Category Archives: Ai

The people who practice Data science consolidate a scope of abilities to break down data to infer significant experiences.

Data science consolidates various fields, including measurements, logical techniques, computerized reasoning (simulated intelligence), and information examination, to arrange it into organized information. The people who practice Data science are called information researchers, and they consolidate a scope of abilities to break down data gathered from the web, cell phones, clients, sensors, and different sources to infer significant experiences. Data science envelops getting ready information for examination, including purging, amassing, and controlling the information to perform progressive information investigation. Scientific applications and information researchers can then survey the outcomes to uncover designs and empower business pioneers to draw informed experiences. Information researchers here play an important role in the entire process.

Artificial intelligence (simulated intelligence) alludes to the reproduction of human knowledge in machines that are customized to think like people and copy their activities. The term artificial intelligence may likewise be applied to any machine that displays qualities related to a human brain, for example, learning and critical thinking. The ideal quality of artificial intelligence is capacity to support and make moves having the most obvious opportunity with regards to accomplishing a particular objective. A subset of man-made brainpower is AI, which alludes to the idea that PC projects can naturally gain from and adjust to new information without being helped by people.

Machine learning is a part of information investigation that carries out an all-around planned logical model for a particular issue. In a laymans language, it is a branch cut out of man-made reasoning by which it is intended to gain from information, recognize designs, process examples, and pursue choices with limiting possibilities of mistakes. This is accomplished with negligible human intercession. Machine learning is the investigation of PC calculations that can work naturally through experience and by the utilization of data. AI calculations assemble a model in light of test information, known as preparing information, to pursue forecasts or choices without being expressly customized to do as such.

Python is the most ordered programming language utilized in DS/ AI and ML spaces. Its not difficult to utilize an open-source programming language with a wide client base and extremely itemized and continually refreshed documentation. One can program, script, picture, deductively register, and web scratch utilizing Python. Information design, seclusion, and Item Direction functionalities in Python are ideal for application improvement utilizing information science. Information researchers use Python for different cycles like making monetary models, web scratching information, making recreations, web advancement, information representation, and others. There is a very much tried bundle for practically any issue in Python.

R is one more programming language generally utilized in the information science industry. R is more helpful for information representation and going with choices utilizing graphical information. It is exceptionally simple to learn and is all around reported. There are many free web-based assets to learn R. R is utilized as a superb information science programming apparatus in numerous enterprises like medical care, internet business, banking, and others.

Practically every one of the significant ventures is moving from in-house servers to some type of cloud computing. Further, the applications are created as a bunch of free microservices that are conveyed and run on the cloud. Cloud computing permits associations to scale their IT system as per the requests and save both activity cost and capital speculation. All significant DS programs are intended to productively construct and run on the cloud. Central parts like Microsoft (Sky blue), Amazon (AWS), Google (GCP), and (IBM Cloud) have their own business DS contributions running over cloud arrangements.

Statistics, probability, and mathematics are the premise of Data Science, Artificial Intelligence, and Machine Learning. One cant plan hearty ML calculations without having solid groundwork in these three fields. It is inordinately difficult to extricate significant bits of knowledge from unstructured informational indexes. Measurement is an unquestionable requirement to do information arrangement and investigation. Information researchers ordinarily suggest one model from an assortment of models subsequent to running different factual tests on the aftereffect of each model to pick the best model. Also, many existing models like NaiveBayes or Backing Vector Machine (SVM) require knowledge of probability and mathematics to understand the fundamental condition.

Artificial intelligence is generally utilized to computerize the information examination frameworks and estimate all the more precisely. Information researchers can infer constant noteworthy bits of knowledge with computer-based intelligence that is all around upheld with information. The use of artificial intelligence has proactively made numerous manual positions out of date. Simulated intelligence observes wide applications in picture handling, normal language handling, PC vision, etc.

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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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What are the AI and Data Science Skills that Leaders should Master? - Analytics Insight

With much of the national security community's attention now turned to long-term competition with China, the race to sustain the United States global leadership position in science and technology has taken center stage. One of the most frequently discussed aspects of this competition is artificial intelligence (AI), which is often mentioned as a potentially transformative technology.

