Key Takeaways  It's not a theory, it's (long-term) facts: In 5.4 billion years the sun will become a red giant and engulf the Earth. In the shorter term, volcanoes could destroy life as we know it. It's a common complaint every 100,000 years or so. It could go either way, but there's also a risk that super-intelligent AI could turn on humans and spell the end of the speciesHumans have a macabre fascination with end of the world theories — will humankind go out with a bang or disappear with a whimper? Are we the architects of our own demise or cosmic pawns in a game we can't control? Let's sort through fiction, fluff and outright fantastical futurism to discuss the top four evidence-based apocalyptic theories.Scorched EarthWant an apocalypse theory that's effectively a sure thing? Opt for the sun-as-red-giant scenario. As noted by Universe Today, in approximately 5.4 billion years the sun will start fusing helium and rapidly expand across the orbits of Mercury, Venus and potentially Earth. But humanity will be gone long before this happens, since our star is steadily increasing in both luminosity and heat: In just over 1 billion years, a 10 percent increase to heat energy will trigger a runaway greenhouse effect. Two and a half billion years more and the sun boils our oceans, melts the ice caps and turns Earth into a second Venus.The solution for humanity? Get off Earth and find a new planet. Exploration of Mars and the outer solar system is a good start.AI ApocalypseRobots are taking human jobs. While the Atlantic notes that new machine workers can help maximize productivity and the worries of full-scale robot replacement are unfounded, pervasive concern persists. Author Isaac Asimov called it the "Frankenstein Complex" — fear that intelligent machines will rise up and destroy their human overlords. This idea is a perennial favorite in pop culture: James Cameron's Terminator saga remains a cinematic highlight of the above.But it's not a sure thing. Done right, super-intelligent computers could be our best ally against other signs of the apocalypse — as noted by ScienceAlert, "an intelligence of such power could easily combat most other risks in this report, making extremely intelligent AI into a tool of great potential."Zombie Ground Zero?Zombie scenarios are popular end of the world theories in both novels and television, but chances are we won't see any walkers, shamblers or brain-eaters in the near future. For a disease to transition from "annoying" to "end of the world," it needs four key characteristics: Incurable — Like current strains of Ebola, there's no way to "cure" the illness, simply survive (if you're lucky). Almost always fatal — Diseases like rabies almost invariably kill unvaccinated human beings. If an unknown pathogen with this kind of mortality rate emerges, we're in trouble. Easily transferable — If the illness can spread like common colds, there's virtually no way to stop new infections from happening. Long incubation period — Diseases such as HIV that allow massive windows of time for new victims to become infected fall into this category.If a disease with these four factors were to emerge in a high-density urban area, modern medical science would be hard-pressed to do anything but lessen the suffering of the infected. That said, investment in rapid-response disease detection and quarantine procedures could help reduce the impact here from "world-ending" to "disastrous."Super-Violent VolcanoesAccording to Science Magazine, there's a repeating apocalyptic problem every 100,000 years — supervolcanoes. These volcanoes start with collapsing underground calderas that produce eruptions of more than 450 cubic kilometers of magma. For reference, the Mount St. Helens explosion in 1980 released just 0.25 cubic kilometers of magma. Worst-case scenario? In addition to large-scale destruction near ground zero, the ash lifted into the atmosphere blocks the sun and drops global temperatures by five to ten degrees Celsius for a decade, while ash on the ground kills all plant life.Potential prevention here stems from solid knowledge, which means more time and money for scientists trying to understand and model volcanic behavior. While it may be impossible to eliminate the chance of a supervolcano explosion, good data could help minimize the impact.Prepping for the end of the world? Signs of the apocalypse include angry robots, new infections and supervolcanoes. Oh, and don't be fooled by the bright, friendly sun — it's coming for us all, eventually.We're here for a good time — might as well make it a long(er) time with effective conservation. Learn more.

Apocalyptic Analysis — Top 4 End of the World Theories

Key Takeaways  Complex Systems Theory examines the relationships between factors as well as the individual parts that make up the system. By studying how the parts of a system relate to each other, we can predict the likelihood of certain outcomes. Big data and machine learning can analyze the data from complex systems and help us recognize patterns to make better decisions.It's official: life is complicated. A new discipline called complex systems theory uses scientific approaches to understand and predict social issues. This emerging field gives us concepts and tools to help us navigate the modern world, which is growing more connected and therefore more challenging by the nanosecond. Science can provide insights and predictions that governments and other entities can use to guide decisions. What Is a Complex System?When a whole is greater than the sum of its parts, it is considered a complex system. Traditional thinking would analyze each individual component, but this method also includes the relationships between all components. This gives us insight into emergent behaviors that wouldn't normally be expected from the parts. By studying how the parts of a system relate to each other, we can predict the likelihood of certain outcomes. Complex systems theory can reveal hidden patterns that help predict the weather, social issues, the spread of diseases, financial recessions and more.Whether we are looking at something as small as an atom or as big as the stock market, it's not easy to predict future behavior because there are so many variables. If we can accept and embrace the complexity, we can find patterns in what first appears to be chaos. With globalization and the digital revolution, we are more connected than ever. Suddenly, a bunch of complex systems (governments and cultures) are forging new connections. The bad news is that bad things, such as diseases and violence, spread quickly. The good news is that advances in science and technology mean we have new solutions.At the first sign of change (a weather event, shifting political attitudes, recession, etc.), we can run computer simulations that help us see into the future. Not only can we be better prepared for a crisis, but it's even possible to avoid a crisis altogether.Today's digital era is filled with extremely large data sets that are filled with valuable information about the "parts" of each complex system. While it would be impossible for humans to sift through the deluge of information, computers can reveal trends in data that help us understand and predict human behavior. Big data and machine learning can help us recognize patterns to make better decisions. The latest artificial intelligence lets software crunch the numbers, and complex systems theory provides a framework for moving forward.Experts develop mathematical approaches that can be used to help understand problems that are typically far beyond the scope of science. According to the New England Complex Systems Institute, "We combine a quantitative foundation in physics, computer science and mathematics with computer simulations and high dimensional data analysis to describe real world patterns of behavior."Bringing the Theory to the Real WorldWhile complex systems theory can't solve our problems, it can help policymakers minimize the damage. This new field of study provides analytical tools and computer models for simulating how these systems are likely to react to a given change. The concepts that are being defined can help leaders think about challenges on a deeper level. The theory could help improve the healthcare system and prevent pandemics. For example, a recent study revealed that the best way to prevent an outbreak from spreading (such as Ebola or Zika) is to focus on community rules, such as restricting travel, rather than individuals.Analyzing data can help scientists understand what causes a food crisis. Scientists can provide suggestions for resolving the immediate crisis, as well as make long-term predictions that can influence policy changes to reduce future crises. In finance, this approach can help predict an economic crisis so that governments can be prepared for the fallout. Algorithms can describe the ways that relatively small changes in one part of the world will affect lives in other countries. The same framework that helps scientists explain biochemical reactions and understand how new species form can also be used to guide worldwide policies.Discover how we are constantly questioning, problem-solving and creating new ways to tackle seemingly impossible challenges in digital transformation and evolution.

