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

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

To many, AI is just a horrible Steven Spielberg movie. To others, its the next generation of learning computers. But what is artificial intelligence, exactly? The answer depends on who you ask. Broadly, artificial intelligence (AI) is the combination of computer science and robust datasets, deployed to solve some kind of problem.

Many definitions of artificial intelligence include a comparison to the human mind or brain, whether in form or function. Alan Turing wrote in 1950 about thinking machines that could respond to a problem using human-like reasoning. His eponymous Turing test is still a benchmark for natural language processing. Later, Stuart Russell and John Norvig observed that humans are intelligent, but were not always rational. Russell and Norvig saw two classes of artificial intelligence: systems that think and act like a human being, versus those that think and act rationally. Today, weve got all kinds of programs we call AI.

Many AIs employ neural nets, whose code is written to emulate some aspect of the architecture of neurons or the brain. However, not all intelligence is human-like. Nor is it necessarily the best idea to emulate neurobiological information processing. Thats why engineers limit how far they carry the brain metaphor. Its more about how phenomenally parallel the brain is, and its distributed memory handling. As defined by John McCarthy in 2004, artificial intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.

Moreover, the distinction between a neural net and an AI is often a matter of philosophy, more than capabilities or design. Many AI-powered systems are neural nets, under the hood. We also call some neural nets AIs. For example, OpenAIs powerful GPT-3 AI is a type of neural net called a transformer (more on these below). A robust neural nets performance can equal or outclass a narrow AI. There is much overlap between neural nets and artificial intelligence, but the capacity for machine learning can be the dividing line.

Conceptually: In the sense of its logical structure, to be an AI, you need three fundamental parts. First, theres the decision process usually an equation, a model, or just some code. AIs often perform classification or apply transformations. To do that, the AI must be able to decide on patterns in the data. Second, theres an error function some way for the AI to check its work. And third, if the AI is going to learn from experience, it needs some way to optimize its model. Many neural networks do this with a system of weighted nodes, where each node has both a value and a relationship to its network neighbors. Values change over time; stronger relationships have a higher weight in the error function.

Deep learning networks have more hidden layers than conventional neural networks. Circles are nodes, or neurons.

Physically: Typically, an AI is just software. AI-powered software services like Grammarly and Rytr use neural nets, like GPT-3. Those neural nets consist of equations or commands, written in things like Python or Common Lisp. They run comparisons, perform transformations, and suss out patterns from the data. They run on server-side hardware, usually, but which hardware isnt important. Any conventional silicon will do, be it CPU or GPU. However, there are dedicated hardware neural nets, a special kind of ASIC called neuromorphic chips.

Not all ASICs are neuromorphic designs. However, neuromorphic chips are all ASICs. Neuromorphic design is fundamentally different from CPUs, and only nominally overlaps with a GPUs multi-core architecture. But its not some exotic new transistor type, nor any strange and eldritch kind of data structure. Its all about tensors. Tensors describe the relationships between things; theyre a kind of mathematical object that can have metadata, just like a digital photo has EXIF data.

Modern Nvidia RTX GPUs have a huge number of tensor cores. That makes sense if youre drawing moving polygons, each with some number of properties or effects that apply to it. But tensors can handle more than just spatial data. The ability to parallelize tensor calculations is also why GPUs get scalped for crypto mining, and why theyre used in cluster computing, especially for deep learning. GPUs excel at organizing many different threads at once.

But no matter how elegant your data organization might be, it still has to filter through multiple layers of software abstraction before it ever becomes binary. Intels neuromorphic chip, Loihi 2, affords a very different approach.

Loihi 2 is a neuromorphic chip that comes as a package deal with a software ecosystem named Lava. Loihis physical architecture invites almost requires the use of weighting and an error function, both defining features of AI and neural nets. The chips biomimetic design extends to its electrical signaling. Instead of ones and zeroes, on or off, Loihi fires in spikes with an integer value capable of carrying much more data. It begs to be used with tensors. What if you didnt have to translate your values into machine code and then binary? What if you could just encode them directly?