Last year, the National Security Commission on Artificial Intelligence found that China is determined to surpass the United States in AI leadership, particularly in military applications. Top academics and industry thinkers agree that China is making significant headway toward this goal and is rapidly catching up with the United States research progress, leading a disgruntled former Pentagon official to suggest that the United States is already losing the race for AI superiority. Close analysis supports claims that China has indeed established areas of advantage, especially in data collection technology for surveillance and facial recognition.

This anxiety extends to the U.S. military enterprise as well. The militarys focus on AI has played out not only through organizational changes like the creation of the Joint Artificial Intelligence Center (JAIC) and a focus on AI in recent strategic documents, but also through a shift in its approach to warfighting. The central Department of Defense effort to modernize its methods of fightingslickly named Joint All-Domain Command and Control (JADC2)identifies "AI, machine learning, and predictive analytics" as the core enablers that will allow the future military to defeat any adversary at any time and in any place around the globe. As part of its JADC2 efforts, the Department of Defense is seeking to enforce compliance with new universal standards, including by building a "joint operating system" that will force a top-down centralization of military data storage and accessibility.

On paper, there is a clear appeal to this approach. It feels efficient, and it allows us to dismiss the messy, intensely human reality of war by envisioning a seamless, rapid flow of data, knowledge, and decisions through the United States warfighting machine, allowing it to dominate the enemy. However, like outfitting an 1890s office building with pneumatic tubes, connecting a military enterprise to today's rather immature conception of AI might seem modern, but it has the potential to be a liability down the road. Indeed, it might play to U.S. interests to let China take the lead. Watch as they connect every aspect of their military to centralized systems that sweep up data for use in current AI approachesapproaches built around big data machine learning and todays imperfect classification algorithmsand issue commands intended to destroy opposing forces without the need to trust their military operators. This science-fiction version of military AI could be a liability because, at the end of the day, military success has more to do with surprise than efficiency.

When considering the dynamics of competition between the United States and China, it is important to recognize that nations have asymmetric motivations and capabilities to develop and deploy AI. China's single-party political system still depends to some extent on the support of its people to maintain legitimacy. In the absence of democratic elections, surveilling a populace and using the data to measure sudden anomalies is a powerful tool to monitorand respond tothe sentiment of the people. Moreover, China has strong incentives to leverage technology to overcome the failings of past one-party systems. While it also has a capable and sometimes freewheeling private sector, strong state control helps China ensure the people hew to its national mandates.

Chinese president Xi Jinping has voiced persistent doubts about the ability of Chinese military officers to make combat leadership decisions, asserting that some officers cannot understand higher authorities intentions or make operational decisions under pressure. Even though Chinas military enterprise has made remarkable progress in building systems from stealth drones to hypersonic missiles, this foundational aspect of combat remains a key issue. This helps explain the Chinese militarys fascination with AI, which would seem to present a way to paper over leadership concerns, with machines dominating the battlefield in the same way that Chinese AI can beat the best video game players in the world.

Meanwhile, the United States draws from a highly decentralized system that supports and drives a vibrant commercial sector. American industry has harnessed the seemingly magical power of AI to drive online advertising. And in any given year, there are dozens of startups figuring out how to leverage machine learning to build and monetize better digital mousetraps. At the same time, the U.S. military struggles to operationalize AI. In the four years since the JAIC was founded, it hasn't made visible progress toward delivering access to troves of military data for users of its joint common foundation toolkit. That's no easy task, given the context-specific nature of data, complex security environment, and distinct service cultures that make sharing frustratingly difficult. However, perhaps this isnt the catastrophe that corporate executives would like us to think.

It appears that leaders are distracted by powerful examples of AI applications that are not representative of actual warfare. For China, these examples include domestic surveillance efforts designed to ensure internal harmony. On the other hand, American leaders are drawn to the prevalence of AI in commercial applications, especially the applicability of AI for improving operations efficiency, corporate decision-making, and crossing information technology stovepipes. But despite a superficial similarity, the rule-bound world of video games has little to do with the horrors of actual military conflict.