Complex Systems Theory: How Science Solves Social Issues

Key Takeaways Wildlife conservation benefits from drones as eyes in the sky for population and habitat monitoring. Boosting analytical power with AI can help speed up results. AI coupled with drone flights can help spot poachers and protect vulnerable populations.Wildlife conservation is getting help not only from wildlife drones but also from the limitless computing power of artificial intelligence (AI). With diversity under threat, researchers are flying unmanned aerial vehicles (UAVs) as wildlife drones to gather information on endangered populations and habitats. To cope with the massive amounts of data generated, AI is helping to speed up results. From remote areas of the planet to endangered populations, drones and AI are working together to extend the reach for conservation and research.How Are Drones Used in Wildlife Monitoring?Traditionally, conservation efforts took place on foot or by aerial surveillance, with population counts and habitat assessments performed manually by an observer. The main problems with this approach are access, impact and scale. Access: Wildlife conservation often means working in remote regions of the planet, which aren't always easily accessible. Even traditional aerial survey is difficult if dense scrub blocks visual identification. The Wildlife Society describes how researchers used drones to penetrate dense vegetation in Brazil's Pantanal wetlands, the world's largest tropical floodplain. Images collected over two months counted 66 marsh deer in this delicate ecosystem. Impact: Human presence impacts wildlife, disrupting normal behavior with damaging results on feeding, breeding and hunting. The World Wildlife Fund is using remote drone surveillance to monitor cetaceans in Canada's arctic waters. Without disrupting the bowhead and narwhal whales, researchers witnessed unique behaviors for feeding and hunting that had never previously been seen. Scale: Covering large areas on foot or even with a vehicle is limited, so monitoring an entire population takes a lot of time and may not be possible. The Nature Conservancy in Colorado not only uses drones to track bison movement over inaccessible territory but also picks out cottonwood tree health from the infrared imaging sensors in UAVs.Replacing direct human observation with drones can help increase wildlife conservation efforts. Although there are issues with regulation for flights beyond the visual range of the operator, drones can help with access to remote areas, and their sensors can cut through dense vegetation. There are also ethical concerns with flying at the correct height to avoid disturbing wildlife. However, once this is addressed and permits are obtained, there will be less impact on the species under observation, so they are free to continue uninterrupted. This is extremely valuable for scientists interested in the behavior of species in the wild.Drones and AI Meet ConservationWhen the power of AI can be combined with drones, wildlife conservation can truly take flight. Drone flights can cover a greater area than traditional methods and, when coupled with AI, can return more results faster. Plus, wildlife drones with AI are not only highly scalable but also teachable. Sensors aboard drones can capture images and features that the human eye may miss, and then, AI can make light work of processing all the incoming data.AI is already supporting conservation projects around the world. Faster processing speeds help sift through images from camera traps to capture meaningful data from thousands of empty images. The National Wildlife Federation describes how researchers are training AI tools for pattern recognition to identify humpback whales by their songs, whale sharks by their markings and trout by their spots.Researchers in Botswana use drones for rhino conservation, coupling them with machine learning AI that makes the process much more effective. According to The Wildlife Society, drones are helping monitor rhino populations as they rebound from near extinction. In the 1980s, rhino herds were greatly reduced due to poaching activity, and their recent reintroduction is being closely monitored for the herds' protection. Frequent foot patrols monitor the rhino habitat, but there's often too much to cover and not enough personnel to be completely effective. Enter AI as a solution.Using wildlife drones, researchers use AI to train detection programs on the images obtained from aerial reconnaissance. The scientists use machine learning to speed up alerts and support foot patrols. The research team's AI can now discriminate between rhino herd movement and human activity. It can even tell the difference between the wardens and poachers simply by analyzing their behavior on the ground. In addition to spotting poachers and protecting rhinos, the AI is helping keep track of other species. The drones can also spot giraffe, elephant and zebra herds.In short, AI is making a big difference to wildlife conservation as eyes in the sky, and it's just getting started.Northrop Grumman is committed to promoting conservation and inspiring the next generation of environmental stewards. Learn more about our environmental stewardship. 

Wildlife Conservation: How Are Drones Used in Wildlife Monitoring?