Machine learning models that use Lava can take full advantage of Loihi 2s unique physical design. Together, they offer a hybrid hardware-software neural net that can process relationships between multiple entire multi-dimensional datasets, like an acrobat spinning plates.

AI tools like Rytr, Grammarly and others do their work in a regular desktop browser. In contrast, neuromorphic chips like Loihi arent designed for use in consumer systems. (At least, not yet.) Theyre intended for researchers. Instead, neuromorphic engineering has a different strength. It can allow silicon to perform another kind of biomimicry. Brains are extremely cheap, in terms of power use per unit throughput. The hope is that Loihi and other neuromorphic systems can mimic that power efficiency to break out of the Iron Triangle and deliver all three: good, fast, and cheap.

If the three-part logical structure of an AI sounds familiar, thats because neural nets have the same three logical pillars. In fact, from IBMs perspective, the relationship between machine learning, deep learning, neural networks and artificial intelligence is a hierarchy of evolution. Its just like the relationship between Charmander, Charmeleon and Charizard. Theyre all separate entities in their own right, but each is based on the one before, and they grow in power as they evolve. We still have Charmanders even though we also have Charizards.

Artificial intelligence as it relates to machine learning, neural networks, and deep learning. Image: IBM

When an AI learns, its different than just saving a file after making edits. To an AI, learning involves changing its process.

Many neural nets learn through a process called back-propagation. Typically, a neural net is a feed-forward process, because data only moves in one direction through the network. Its efficient, but its also a kind of ballistic (unguided) process. In back-propagation, however, later nodes in the process get to pass information back to earlier nodes. Not all neural nets perform back-propagation, but for those that do, the effect is like changing the coefficients in front of the variables in an equation.

We also divide neural nets into two classes, depending on what type of problems they can solve. In supervised learning, a neural net checks its work against a labeled training set or an overwatch; in most cases, that overwatch is a human. For example, SwiftKey learns how you text, and adjusts its autocorrect to match. Pandora uses listeners input to finely classify music, in order to build specifically tailored playlists. 3blue1brown even has an excellent explainer series on neural nets, where he discusses a neural net using supervised learning to perform handwriting recognition.

Supervised learning is great for fine accuracy on an unchanging set of parameters, like alphabets. Unsupervised learning, however, can wrangle data with changing numbers of dimensions. (An equation with x, y and z terms is a three-dimensional equation.) Unsupervised learning tends to win with small datasets. Its also good at recognizing patterns we might not even know to look for.

Transformers are a special, versatile kind of AI capable of unsupervised learning. They can integrate many different streams of data, each with its own changing parameters. Because of this, theyre great at handling tensors. Tensors, in turn, are great for keeping all that data organized. With the combined powers of tensors and transformers, we can handle more complex datasets.

Video upscaling and motion smoothing are great applications for AI transformers. Likewise, tensors are crucial to the detection of deepfakes and alterations. With deepfake tools reproducing in the wild, its a digital arms race.

The person in this image does not exist. This is a deepfake image created by StyleGAN, Nvidias generative adversarial neural network.

Video signal has high dimensionality. Its made of a series of images, which are themselves composed of a series of coordinates and color values. Mathematically and in computer code, we represent those quantities as matrices or n-dimensional arrays. Helpfully, tensors are great for matrix and array wrangling. DaVinci Resolve, for example, uses tensor processing in its (NVidia RTX) hardware-accelerated Neural Engine facial recognition utility. Hand those tensors to a transformer, and its powers of unsupervised learning do a great job picking out the curves of motion on-screen and in real life.

In fact, that ability to track multiple curves against one another is why the tensor-transformer dream team has taken so well to things like natural language processing. And the approach can generalize. Convolutional transformers a hybrid of a CNN and a transformer excel at image recognition on the fly. This tech is in use today, for things like robot search and rescue or assistive image and text recognition, as well as the much more controversial practice of dragnet facial recognition, la Hong Kong.