The shortfalls of todays AI technologies are clear. Machine learning technologys dependence on black-box processing of historical data creates deep and systemic vulnerabilities. For instance, there are several well-publicized examples of self-driving cars misidentifying road signs due to subtle perturbations that humans cant see. Voice commands are no more secure, as audio processing systems can also be fooled with faint noises that dont sound like speech to humans. This is to say nothing of the threat of adversaries intruding into centralized AI command and control systems or the risks from communications interruption between algorithms and actions. Even when a computing environment is secure and algorithms arent fooled by hidden features, there is no way to know if adversarial data was accidentally collected a decade ago and used to improperly train a system. While research is underway to help mitigate these issues, a better approach is to leverage architectures that dont present attractive centralized targets, dont vacuum up data from all sources, and dont force military users into common standards or platforms.

Conflict is an intrinsically human phenomenon, and people will always be a part of military decision-making. With the ubiquity of computing and communication, the real question for the U.S. military is not whether AI will be used in military applications, but how to architect it. The United States could choose to trust hierarchy, using centralized mandates and common standards and platforms to accelerate decisions, ultimately imitatingeven chasingour strategic competitor. Alternatively, it could embrace heterogeneity and pursue a federated model with an array of competing projects scattered across the Department of Defense. This latter model delegates trust to decisionmakers throughout the Pentagon, accepting the intrinsic risk it invites. Crucially, it also plays to the United States strengths.

In this alternative model, leaders would seek to promote the sharing of concepts and technology by centrally funding infrastructure like code repositories, but they wouldnt seek to converge projects or standards. They would instead look to amplify success where it emerges.

The result might resemble something of a zero trust architecture for decision-making. In cybersecurity, a zero trust architecture recognizes that it is nearly impossible to make any system invulnerable to compromise and instead focuses on minimizing the risk and collateral damage of a successful attack. If any system is compromised, the effects cant cascade and collapse the entire enterprise. While this sounds appealing, it comes with tradeoffs, namely that it forgoes the efficiency gains that centralized AI systems bring. Businesses that adopt modern tools like Palantir or C3.AI seek to place all of their enterprise data under a single pane of glass to maximize the operational payoff of decision support algorithms. Instead, a federated system offers the advantage of surpriseone can never be quite sure what individuals will dofor better or worse. But this unpredictability is resilient and inflicts costs on the adversary. Moreover, the United States is uniquely positioned to take advantage of a federated model.

The most valuable long-term potential of AI is not displacing humans from their tasks, automating decision-making, or modernizing weapon systems. Instead, over the decades to come, AI will permit us to richly recombine intelligencein both human and machine formsaround collective problem-solving. We are only in the early stages of this journey. However, strategic advantages will come from harnessing the United States unique strengths, including the messy, cantankerous character of its system of governance and national history.

Melissa Flagg is a visiting fellow at the Perry World House, a fellow at the Acquisition Innovation Research Center, and an advisor to the Andrew W. Marshall Foundation. She is also a former Deputy Assistant Secretary of Defense for Research with over fifteen years in defense research and engineering. She can be found on Twitter @flaggster73

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AI Competition With China Should Be Done the American Way - The National Interest Online

Suggesting that some radiologists may not be aware of the intended use of computer-aided triage and notification (CADt) devices, the Food and Drug Administration (FDA) has issued an advisory on the use of the imaging software for patients with suspected large vessel occlusion (LVO) in the brain.

Emphasizing proper use of CADt software, the FDA notes these devices are not intended to substitute for diagnostic assessment by radiologists. While CADt devices can help flag and prioritize brain imaging with findings that are suspicious for LVO, the advisory points out that an LVO, a common cause of acute ischemic strokes, may still be present even if it is not flagged by the CADt imaging software.

If there is any potential over-reliance on CADt software, Vivek Bansal, MD said it may stem from a team of health-care providers striving to do the right thing for the patient under tight time constraints. While interventionalists, neurosurgeons and neurologists all have strong knowledge of brain vessels, there may be different levels of experience, according to Dr. Bansal, the national subspecialty lead for neuroradiology at Radiology Partners. He added that while these specialists look closely at images they take in the operating suite, they may not look at the actual CT images to the same level.

In regard to the imaging, Dr. Bansal said one may be looking at tiny branching vessels that are diving up and down into different slices of the images, and you have to scroll up and down to really trace them out vessel by vessel. This can be challenging and particularly hard to do on a smartphone in a brightly lit room, pointed out Dr. Bansal.