Unmanned aircraft systems, or drones, have become a ubiquitous part of our lives. Smart, microprocessor- and software-driven flying machines now regularly perform military missions tagged as "dull, dirty or dangerous," while local government agencies and private entrepreneurs enlist drones for missions such as aerial surveillance of fires, wedding photography and small-package deliveries. Against this backdrop, however, human test pilots — those daring young men and women in their flying machines of movie legend — remain a central and indispensable part of aircraft design, and the teams and processes that develop, test and field new aircraft systems, both manned and unmanned. "Developmental test pilots serve as the 'bridge' between engineers (who design the plane) and operational pilots (who fly the plane)," explains Bob Hood, a retired U.S. Air Force test pilot and one of 15 active test pilots employed today by Northrop Grumman. "The test pilot must be able to speak 'engineering' to help aircraft developers produce a plane with good flying qualities that can meet its mission requirements. He must also help developers produce a flying experience for pilots that is engaging, intuitive and easy to understand." Start With STEM Many test pilots begin their careers as military pilots with a degree in engineering or another technical degree such as physics or mathematics. "To enter test pilot school, you also have to have substantial flying experience — a minimum of 750 flight hours — in military aircraft," explains Troy Johnson, a retired Air Force test pilot and Northrop Grumman Aerospace Systems' chief test pilot. The U.S. Navy and the Air Force both operate their own test pilot schools; commercial test pilot schools are also available in the U.S. and abroad. Join the Dream Team Ironically, explains Don Weiss, a retired U.S. Marine Corps test pilot and Northrop Grumman's chief B-2 stealth bomber test pilot, "a majority of the contributions of a test pilot are not made while seated in an aircraft." The role of a test pilot actually begins, he explains, "in the 'dreaming' stage of aircraft design as part of a team considering all aspects of aircraft operation and performance." The test pilot contributes to everything from aircraft design criteria; vendor selection for specific subsystems; and the layout of cockpit controls and displays; to helping develop simulators, flight test plans and training programs — and the earlier in the program, the better. "The single most important job of a test pilot," advises Wayne Staley, a retired Northrop Grumman test pilot who began his test pilot career in the Air Force, "is to collect the data that the aircraft's designers need to evaluate the operation and performance of the aircraft." Bring the Right Stuff The most important characteristic of a test pilot? "Integrity," says Weiss. "Both engineers and pilots have to trust, intimately, the real-time findings of the test pilot. If there is an issue with the plane, the test pilot has to be very transparent so that the situation is well understood by designers and operators alike." Good test pilots need to be disciplined, curious, and cool under fire. According to Hood, however, "fear is not part of a test pilot's vocabulary." If something unusual happens during a flight, he says, "You have to operate in the present. It doesn't matter how you got there, only how you're going to fix it." And this advice has paid off. In October, both Hood and Staley were recognized by the Society of Experimental Test Pilots (SETP) for their career-long ability to meet challenging flight test requirements. Both men were honored as members of SETP's 2017 Class of Fellows at the organization's annual meeting, in Anaheim, Calif. Evolve With Technology As aircraft design technology has evolved, so too has the role of the test pilot. "In the 1950s, pilots took planes out to see what they would do. Today we take aircraft out to verify the predictions of our models," says Staley. In an age of sophisticated wind tunnel testing, modeling and simulation tools, he adds, "we don't get into the cockpit until our predicted performance is good." But a new aircraft system doesn't need a cockpit to benefit from the skills of a test pilot. "Increasingly, high speed computers do most of the 'thinking' and management of flight control surfaces for autonomous systems," says Weiss. "But you still need someone who can evaluate the air vehicle's flying qualities, because humans are involved in operating it from the ground." FAA rules require operators of certain classes of unmanned systems to be flight qualified as pilots, and instrument rated. Test pilots work closely with designers of ground control stations, therefore, to ensure that aircraft controls, displays, and methods of presenting information are consistent with how our pilots are trained. Test pilots also help autonomous system designers ensure that an aircraft's avionics and flight control systems can respond appropriately to human air traffic control commands to the air vehicle operator. "The goal is to design autonomous systems so that they are fail-safe and foolproof, but every once in a while, these systems fail and you end up losing an aircraft," observes Johnson. "By including test pilots' expertise in the development of an aircraft, there's a much better chance that a pilot can bring it home safely." Manage the Risks In the end, being a test pilot centers on risk management, whether you're testing a manned or an autonomous system. "The test pilot and the test team spend a lot of time discussing and mitigating risks before a plane ever takes off," explains Staley. "These discussions involve the people who understand best how the aircraft works. Our shared goal is to identify possible problems and minimize our exposure to those problems. That way we'll always have a safe escape route if unexpected situations arise." Enjoy the Ride For all the discipline, teamwork, and mission planning, however, test pilots still go to work to do what they love most — fly high performance aircraft. "One of my most memorable experience was testing the weapons delivery capabilities of a military jet over the ocean," recalls Weiss. "On test day, there was a marine layer of clouds. We needed to have clear conditions to complete the test properly. We spotted a hole in the clouds that was just large enough to drop in, release the weapons, document it and get out. That was fun, and it felt good to accomplish the test under challenging conditions. Most importantly, we got the data. I call that a pretty good day at the office."

Aircraft Design in the Age of Autonomous Systems: Test Pilots Still Play Essential Role

