their expertise on computers to help me understand the seemingly magical devices around me, empowering me to think about how to use technology for my own purposes and create technology of my own.

Todays youth are growing up with rapid advances in AI. Already, we are seeing how the unprecedented efficiency and personalization of AI-powered technology can elevate todays youths ability to learn, form personal relationships, and create joy. To optimize it for childrens safety and emotional well-being, I believe it is critical that AI-powered technology is designed so that it can match the diverse needs and abilities found throughout childhood development. My hope is that the combination of well designed AI-powered technology for youth and educational programs about AI will empower and inspire todays youth to harness AI responsibly to bring to life their visions for the future.

See original here:
AI ethics: digital natives on protecting future generations | World Economic Forum - World Economic Forum

Have a look at the top 5 benefits of using Artificial intelligence in software testing

One of the recent buzzwords in the software development industry is artificial intelligence. Even though the use of artificial intelligence in software development is still in its infancy, the technology has already made great strides in automating software development. Integrating AI in software testing enhanced the quality of the end product as the systems adhere to the basic standards and also maintain company protocols. So, let us have a look at some of the other crucial benefits offered by AI in software testing.

A method of testing that is getting more and more popular every day is image-based testing using automated visual validation tools. Many ML-based visual validation tools can detect minor UI anomalies that human eyes are likely to miss.

Shared automated tests can be used by the developers to catch problems quickly before sending them to the QA team. Tests can be run automatically whenever the source code changes, checked in and notified the team or the developer if they fail.

Manual testing is a slow process. And every code change requires new tests that consume the same amount of time as before. AI can be leveraged to automate the test processes. AI provides for precise and continuous testing at a fast pace.

AI/ ML tools can read the changes made to the application and understand the relationship between them. Such self-healing scripts observe changes in the application and start learning the pattern of changes and then can identify a change at runtime without you having to do anything.

With software tests being repeated each time source code is changed, manually happening those tests can be not only time-consuming but also expensive. Interestingly, once created automated tests can be executed over and over, with zero additional cost at a much quicker pace.

Conclusion: The future of artificial intelligence and machine learning is bright. AI and its adjoining technologies are making new waves in almost every industry and will continue to do so in the future.

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Analytics Insight is an influential platform dedicated to insights, trends, and opinions from the world of data-driven technologies. It monitors developments, recognition, and achievements made by Artificial Intelligence, Big Data and Analytics companies across the globe.

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Top 5 Benefits of Artificial intelligence in Software Testing - Analytics Insight

Artificial intelligence in medicine is the use of machine learning models to search medical data and uncover insights to help improve health outcomes and patient experiences. Thanks to recent advances in computer science and informatics, artificial intelligence (AI) is quickly becoming an integral part of modern healthcare. AI algorithms and other applications powered by AI are being used to support medical professionals in clinical settings and in ongoing research.

Currently, the most common roles for AI in medical settings are clinical decision support and imaging analysis. Clinical decision support tools help providers make decisions about treatments, medications, mental health and other patient needs by providing them with quick access to information or research that's relevant to their patient. In medical imaging, AI tools are being used to analyze CT scans, x-rays, MRIs and other images for lesions or other findings that a human radiologist might miss.

The challenges that the COVID-19 pandemic created for many health systems also led many healthcare organizations around the world to start field-testing new AI-supported technologies, such as algorithms designed to help monitor patients and AI-powered tools to screen COVID-19 patients.

The research and results of these tests are still being gathered, and the overall standards for the use AI in medicine are still being defined. Yet opportunities for AI to benefit clinicians, researchers and the patients they serve are steadily increasing. At this point, there is little doubt that AI will become a core part of the digital health systems that shape and support modern medicine.

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Artificial Intelligence in Medicine | IBM