The clock is ticking, and time is brain. We are trying to race against the clock because every minute we take to arrive at a diagnosis, more brain cells may be dying (if the patient has a clot). The quicker we can get them to a diagnosis and the patient gets to a cath lab, the better the outcomes for the patient. I think that is the biggest challenge: trying to do something that is very meticulous in a very small amount of time, explained Dr. Bansal.

The FDA advisory also maintained that it is important to have awareness of the design capabilities of different CADt devices, many of which have artificial intelligence (AI) or machine learning technology, For example, the FDA cautioned that LVO CADt devices may not assess all intracranial vessels. Dr. Bansal said this is an important distinction with AI tools.

While some AI tools are very good at looking at an M1 occlusion, which is the proximal part of the middle cerebral artery, the newer AI tools are capable of looking at M2 occlusions with proximal anterior cerebral artery (ACA) and posterior cerebral artery (PCA) occlusions. All of these things are important in terms of patient care, maintained Dr. Bansal, who is affiliated with the East Houston Pathology Group in Texas.

Dr. Bansal said the key is understanding the role of AI-enabled devices and their value in triaging cases.

At any given moment, I might have 40 stat exams on my list. Im cranking through them as fast as I can but if AI tools are saying 'Hey, look at this one next, whether it is a potential large vessel occlusion or brain bleed, that is very helpful, suggested Dr. Bansal. Where we are at right now, I think that the only way we can look at AI is to look at it as a triaging tool.

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FDA Issues Advisory on Use of AI and Machine Learning for Large Vessel Occlusion in the Brain - Diagnostic Imaging

AI-powered farming has enhanced the food production process to a great extent

The world population will soon touch 8 million and that comes with farming challenges. Naturally, with the human population, food production should also increase and old agricultural methods might not do the job. Thanks to artificial intelligence, we can now reinvent farming with AI-powered vertical farming.

AI-powered vertical farming opens up many opportunities for farms in urban and industrial areas. It can produce crops in all seasons and in cost-controlled ways in any type of space. Automation through artificial intelligence, machine learning, and robotics is a viable option to bring down the cost of labor and other operations. This is a boon for regions where the farmers are reaching old age.

Seeing these advantages, Asian cities in China and Japan are the first ones to adopt this technology with the help of many indoor vertical farms. This method allows smaller farmers as well big farming companies to grow yield according to the customers demand. This is a lifestyle-changing new-age farming method.

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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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AI-Powered Farming is the Future of Food Production - Analytics Insight

Putting more and more performance capabilities on a semiconductor chip is a challenge, says Sandy Saper, head of HW, ASIC & FPGA development at Ready. But the ability to put more on chips through time has brought us to todays technology, he shares. Developing a chip involves a variety of disciplines including architecture, design, and verification. Ensuring the chips work properly is critical because bugs can be very costly and time consuming to fix, he says. In addition to reaching the performance requirements, an engineer also has to think about how to meet them more efficiently, Saper explains. Ready has teams working on hardware as well as software, and Sapers hardware team is now developing chips for artificial intelligence and supercomputers. He says that looking forward, engineers need to think about how to have more than one chip communicate as the demand for higher performance grows.

I fell in love with what's happening with chip design recently. I'd love to hear about your career journey and how you got into chip design.

I've been 30 years in the development of chips. Now I manage a hardware team that designs and develops chips for the semiconductor industry. I work at Ready, which is a private company in Israel. Half of the company is hardware, and half is software.

I was always interested in electricity as a teenager. When I signed up for university, I said, "Let's go into electricity." I had no idea what chips were then. But during my studies, I started to learn about all the amazing things electricity does. Then I started to learn about chip design. The two of us clicked, me and chip design. Ever since then, I've been developing chips from the start.

The interest in electricity and chip design, did it stem mainly from the academic side or the challenge side? What did you understand about where the world was going and the role chip design was going to play?

I was less into the academics and more into the hands-on and do it. You have an idea, get it done. I really didn't think back then, in my early 20s, where this was going. Back then, the technologies were in their infancy. When I'd finished university, there were already low-end microcontrollers for simple tasks, for computers. Just then, the first desktops were being produced.