Key Takeaways The difference between chronological and brain age can be measured by examining the hippocampus. The challenge? Human analysis isn't accurate enough. Using multiple instanced learning (MIL), artificial intelligence (AI) tools were able to accurately determine the brain age of deceased patients. Brain age analysis demonstrates key capabilities of AI, such as identifying complex and subtle patterns in large bodies of data.Artificial intelligence (AI) in medicine may evoke images of robots examining patients or even performing surgery — and that would be somewhat accurate. For example, AI technology has already been put to work performing some surgical tasks, according to the American College of Physicians. But AI is also impacting the medical world in other crucial ways, including research tools now being developed and used in brain research.How is AI helping humans understand the brain and, in particular, how the brain ages? According to the Icahn School of Medicine at Mount Sinai, an AI tool called HistoAge is able to determine "brain age" by identifying subtle cellular patterns in a region of the brain called the hippocampus. The tool gets its name from medical histology, which per the National Institutes of Health is the study of biological structures on a microscopic scale.Brain Age AccelerationThe hippocampus region of the brain is known to be associated with deteriorating brain function with age, resulting in conditions such as Alzheimer's disease. As reported by Neuroscience News, by determining the difference between brain age and the chronological age of a patient, brain age acceleration can be determined. This comparison, in turn, yields insights into both normal brain structure and function and also how the brain ages, including age-related decline as well as changes that may indicate the onset of brain disorders.Brain researchers believed that close comparative examination of structures in the hippocampus would yield key insights into how the brain ages, but there was a hitch. The Mount Sinai research team suspected that close examination of sectioned tissue sliced from the hippocampus could be used to determine brain age acceleration, but as noted by Neuroscience News, this was "a task that is impossible for a human observer to perform with any degree of accuracy."The problem is the structural patterns that reveal brain age are both subtle and numerous — meaning more correlations working together and interacting than anyone could hold in mind to make the comparison. This is not unlike the challenge of large-scale language translation, as reported by MIT News. Linguistics researchers sought for years to automate translation by sussing out the underlying grammatical logic that allows different human languages to express ideas. But grammar is just too complicated to fully work out by hand.Then, AI researchers discovered that if they just gave a computer an enormous amount of text to chew over (e.g., millions or billions of pages), they didn't need to fully work out the grammar in advance. The computer could patiently work through the mass of material, find correlations — such as how often one particular word follows some other word — and use all those subtle relationships to determine the best translation for a series of words. And it worked!Multiple Instance LearningThe brain researchers at Mount Sinai use the same approach but with the preserved brains of nearly 700 elderly brain donors instead of a corpus of text. However, this raised a further challenge. Fully detailing all these preserved brains would lead the researchers right back to the problem of too many subtle patterns to follow. The research team could only work with limited information, such as the chronological age and medical diagnoses of the donors at death.The solution to this problem was an AI technique known as multiple instance learning (MIL). This technique is suited to "weakly supervised" machine learning. As described by Jonathan Glaser, "Whereas traditional machine learning techniques rely on feature extraction by domain experts, deep learning algorithms learn high-level features from data on their own."In MIL, as reported at arXiv.org, data is initially sorted into "bags," or assorted collections of individual data instances. A bag may be positive if its data includes at least one instance of a specified characteristic or negative if it has no instances with that characteristic.Repeated passes through the data allowed HistoAge to build up its own detailed model of hippocampus characteristics based on its training data. The process was complex, but the goal was simple: The AI was to determine an estimated brain age at death for each brain studied. And, as with machine translation, it worked. Per Neuroscience News, HistoAge was able to estimate the average age at death within 5.45 years of the donors' actual ages at death.The Future of AI Brain Study?Mount Sinai researchers found that when stacked up against previous existing brain age determination techniques, such as DNA methylation, the age acceleration estimations derived from HistoAge reliably determined brain age and discovered insights into the driving factors of clinical and pathological outcomes related to cerebrovascular disease, cognitive impairment and abnormal levels of Alzheimer's-type degenerative protein aggregation. This offers a preliminary answer to the question, "How is AI helping humans understand the brain?"Note that this line of research into AI in neuroscience is not aimed at the goal of AI examining individual (living) patients but at building up a research model that human brain specialists can then use as a baseline tool for evaluating an individual patient's brain health. This exemplifies the wide range of applications in which AI technology is enabling transformative progress.From a big-picture perspective, HistoAge is a demonstration of AI capabilities: identifying complex and subtle patterns in a large body of data — patterns that humans without AI tools would never be able to detect, let alone work with. And this innovative use of AI could pave the way forward for brain study in the years to come.AI is a key component of Northrop Grumman digital transformation efforts. See how we're applying AI to discover new insights.

How Is AI Helping Humans Understand the Brain?

Key Takeaways Current diagnostic tech relies heavily on the efforts of medical professionals. Artificial intelligence could help speed up the process and improve diagnostic outputs. AI won't replace healthcare staff — instead, the future of diagnostics will be a tandem effort.Despite years of training and experience — and a vast wealth of medical knowledge at their fingertips — doctors still have difficulty pinpointing certain conditions and diseases. Artificial intelligence (AI) holds the promise of reshaping diagnostic technology and helping doctors finally put a finger on diseases that have a way of eluding detection.What Is Diagnostic Technology?Diagnostics is the process of identifying diseases or other health conditions. Diagnostic technologies help streamline these efforts.According to the World Health Organization, these technologies include "medical devices, techniques and procedures used for in vitro and in vivo determination of physiological status or presence and characteristics of a disease."How Is AI Used in Diagnostics?Some of the most evasive diseases could be easier to detect with AI-supported diagnostic technology. This technology can scan thousands of medical records and analyze data that might escape medical professionals' attention, improving the time-consuming process of classifying medical records.Electronic health records (EHRs) potentially possess bits of insight that can lead to more precise diagnoses and improved healthcare. According to a 2022 Report to Congress from the Office of the National Coordinator for Health Information Technology, 96% of all non-federal acute care hospitals use EHRs.While the digitization of patient records makes it easier for medical professionals to access key data on demand, this information still needs to be read by a doctor. The tedious nature of the process — not to mention the role of fatigue and distraction from other pressing issues — may cause a health professional to overlook a symptom or misdiagnose a condition.AI, meanwhile, doesn't get tired. Give intelligent tools a set of instructions and turn them loose on large data sets, and they're perfectly capable of working day and night until they find an answer or discover a connection, no matter how minute.Making a DifferenceAI-enabled technology is already helping improve diagnostic accuracy.As noted by the World Economic Forum, a Japanese startup has developed an endoscopic AI tool that can analyze images in 0.02 seconds and accurately determine if cancer is present in 94% of cases. While accuracy is similar for human physicians, it takes them 4 seconds on average to read an image. By using AI, doctors can speed up the screening process while still keeping human oversight intact.AI is also improving the early detection of illnesses, such as pancreatic cancer. Researchers at Mayo Clinic have introduced an AI technology that can automatically detect cancer in CT scans — in some cases, the technology was able to detect cancer up to 475 days before its eventual, clinical diagnosis. With most pancreatic cancer patients still receiving their diagnosis when they've reached advanced stages of the disease, this AI-driven approach could help improve treatment options and reduce mortality rates.Work out of Germany, meanwhile, focuses on the use of AI to detect rare types of gastrointestinal diseases, according to News Medical. The new AI model only needs training data from less-common diseases to reliably detect their less-frequency counterparts. Using what's known as anomaly detection, the model can quickly recognize and flag deviations from "normal" tissue, which may indicate a gastrointestinal issue.The Future of Artificial Intelligence and MedicineAI diagnostics can help support — not supplant — medical professionals. While intelligent tools excel at data entry, capture and analysis, they come up short in context. Put simply, humans are more than the sum of our symptoms; what works for one patient may not work for another, even if their disease presentation is almost identical.The result is a shared future for AI and medical staff. High-tech tools can help reduce the amount of time doctors spend sifting through records and offer insightful observations on potential diagnoses. Armed with this information, healthcare professionals are better prepared to meet patients where they are and create personalized treatment plans.Discover how Northrop Grumman is leading the way in AI and machine learning. 