The ability to handle a changing mass of data is great for consumer and assistive tech, but its also clutch for things like mapping the genome, and improving drug design. The list goes on. Transformers can also handle different kinds of dimensions, not just the spatial, which is useful for managing an array of devices or embedded sensors like weather tracking, traffic routing, or industrial control systems. Thats what makes AI so useful for data processing at the edge.

Not only does everyone have a cell phone, there are embedded systems in everything. This proliferation of devices gives rise to an ad hoc global network called the Internet of Things (IoT). In the parlance of embedded systems, the edge represents the outermost fringe of end nodes within the collective IoT network. Edge intelligence takes two main forms: AI on edge and AI for edge. The distinction is where the processing happens. AI on edge refers to network end nodes (everything from consumer devices to cars and industrial control systems) that employ AI to crunch data locally. AI for the edge enables edge intelligence by offloading some of the compute demand to the cloud.

In practice, the main differences between the two are latency and horsepower. Local processing is always going to be faster than a data pipeline beholden to ping times. The tradeoff is the computing power available server-side.

Embedded systems, consumer devices, industrial control systems, and other end nodes in the IoT all add up to a monumental volume of information that needs processing. Some phone home, some have to process data in near real-time, and some have to check and correct their own work on the fly. Operating in the wild, these physical systems act just like the nodes in a neural net. Their collective throughput is so complex that in a sense, the IoT has become the AIoT the artificial intelligence of things.

As devices get cheaper, even the tiny slips of silicon that run low-end embedded systems have surprising computing power. But having a computer in a thing doesnt necessarily make it smarter. Everythings got Wi-Fi or Bluetooth now. Some of it is really cool. Some of it is made of bees. If I forget to leave the door open on my front-loading washing machine, I can tell it to run a cleaning cycle from my phone. But the IoT is already a well-known security nightmare. Parasitic global botnets exist that live in consumer routers. Hardware failures can cascade, like the Great Northeast Blackout of summer 2003, or when Texas froze solid in 2021. We also live in a timeline where a faulty firmware update can brick your shoes.

Theres a common pipeline (hypeline?) in tech innovation. When some Silicon Valley startup invents a widget, it goes from idea to hype train to widgets-as-a-service to disappointment, before finally figuring out what the widgets actually good for.

Oh, okay, there is an actual hypeline. Above: The 2018 Gartner hype cycle. Note how many forms of artificial intelligence showed up on this roller coaster then and where they are now. Image: Gartner, 2018

This is why we lampoon the IoT with loving names like the Internet of Shitty Things and the Internet of Stings. (Internet of Stings devices communicate over TCBee-IP.) But the AIoT isnt something anyone can sell. Its more than the sum of its parts. The AIoT is a set of emergent properties that we have to manage if were going to avoid an explosion of splinternets, and keep the world operating in real time.

In practice, artificial intelligence is often the same thing as a neural net capable of machine learning. Theyre both software that can run on whatever CPU or GPU is available and powerful enough. Neural nets often have the power to perform machine learning via back-propagation. Theres also a kind of hybrid hardware-and-software neural net that brings a new meaning to machine learning. Its made using tensors, ASICs, and neuromorphic engineering by Intel. Furthermore, the emergent collective intelligence of the IoT has created a demand for AI on, and for, the edge. Hopefully we can do it justice.

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What Is Artificial Intelligence? - ExtremeTech

Not having policies in place to regulate artificial intelligence (AI) and machine learning (ML) could have dire consequences across every sector of the health care industry.

That was the point made by Brian Scarpelli and Sebastian Holst during their presentation titled A modest proposal for AI regulation in healthcare, held during the HIMSS22 Global Health Conference in Orlando. Scarpelli is the senior global policy counsel for the Connected Health Initiative and Holst is principal with Qi-fense, a consulting group that works in AI and ML.

ML properties do more than challenge domain-specific applications of technology, Scarpelli and Holst write. Many of these properties will force an evaluation and retooling of core manufacturing, quality, and risk frameworks that have effectively served as the foundation of todays industry-specific regulations and policies.