Every year or two, there's another shrink where you can combine more and more in one chip. It's Moore's Law, and it's been going on ever since. We've been cramming more and more functions in every chip, which has enabled us to get where we are today. Cramming more and more performance in a single chip is more and more challenging for the design engineers, architects, and electronic engineers.

Why is chip design still relevant today? What advancements do we still have to make?

Artificial intelligence is just starting. Supercomputing is just starting. We work on chips that are serving the artificial intelligence and supercomputer markets. Without the supercomputers we have today, we could not have developed the vaccines that we are using for helping us cope with COVID-19. Supercomputers need super chips, and that's what we do.

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Sandy Saper, head of HW, ASIC & FPGA Development at Ready

(Photo: Idan Canfi)

Tell me about Ready, about where you're at today.

Ready is a company that has hardware and software. When you want to develop a chip, you need to know you come with requirements. It starts off with understanding your requirements to meet the performance, the price, and the power that you need.

There are a lot of disciplines to make a chip that meets the requirements. It starts off with the architecture. How do I even put all these blocks together so that they will function with each other? What's going to be in hardware and what is going to be in software is very critical. We in Ready can decide for the customer what can be in hardware and what can be in software. The architecture team decides how we are going to put this chip together to meet the performance and can work on it for between a month and half a year to make sure that the end product is going to meet what you need.

Then there's a design team, which has the expertise on how to design each actual block to meet the performance. Then after they have finished the design, it needs to be fully verified. We have a high quality verification team to make sure that what we have designed is going to fully work and there are no bugs. We cannot allow bugs. Then we have to change all the code that was written and translate that into the physical chip. Then we have to validate that the physical meets the requirements.

Every stage takes a few months, depending on the size of the chip. Then we have to validate that what we have done is exact. Then we ship it off to the factory. It costs millions of dollars to just prepare the chip for the factory, for the fab. The fabs are the highest technology factories in the world. Super clean, very high quality.

I cannot ship a chip that has a bug because it's going to cost me a million dollars to fix it. The time it takes to fabricate a high-end chip in the fab can take three to four months, and the time to market is critical. So if, God forbid, you have a bug in your chip, you will only detect that when you get the silicon after four or five months. At Ready, we have an excellent team that does the verification, the design, the architecture, and the final product.

Then after it's gone through all the stages in the factory, it needs to be packaged. We receive the chip and we validate, before we ship it to the customer, that it meets all the performance that we designed it for. That can also take a few weeks to validate that everything is perfect and that you don't have a field failure.

Coming from the software world, I was taught that as long as the bug is not huge, ship it and then roll out an update once you get some feedback. But it sounds like the detail that you're dealing with is an incredible amount. So I'm imagining that if I thought my software was complicated and had bugs, designing chips is just a little bit more detailed, right?

The code is very complicated to meet the requirements. You need a high-quality team and managers to manage that everything is fitting each other, everything comes on time, everything is working together till it works, until we ship it off to the factory.

What does a chip designer do when they get to the office in the morning?

After having the first cup of coffee, interaction with your co-workers is very important. To understand the requirements and how do I implement them so that it's efficient, it will function, and there's minimum chance of a bug. You go to your work, understand what the functionality is that I have to implement in this block. Or if it's a verification engineer, how am I going to write the code that is going to verify that the design is really clean?

We have tools that we use, and a lot of automation is in the process. We think part of the engineer's job is also to see how we can do this more efficiently. Every generation gets more and more complicated because we can put in more and more. So we're always thinking, "How can I do this more efficiently next time? Or now?" Which is very important since the demand for resources is high. The more efficient you are, the better.

As we're transitioning to the future, where are semiconductors positioned with the younger generation as opposed to software?

One basic axiom is that "software has to run on hardware." I always tell my software colleagues, "Without us, you couldn't develop anything." I think that we are going to have to think about supercomputing and how much we can really cram into one chip. How do I think of supercomputing? In other words, supporting multiple chips. Communication between the chips is very critical, which is also being worked on. That's the way I see it going forward. We now have to think about how we have multiple chips in the system, how are they going to communicate with one another so that we can meet the higher demand for performance.