The Future of Diagnostic Technology and Artificial Intelligence

Key Takeaways Technology is transforming the future of the aviation industry, with AI having an outsized impact. The aviation industry is moving toward more environmentally friendly propulsion technology. Infrastructure and regulations are the biggest challenges for the future of aviation technology.In 1903, the Wright brothers' flight of a heavier-than-air powered craft answered a question: Yes, it's possible for humans to fly. It also fostered an obsession with flying. For more than a century, humans have been pushing the boundaries and looking for new ways to touch the sky. With commercial and military air travel now firmly commonplace, what's in store for the future of the aviation industry?In December 1903, Wilbur Wright managed a flight of 852 feet in just under a minute — that might not be far or fast by today's standards, but it was a massive milestone for human ingenuity at the time, explains the Wright Brothers Aeroplane Company. By 1914, the first commercial air service began operating between St. Petersburg and Tampa, Florida, according to Space.com. By World War I, propeller planes dotted the sky, and craft capable of breaking the sound barrier and carrying thousands of pounds in cargo and hundreds of people followed as the science of flight was developed and refined.Now, we look to the future of what's to come in the industry — and it promises to be exciting.Technology Is Transforming the Future"The future of aviation," according to Roddey Smith, a Northrop Grumman fellow in the aeronautics sector, "could include a 30-minute ride in a spacecraft from one side of the earth to the other."Can you imagine flying from the East Coast to the West Coast in a matter of minutes? Clearly, the future of the aviation industry won't look anything like it does today. Already, drones are everywhere, and there are small aircraft capable of vertical takeoff and landing.Smith believes that artificial intelligence (AI) will have an outsized impact on aviation technology. "AI can take in vast amounts of data, analyze it and come up with a set of proposed solutions for a question," Smith says. "AI works at light speed; it takes in more data than you could ever ingest in your whole lifetime, and then, it gives you a bunch of viable options."The technology is now at a point where aircraft can take off, fly and land all by themselves in auto mode. AI can take the air traffic pattern and the conditions of the runway into account without any human intervention. That said, there will probably always be a human in the middle to mediate and decide how to employ AI technology, but machines will eventually become smarter because they'll be able to think faster, collect more data and deal with a lot more information than any human ever could."Over time, though," says Smith, "we will slowly build trust in the AI entity itself."Eco-friendly Aviation TechnologyThere's a motivation by the government and environmental organizations to make aircraft more environmentally friendly to conserve resources. With this in mind, aviation technology is moving toward changes in propulsion technology: The future of aviation could include a hybrid combination of electric and gas or hydrogen and fuel cells."Batteries are one of the most important make-or-break technologies for the future of aviation industry," says Smith. "Especially for aviation range and performance." His team seeks out industry experts and leaders to integrate the latest batteries into Northrop Grumman's portfolio products. Smith and his team are also keenly interested in propulsion technologies."Sustainability and eco-friendly aviation is a viable alternative to traditional jet fuel," he says, "but the experience will look different." Instead of a jet that's able to fly 10,000 miles with 400 people, electric aviation technology might begin by flying half as far with fewer people on board.Infrastructure IssuesFlying cars aren't a technology problem — those vehicles already exist. The problem is how to regulate them and how to set rules in place in a 3D space. Without regulation, the future of the aviation industry would be like driving your car without stoplights, street signs or traffic rules. Hundreds of thousands of individuals taking off and flying in the same airspace without any coordination would be a disaster.To fly an airplane now, you have to file a flight plan ahead of time, and there's a series of air traffic control operators with radars and radios directing the airplane's every turn. The Federal Aviation Administration is currently under a tremendous amount of pressure to come up with guidelines to regulate how thousands of air vehicles would operate in the sky.Much of the technologies that would control the movement of air vehicles would need to be automated. A stationary drone or system of location beacons, for example, could be like a police officer who stands in the middle of an intersection with a whistle and waves people through. With new aviation technology, airplanes could swirl around and over each other and go into a holding pattern when needed.The Thrill of FlightWhatever the future of aviation looks like, it will be exciting to see — and equally thrilling to watch develop."Pilots still just love the joy of flying," says Smith. "I don't think that will ever change." But what really lights up a pilot is when they're able to execute a mission with the aircraft and be effective at their job, something technology will likely make even easier in the near future.Find out more about Northrop Grumman's innovative ways to soar.

Future of the Aviation Industry: Is the Sky the Limit?

Key takeaways Virtual reality in education is changing the way students experience the classroom, especially in STEM fields. Virtual reality and learning can help improve student comprehension. VR is inspiring the next generation of STEM enthusiasts.When used in education, virtual reality (VR) allows students to gain many types of experiences they wouldn't normally have access to in their classrooms. VR allows students to take virtual field trips to places as unusual as the inside of a computer, a fusion reactor and even the moon or Mars.The type of immersive experiences that VR in education can provide to students, especially in STEM fields, is often more exciting and stimulating than what they'd have access to in a conventional learning environment. Another advantage of virtual reality and learning is that it can be effective with children of all ages. That's because VR provides students an opportunity to learn hands-on and may be attractive to students who might not otherwise be interested in STEM. Additionally, VR simulations broaden the assortment of real-world uses students can investigate.Technology to Promote Better Student ComprehensionVR science education can place students in situations that, in a standard classroom, might be difficult to visualize. For example, VR can give students the opportunity to get hands-on experience with especially large sets of data, which are common in STEM subjects such as astronomy, genetics and climate science. It also gives students a sense of already being in real-world work situations.For example, the Simmons School of Education and Human Development uses a chemistry mobile app to help students learn chemistry by allowing them to manipulate physical blocks to create elements. The virtual classroom looks similar to a video game, with student collaborators as the "players." Students learn about geometric shapes and about how angle, speed and gravity impact projectile motion. Similar technology from Google allows students to experience VR expeditions, such as landing on Mars or exploring the inside of a carbon capture lab.This type of technology also provides many learning benefits, such as increased focus, engagement and learner involvement. For example, VR science education makes exploring a coral reef, flying through the bloodstream to learn about cells or testing an engineering prototype easier for students to understand. Students can also experience simulations in fields such as natural history, computer programming, aeronautical engineering or astrophysics. By actually exploring these virtual science worlds, they can gain a greater retention of STEM fundamentals.Since virtual reality and learning use interactive features to help students connect with the curriculum, it has the potential to improve their learning experience and memory, according to research published in Frontiers in Psychology. This is because the immersive nature of the virtual reality experience increases both cognitive and behavioral engagement, so students are better able to understand and remember what they learned in greater depth, which is critical to positive learning outcomes. The type of interactive features that VR in the classroom provides can also increase the ability of students to grasp and retain STEM subjects, and this, in turn, can help build life skills that will prepare them for future professions in STEM.Next Generation STEMAs outlined by Accelerate Learning, another benefit of VR in the classroom is that it supports a wide range of learning styles and impairments with features such as text-to-speech and closed captioning, enabling more students to succeed.In addition to making STEM fun, virtual reality in education encourages analytical and critical thinking and can enhance students' problem-solving abilities. It can also foster more collaboration. With VR, students in different locations can work together on experiments, design solutions with one another or problem-solve as a team.But why isn't VR in all classrooms yet? Namely, costs. One major drawback of using VR in STEM classrooms is the upfront cost of headgear, software and content. STEM teachers also need to be trained on the best practices of how to incorporate VR and learning. There are currently several workshops, classes and events that supply educators with the technical knowledge and skills they need to utilize VR equipment and software in their classrooms. The investment in VR is a long-term commitment to STEM education.Since it's highly likely VR will continue to transform the way students interact with STEM subjects, it will be increasingly important for educators and educational policymakers to supply teachers with the support they need so all students can embrace the full potential of VR in education.Find out more about how engineers at Northrop Grumman are using virtual reality to help visualize what's possible.