Heres some key points from their presentation on the growth of AI/ML and the need for regulation.

AI can potentially revolutionize health care in all facets. It can reduce administrative burdens for providers and payer and allow for resources to be deployed within a health system to serve vulnerable patient populations. It can manage public health emergencies such as the COVID-19 pandemic and help improve both preventive care and diagnostic efficiency.

According to Scarpelli and Holst, the growth in machine learning products has surged since 2015, starting first with processing applications, including products for processing radiological images, and has since progressed into diagnosis applications, particularly also in the radiological space to assist with triage and prioritization.

The number of patents coded to machine learning and health informatics has exploded, from 165 in 2017 to more than 1,100 in 2021.

While AI is promising, there are potential legal and ethical challenges that must be addressed. For example, one of the major themes of the HIMSS22 conference has been the challenge of achieving health equity and eliminating implicit bias. Thats one of the major challenges of AI as well since AI solutions can be biased. Many sessions focused on how diverse teams are needed when creating AI solutions to ensure that the programs dont carry the same biases as society, which could exacerbate current social problems, according to Tania M. Martin-Mercado, MS, MPH, a clinical researcher who presented on How implicit bias affects AI in healthcare.

During her presentation, she pointed to an example of an online tool that estimates risk of breast cancer calculates a lower risk for Black or Latinx women than White even when every other risk factor is identical.

A diverse group of health agencies, including the FDA, HHS, CMS, FTC, and the World Health Organization, are developing regulations and asking from guidance from various stakeholders, including AI developers, physicians and other providers, patients, medical societies, and academic institutions.

Scarpelli says that the vision for successful AI follows four principals. It should:

This article originally appeared on the website MedicalEconomics.com

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Policy experts stress the need to regulate artificial intelligence in health care - Urology Times

UNSW Engineering Professor Flora Salim has been honoured for her pioneering work in computing and machine learning by Women in AI, aglobal advocacy group for women in the artificial intelligence (AI) field.

The 2022 Women in AI Awards Australia and New Zealand recognised women across various industries committed to excellence in AI.

Finalists were judged on innovation, leadership and inspiring potential, global impact, and the ability of the AI solution to provide a social good for the community.

Prof. Salim was recognised for her AI achievements in the Defence and Intelligence award category.

The award acknowledged her research in the cross-cutting areas of ubiquitous computing and machine learning, with a focus on efficient, fair, and explainable machine learning for multi-dimensional sensor data, towards enabling situational and behaviour intelligence for multiple applications.

I am thrilled and honoured to receive this award. This highlights our efforts into advancing AI and machine learning techniques for sensor data, Prof. Salim said.

I would like to acknowledge my students, postdocs, collaborators, and mentors. I hope we can inspire more women to join us towards solving difficult AI problems that matter.

Prof. Salim is the inaugural Cisco Chair in Digital Transport in the School of Computer Science and Engineering at UNSW Sydneyand a member of the Australian Research Council (ARC) College of Experts, having recently moved fromRMIT Universitys School of Computing Technologies, Melbourne.

Her research on human-centred computing AI and machine learning for behaviour modelling with multimodal spatial-temporal data has received funding from numerous partners, resulting in more than 150 papers and three patents.

Research led by Prof. Salim with collaborators from Microsoft Research and RMIT University on task characterisation and automating task scheduling led to insights that influenced the research and development of several new Microsoft product features.

UNSW Dean of Engineering, Professor Stephen Foster congratulated Prof. Salim on receiving an award that promotes women in the AI sector.

Artificial intelligence will reshape every corner of our lives in the coming years, so its pleasing to see brilliant women recognised for shaping the future of AI, Prof. Foster said.

I congratulate Prof. Salim for being on the forefront of AI today.

Women in AI is a global not-for-profit network working towards empowering women and minorities to excel and be innovators in the AI and Data fields.

The awards were held at a gala dinner at the National Gallery of Victoria in Melbourne.

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UNSW researcher receives award recognising women in artificial intelligence - UNSW Newsroom