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Michael Matias

(Photo: Courtesy)

Michael Matias, Forbes 30 Under 30, is the author of Age is Only an Int: Lessons I Learned as a Young Entrepreneur. He studies Artificial Intelligence at Stanford University, is a Venture Partner at J-Ventures and was an engineer at Hippo Insurance. Matias previously served as an officer in the 8200 unit. 20MinuteLeaders is a tech entrepreneurship interview series featuring one-on-one interviews with fascinating founders, innovators and thought leaders sharing their journeys and experiences.

Contributing editors: Michael Matias, Megan Ryan

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We work on chips that are serving the AI and supercomputer markets. - CTech

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

Northwell Health is working with Aegis Ventures, a New York-based startup studio to launch Ascertain, a joint company creation platform.

WHY IT MATTERSWith Ascertain, Northwell and Aegis plan to develop and commercialize artificial intelligence companies to tackle various healthcare challenges related to quality, cost, access and health equity.

The joint venture will combine clinical and business leadership, drawing on the expertise of technical and product teams, and building on Northwells diverse datasets and capital resources.

Its main focus will be building and validating effective solutions that can be rapidly brought to market, officials say, noting that Ascertain has already begun creation of its first portfolio companies in partnership with Northwell clinicians and administrators.

One early priority is health, working in collaboration with Northwell's new Center for Maternal Health, the goal is to develop new data-driven approaches to detecting and managing serious complications for expectant mothers and babies.

Ascertains data scientists are working with Northwells Maternal and Fetal Medicine team to examine tens of thousands of cases of preeclampsia, a hypertensive pregnancy complication that disproportionately affects Black mothers in the United States.

Using data science innovations, Ascertain is building AI models to help clinicians predict the risk of developing preeclampsia and enable them to intervene earlier than previously possible.

"An important enabler for a solution to this challenge is predictive analytics, applying available knowledge of a patient's history to enable action far earlier with simple, yet life-saving, clinical interventions," said Dr. Burt Rochelson, chief of maternal medicine at Northwell Health, in a statement. "We believe the AI solution Ascertain is currently developing will enable this."

Ascertain aims to launch more companies this year focused on maternal health, chronic disease detection and management and next-generation AI tools, officials say.

THE LARGER TRENDThe Northwell Aegis collaboration was first announced this past October, with the startup builder anteing $100 million in seed-stage funding to "leverage data from Northwells patient population, one of the most diverse in the world, along with Northwells intellectual capital in AI technology."

The health system's achievements in that space are considerable. One recent initiative, for instance, used machine learning to reduce hospital readmissions by almost 24%. We spoke with its medical director for some perspective on how they did it.

ON THE RECORD"Ascertain brings a unique structure, an innovative approach, and a compelling vision to create breakthrough healthcare AI companies that are set up for success," said Michael Dowling, President and CEO of Northwell Health, in a statement. "We are all driven by the idea that everyone deserves access to high-quality, affordable healthcare. Our aim is clear: to find new, cost-efficient ways to create and accelerate companies that deliver real, equitable solutions."

"Aegis and Northwells multidisciplinary teams are pursuing innovation through a systematic approach that will bring validated solutions to patients more quickly and at a lower cost," added Bill Schoenfeld, Aegis Ventures Chairman. "Our skillsets are extraordinarily complementary and enable us to address healthcares most entrenched quality, equity, and cost problems far more powerfully than either organization could alone."

Twitter:@MikeMiliardHITNEmail the writer:mike.miliard@himssmedia.comHealthcare IT News is a HIMSS publication.

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Northwell Health, Aegis Ventures to launch healthcare AI companies, starting with maternal health - Healthcare IT News

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)

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Developing countries are being left behind in the AI race - and that's a problem for all of us - Economic Times

Luxury auto maker Audi is driving full-throttle toward Industry 4.0, using AI inference and computer vision on the factory floor with autonomous robot welders that can react in real time and fix issues that may arise when welding the frame of a car. Thats just one example of how the company is moving toward realizing its ultimate vision of creating smart factories with a scalable and flexible platform that will enable data analytics, communications and processing at the edge, powered by 5G.