Virtual Reality and Learning: How VR Is Changing STEM Education

 Key Takeaways
 
 
 

    Biomimicry is the study of systems in nature to find solutions in human design.
    Aviation design can benefit from biomimicry — studying birds can help us make aircraft flight and design more efficient.
    AI can help tackle and refine the huge data sets generated by biomimicry research, building algorithms to mimic bird flight or analyzing wing shape, for example.

 
 
 
Who hasn't stared up at a hawk or vulture circling lazily in the sky and wondered how they stay aloft so long? Researchers are now able to tackle this question more systematically using biomimicry, the process of mirroring nature's systems to solve complex human problems.
 
Their goal is to develop artificial intelligence (AI) algorithms that will allow gliders to mimic the flight behavior and endurance of birds and understand wing structural changes that facilitate silent flight, among other projects. Soon, AI in aviation will transform the types of missions that gliders perform and substantially reduce noise in flight.
 
 
 
Flying Like Birds
 
 
 
AI and biomimicry are already helping aviation researchers understand how gliders can function more efficiently. Gautam Reddy, a graduate student in physics, and a team of his colleagues at the University of California, San Diego, described their biomimicry research in a 2018 article in Nature.
 
 
 
The team equipped a two-meter-wingspan, radio-controlled glider with a flight controller and instruments that could measure vertical wind accelerations along the length of the glider and across its wings. Their working theory was that birds use similar sensory information to identify and navigate thermals.
 
 
 
Between 2015 and 2017, they conducted more than 500 flights near Poway, California, each one lasting 20 to 30 minutes. The aim was to train the glider in a concept called "reinforcement" to navigate thermals autonomously. They used their measurements to build a simple algorithm that would steer the glider toward zones of higher lift.
 
 
 
Another study published in the Flow, Turbulence and Combustion journal, also looked at soaring flight, studying behavior in white storks for clues to how the birds stay aloft so effortlessly. The researchers took data observed during stork flight and created a biomimetic computational model that analyzed the way the wing tip changed shape during flight. The study also looked closely at the airflow features around the wing tip feathers during flight. Similar work in pigeons could also aid wing design. For example, Popular Mechanics points to PigeonBot: a highly sophisticated bird-like robot that researchers are using to study how pigeons change the shape of their wings during flight to gain efficiency.
 
 
 
Taming Unpredictability
 
 
 
Though much valuable information can be gleaned from the concept of biomimicry, field studies can be problematic. Atmospheric conditions vary so much hour to hour and day to day, even in the same location, that building a robust AI algorithm for glider aviation using field measurements alone is quite challenging.
 
 
 
"AI algorithms require a lot of data," Reddy said. "One way to add fidelity to the algorithm would be to use numerical simulation and a process called model-based learning."
 
 
 
This model-based approach, which his team did not use, would involve "flying" a simulated glider repeatedly through a simulated atmospheric environment containing realistic thermals, convective currents and turbulence. Atmospheric data and the glider's response to those conditions could be collected on each simulated flight and then used to systematically update and refine the AI algorithm.
 
 
 
A better strategy, Reddy proposes, would be to extract visual and other sensor information from gliders.
 
 
 
"State-of-the-art gliders actually have better aerodynamic properties than birds," he said. "We can also use instruments to gather higher accuracy measurements of temperature, wind velocity, humidity, barometric pressure and other relevant data."
 
 
 
New Rules of Engagement
 
 
 
AI in aviation certainly offers tantalizing prospects.
 
 
 
"If we can come up with a glider that's as efficient as a bird, it could travel across continents," said Reddy. "That capability could be extremely useful for surveillance or remote sensing missions." The one limitation, he admitted, is that gliders rely on thermals, which are normally present during daylight hours only.
 
 
 
AI-controlled gliders could be a perfect complement to high-altitude surveillance systems, such as the U.S. Air Force's Global Hawk unmanned aircraft.
 
 
 
"Gliders are very small, unpowered and virtually silent," said Reddy. "So, they would not need to fly at high altitude. I think they could provide our military commanders with some very interesting options."
 
 
 
AI in the Cockpit and Beyond
 
 
 
Reddy suggests we try out AI as a navigational aid for pilots to enhance their abilities in the air.
 
 
 
"Pilots would still fly as before," he said, "but would use the AI algorithm to suggest the fastest, most efficient route to their destinations, taking advantage of the fastest-flowing streets (thermals) along the route."
 
 
 
AI and biomimicry could also aid other areas of in-flight performance, creating aircraft that fly silently, similar to an owl swooping on its prey. According to Popular Mechanics, owls can inspire quieter aircraft and turbine blades. Analysis of the wing's trailing edge has found that the orientation of the feathers changes the way the air flows as the owl flies.
 
 
 
In a similar way, hummingbird flight dynamics could help drone flight. For example, Penn State University describes novel modeling that's allowing researchers to simulate hummingbird flight, and Science Daily notes that the data from these studies could refine flying robots.
 
 
 
And it's not just birds. The Biomimicry Institute describes AI coupled with slime mold growth can revolutionize materials design. Along with studying bone growth, researchers are developing new approaches that could make aircraft lighter and use less fuel. Combining generative design, AI and 3D printing can even help us create lightweight components, such as cabin partitions that are strong but use less raw material.
 
These are just a few examples of how looking to AI and ecosystems can transform aviation systems. It's clear the future holds some exciting advancements in technology still to come, and nature may play a pivotal role in realizing those innovations.
 
 
 
Northrop Grumman is a leader in the development of autonomous aerial reconnaissance systems. If you'd like to learn more about what we do, check out our work in AI and machine learning.
 
 
 

AI in Aviation Gets a Lift From Biomimicry

Key Takeaways Many animals have rudimentary forms of communication, and AI may help to better understand what these species are saying. Efforts such as the Earth Species Project (ESP) are using natural language processing (NLP) to classify and interpret animal sounds. Along with communication, AI animal analysis has helped develop gecko-like adhesives and improve robot agility.What does the fox say? In the future, we might have an answer that goes beyond a catchy tune. Scientists are using artificial intelligence (AI) to study how to talk to animals — and it's proving to be a challenging endeavor. Researchers have made progress analyzing and classifying animal sounds, but they can only go so far. They're only human. Computers, however, can sort through massive amounts of data to find patterns that aren't obvious."Communication is just a series of repeated sounds, gestures, signals, etc.," said Jay Thakore, a consulting engineer systems architect at Northrop Grumman. "We learn the sound the letters of the alphabet make to form words or that the wave of a hand means 'hello' using repetition. In that same manner, AI can learn the sounds and gestures of animals and then equate those repeated behaviors to words for humans."Interspecies Communication ChallengesIt's hard enough for people to learn a second language, such as a native English speaker studying Mandarin Chinese. There's also body language to consider, such as eye contact, hand gestures and the subtle meaning behind sighs. What's more, humans are only one of the millions of species of animals on Earth.Humans have taught animals to speak in ways we can understand, such as chimpanzees using American Sign Language or pets ringing a bell when they need to go to the bathroom. However, we haven't gotten very far in understanding animals' perspectives. When we're lucky enough to hear quacks, growls and clicks, humans don't often know what they mean. And some animals "speak" in sounds beyond our ears' frequency range.Animals also communicate in other ways, such as dogs sniffing each other, whales slapping their tales in the water and insects producing chemicals called pheromones. To an untrained ear, critter chatter seems nonsensical and random. But experts can translate languages, and AI is a helpful tool because it's particularly good at classification.AI for AnimalsRecent technological advances have opened new pathways for humans to talk with animals. No matter how dedicated and intelligent they were, previous generations of zoologists were limited by the tools invented before or during their lifetimes. Now, technology — such as miniature microphones and drones — make it easier for scientists to eavesdrop on nature. Notably, the type of AI behind streaming apps, voice-controlled assistants and self-driving cars can extract meaning from plentiful animal communication data."People have been exploring and studying animal behavior for years," says Thakore. "AI is in the forefront of everyone's mind because it's everywhere, and people are getting smarter about how to take advantage of it."For example, a nonprofit organization called the Earth Species Project (ESP) is building machine-learning models to understand crow caws, bat screeches and more. Their technology is based on natural language processing, a branch of AI focused on giving computers the ability to understand spoken and written language.But how do developers write algorithms for machine learning, such as an algorithm to classify animal vocabulary? As Thakore explains, AI algorithms are math- and logic-based and adjust themselves to perform better as they're exposed to more data. For the case of animal vocabulary, the algorithm could be a combination of a regression model that expresses a relationship between an input and the output and a decision tree that catalogs the outputs and can be used to determine which outputs happen most often for a particular input. From there, you can build the animal vocabulary.Now What?We humans have a lot to learn from our furry friends. As we get better at AI for animals, the next step is figuring out what to do with this new information and how to use it ethically. Here are a few potential industry use cases: Engineering: A plethora of innovations have already been inspired by animals — robots emulating the way animals climb, adhesives that copy geckos and astronauts landing with cat-like agility. Imagine what we could do if we knew how to 3D print like spiders or build homes as efficiently as ants dig tunnels. Conservation ecology: Some organizations aim to find out how people are harming the environment so we can stop the damage and repair the planet. Animal husbandry: If farmers could talk to animals, they could optimize their care and make informed decisions about raising livestock. Veterinary care and companionship: Who wouldn't want to know what their dog is thinking? Pets can't communicate how they're feeling or what symptoms they're experiencing, but with the help of AI, we could get a better idea of what they're going through. That could affect how we care for them and improve veterinarian diagnoses and treatments.It's still being determined whether AI will help us communicate with animals soon or if this is a futuristic dream, but Thakore remains hopeful: "As far as actually talking to animals, I think that is still a little in the science-fiction realm. But you never know — if someone were to devote enough resources, time and money, the future might not be that far away."Discover more communication use cases for AI.

Is It Possible to Talk to Animals With AI?