In the past, welding required a lot of manual intervention and inspection to ensure sufficient quality, says Nick McKeown, senior vice president and general manager of the network and edge group at Intel, which is working with Audi. Now, with cameras reviewing the quality of the weld the need for human intervention has greatly decreased.

Sandra Rivera

"Edge computing is taking the technology resources we've been developing over many years for the computing industry and using them to analyze and process data at the edge", McKeown says. The concept of edge computing is storing data closer to where it is generated and usedlike the factory floor--instead of in the cloud, which means it can be processed in real or near real time.

"If you want, or need to process data in real time, you actually have to bring the compute to the data, to the point of data creation and data consumption", explains Sandra Rivera, executive vice president and general manager of the datacenter and AI group at Intel. Not having to move large amounts of data enhances security, and increases reliability while reducing latency. And because data is kept more private there is an additional layer of data sovereignty available when needed, adds McKeown.

As telecommunication operators continue rolling out 5G infrastructure, "there are opportunities that start to emerge because the data rate, the latency, the control that you have over the 5G network means that we can start to use it for applications that we would not have previously thought suitable for a cellular technology," McKeown says.

In the Audi factory example, controlling a robot arm in real time requires either a cable, a wire, an ethernet cable that connects to it to guarantee connectivity, the data rate that is needed, and the low latency controlor it has to be replaced with a wireless link, he says.

"Now imagine that robot is moving around. You really don't want a wire trailing around on the floor for other robots to trip over. You'd really like it to be a wireless link", McKeown says. "And the problem is, wi-fi hasn't really gotten there just yet in terms of the quality that you would want. What 5G, in particular private 5G, offers is a much more reliable, much lower latency, much more controlled-by-software experience."

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With 5G, AI at the edge promises a compute-everywhere future - MIT Technology Review

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IT and Engineering

Argo AI is hiring a Cyber Security Architect to lead the companys cyber security program, including IT infrastructure, operations, software and hardware engineering, fleet operations, administrative functions and business goals.

I AM Robotics is looking for a Principal Engineer, Controls & Planning to define requirements and interfaces for satisfy product and architecture requirements, perform algorithm trades, create system designs, oversee design implementation, and work on roadmap development.

Business and Finance

Citizens has an opening for a Business Banking Relationship Manager to provide solutions to customers, grow client relationships and help customers with annual revenues of $500,000 to $5 million.

Chatham University is hiring an Accountant Accounts Payable to oversee data entry for invoices, check requests and printed checks sent to vendors, and serve as a resource to budget administrators.

Administrative

The Allegheny County Airport Authority seeks an Executive Project Manager to organize the CEOs daily portfolio, conduct research, interface with internal and external stakeholders, manage the life cycle of cross-functional projects, coordinate communications and more.

Human Services

Community Life is hiring a Home Health Aide to assist program participants in their homes with daily living activities.

Nonprofit

Casey Droege Cultural Productions seeks a Project Manager to lead projects, mentor artists and work closely with clients, including nonprofits, corporations, small businesses, architects, developers and interior designers.

Retail

Giant Eagle has an opening for an Assistant Store Leader to oversee safe work practices, food safety, employee training and performance, equipment, and departmental financial information.

Advanced Manufacturing

MSA is looking for for a Production Technician (Portable Instruments) to assemble, test, troubleshoot, rework and repair electronic instruments according to customer specifications.

LivaNova is hiring a Controller Manufacturing and Service Technician to complete manufacturing assembly and service processes for TandemLife controllers, docks and accessories.

Job Fairs

The McKees Rocks Community Development Corporation is hosting an Information Session on vocation education, trades and apprenticeship programs on Monday, April 25 at 4 p.m., where job seekers can meet with workforce organizations. For more information, email Jordan Allen or call 412-219-2189.

PA CareerLink is hosting a Job Fair on Wednesday, April 27, from 2 to 5 p.m., at the Carnegie Library of Homestead. To register, email Davis Roddy or call 412-436-2231.

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Project Manager, Commercialization at University of Pittsburgh: The Project Manager will work as a guide for investigators as they move their health science research to the marketplace, educating them on the commercial translation process as a means to implement and disseminate their work.Posted March 16, 2022

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Resource Conservationist at Allegheny County Conservation District: Allegheny County Conservation District is hiring a full-time Resource Conservationist to review permits and perform inspections related to Chapter 102 and 105 to protect and improve Allegheny County's natural resources.Posted March 15, 2022

Senior Director, Development at Carnegie Museums of Pittsburgh: With development team, develops programs and strategies, manages resources focused on fundraising for Carnegie Museum of Art. Bachelor's degree or equivalent experience/education required. View full posting and apply through website.Posted March 14, 2022

Digital Marketing Specialist Integrated Innovation Institute (iii) at Carnegie Mellon University: The Integrated Innovation Institute (iii) within Carnegie Mellon University is searching for a motivated, organized and analytics-obsessed Digital Marketing Specialist to manage and grow our digital advertising channels in Pittsburgh, PA.Posted March 10, 2022

Project Coordinator at Grounded Strategies: Grounded Strategies is hiring a Project Coordinator to support specific program initiatives and projects related to vacant lot reclamation and environmental stewardship.Posted March 09, 2022

Accountant at Pittsburgh CLO: Pittsburgh CLO seeks a detail-oriented, reliable individual to serve as an Accountant and function as an integral member of the Finance Department. Responsibilities include a wide range of accounting duties, such as processing payroll, journal entries, accounts payable and accounts receivable.Posted March 09, 2022

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Controller at Community Living and Support Services (CLASS): Community Living and Support Services (CLASS) is a leader in providing high quality, person-centered care and support to people with disabilities and seniors throughout Allegheny County and is looking to hire a Controller within it's Finance Department.Posted March 07, 2022

Manager of Institutional Giving at Riverlife: Riverlife seeks a Manager of Institutional Giving to oversee a diverse portfolio of corporate, foundation and public funders, and manage all grant proposals and reports for operations and special projects.Posted March 06, 2022

Foundation Coordinator at WQED: WQED is looking for a Foundation Coordinator to support our foundation fundraising needs.Posted March 03, 2022

Sr. Electrical Engineer at Near Earth Autonomy: Near Earth is looking for a Sr. Electrical Engineer with a focus on developing electronics and embedded systems to join our hardware development group. The ideal candidate has experience in and a passion for productizing advanced technologies for aerospace applications.Posted March 01, 2022

Executive Director at Communitopia: Communitopia is hiring an Executive Director responsible for the overall management of the organization, including setting strategy, creating and maintaining a sustainable revenue structure, leading the staff, and meeting programmatic goals that further climate change education in the region.Posted March 01, 2022

Communications Manager at University of Pittsburgh: Pitts Center for Governance and Markets is seeking a motivated communications manager to design, lead and execute their ambitious and mission-critical marketing and communications efforts. Tasked with developing and executing a strategy for broadly promoting the work of the Center.Posted March 01, 2022

Special Education Teacher Zelienople ($5,000 new hire incentive bonus!) at Glade Run Lutheran Services: St. Stephen's Lutheran Academy is an educational option for students in grades K through 12 who require specialized educational programs due to behavioral health, academic and developmental concerns. Our innovative programs are designed to help students succeed academically and behaviorally.Posted February 25, 2022

School Based Therapist Seneca Valley and North Allegheny School Districts (up to a $5,000 new hire incentive bonus!) at Glade Run Lutheran Services: Job responsibilities include: completing client intakes; developing measurable treatment goals in collaboration with clients and families; completing treatment reviews in accordance with outpatient regulations; and providing individual, group and family therapy as is clinically indicated.Posted February 25, 2022

Assistant Director, Online Programs Tepper School of Business at Carnegie Mellon University: Carnegie Mellon University seeks an Assistant Director of Online Masters Programs in the Tepper School of Business to facilitate in-program planning and events execution for two premier online masters programs. Successful candidates will bring a strong project or events management background.Posted February 25, 2022

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Communications Manager at University of Pittsburgh: Communications Manager, University of Pittsburgh, Office of Senior Vice Chancellor for Research: The Communications Manager enjoys collaborating while managing website, newsletter and social media; producing newsletter and annual report; and joining in strategic communications planning and execution.Posted February 24, 2022

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Who's hiring in Pittsburgh? Argo AI, Citizens, Chatham University and more 4/18/22 - NEXTpittsburgh