Key Takeaways:  Dogs take a "total body" package approach rather than focusing on the face for social recognition. This social behavior preference is associated with anatomical differences in rod and cone density in the retina. Facial-focused species include cats and sheep, as well as humans and primates.What do dogs see when they look at humans? With their wide eyes and steady gazes, it's easy to anthropomorphize our furry friends and imagine they're seeing the same things we are — the precious and particular facial features of a trusted companion. However, according to recent research, that's not entirely accurate. While humans typically take a face-first approach to social interactions, dogs prefer the total body package.The Kin ConsiderationFor dogs, seeing and recognizing familiar faces is a subset of the larger kin and mate selection process. In effect, dogs become familiar with their "pack" — human or otherwise — by creating a mental map of key physical characteristics that include "non-facial bodily cues, acoustic or chemical signs," according to Attila Andics, co-author of the recent study from the Society of Neuroscience.While research from Andics and his colleagues found that dogs are perfectly capable of recognizing human faces, this information isn't their top priority. Using fMRI scans, the research team discovered no difference in dogs' mental activity when looking at human faces or the back of human heads. This suggests that dogs lack the specific face recognition functions found in the brains of humans and other primates. While dogs did show species preferences through greater brain activity when looking at pictures of other dogs compared to humans, they're simply not built with the same facial framework.This isn't to say that your dog doesn't like your face or doesn't enjoy gazing into your eyes. Andics notes, "It is amazing that, despite apparently not having a specialized neural machinery to process faces, dogs nevertheless excel at eye contact, following gaze, reading emotions from our face, and they can even recognize their owner by the face." In fact, research suggests that dogs can differentiate between happy and angry human faces and are capable of recognizing the human smile — often a sign of aggression in other species — as a positive social cue in human/canine interactions. In other words, dogs have evolved their ability to recognize and react to human faces and emotions as part of people-focused packs.The Vision VariancePart of the disconnect between dog and human facial recognition stems from differing volumes of light receptors known as "cones" and "rods". Compared with canine eyes, human eyes have a higher abundance of cone receptors, which are used to distinguish colors. This allows us to detect three distinct colors — red, green and blue — along with variations and combinations of these colors. Lower cone volumes in dogs' eyes mean they can only see two colors, although the jury is out on exactly which ones are part of the canine color experience.Meanwhile, when it comes to the volume of rods, canine vision vastly outpaces that of humans, allowing our four-legged friends to see much better in the dark. Dogs also have an extra layer of eye tissue known as the "tapetum lucidum," which further enhances light reflection into the retina. The result is a visual system that's designed to see shapes in any light condition but isn't well suited to recognizing the specifics and subtleties of human facial features.The Interspecies IntersectionIf dogs can recognize human faces but don't prioritize this physical characteristic, where does that leave us? Are we the only species with dedicated facial processing functions? Not quite. As NPR notes, other primates also have groups of specialized neurons designed to facilitate facial recognition. As our closest evolutionary cousins, this makes sense since many primate species demonstrate human-like interactions within social groups.And primates aren't the only ones with a face focus — research suggests that cats have specific facial expressions that can be decoded by humans with enough observation. The existence of these expressions also suggests that other cats could potentially recognize them and, in turn, observe and interpret human expressions. However, similar to how cats recognize their human-given names but don't respond unless they feel like it, they simply may not care to do so with humans.Also making the list of facial-focused species? Sheep. As pack animals with relatively large brains, it makes sense that sheep learn to quickly recognize common companions. But studies also found a predisposition for facial recognition — even having only seen their human handlers in person, sheep were quickly able to recognize their photograph. In addition, they were able to recognize facial images both straight-on and from an angle, and with similar precision as humans.The Eyes Have ItWhat do dogs see when they look at us? Trusted kin and familiar pack mates. And although canine vision may use a broader data set to ensure species safety, dogs' natural tendency toward empathy and engagement means that, while your face isn't the biggest priority, they're always happy to take a look.Find out how AI can help you communicate with animals.

What Do Dogs See? It's Rough, But Faces Aren't First Priority

Key Takeaways: AI in retail is becoming widespread and upgrading the customer experience at scale. AI tools deliver human-friendly output from computers so customers can easily understand the information given. Technology helps personalize shopping choices, drive better customer service and support sustainable initiatives by optimizing delivery routes, for example.Every time you step inside a store or shop online, retailers get to know you better. Artificial intelligence (AI) in retail is becoming widespread and making a difference. From personalized shopping experiences to automation that frees staff for more attentive customer service and more, innovations in AI are revolutionizing the retail experience. As different organizations explore the potential for machine technology, how is AI changing the retail industry?Where Does AI in Retail Fit In?First, a definition. Edureka quotes John McCarthy's 1956 description of AI as "the science and engineering of making intelligent machines." Bringing the definition up to the present day, AI seeks to be a human-friendly interface with machines, giving people the opportunity to speak to computers and get answers that aren't spit out as line after line of code. And this is where it gets useful for retail.AI can switch the focus to making shopping a better experience for shoppers. Customers not only want ease and convenience — they also want less frustration, a more personalized shopping experience and the ability to switch from in-person retail to browsing online with delivery to the home.With this in mind, there's the potential to collect massive amounts of data within stores. Leveraging this data for consumer and business benefit is the way forward. According to ITRex, the top AI functions in retail will be customer care and personalization, quality control, inventory management and pricing, and fraud detection. And far from heralding a no-contact, machine-only, isolated experience, AI technology can help free up store staff to engage more with shoppers. In return, businesses can expect better productivity, efficiency and profits.Trends for AI in Retail 2024AI is in its hobbyist phase. According to the Gartner Hype Cycle for Emerging Technologies, AI is at the "Peak of Inflated Expectations" and on the cusp of its descent into the "Trough of Disillusionment." Despite this gloomy-sounding prediction, this means that, although seemingly all-encompassing right now, AI is not going to be the blanket solution for everything and every business. But it may be for retail.Algolia predicts that the AI in retail market will hit $24 billion by 2028 and reach a staggering $45 billion by 2032. The retail sector is exploring what value AI might bring, and there are some exciting projects on the go. But how is AI changing the retail industry exactly?Walmart, for one example, is developing AI for shoppers based on previous shopping history, both personalizing the experience in-store and using the information to manage inventory stocking levels efficiently. No more empty shelves or items on back order. The company is also exploring AI to optimize delivery, with tools to plan routes based on weather, traffic and location to reduce costs and make the process more sustainable. Going forward, sustainability through intelligent routing, supply chain management, previous purchase patterns and reducing waste materials could be a key benefit.Noting that 75% of customers are more likely to buy again from brands that personalize the experience, Neontri lists Amazon's customized homepage as a major player in this arena. Other use cases for effective AI include loyalty programs, like the one Starbucks offers, that can help ensure customers remain faithful to the brand. Freeing up worker time comes from using generative AI to streamline basic tasks, like product descriptions, such as in H&M's online catalogue where the technology is used to automate those inputs. Humans aren't removed from the equation since they check all machine output, but AI does free staff time up for more creative content projects and customer engagement.Online shopping gets a boost from AI, too. Retailers, such as Warby Parker, can offer virtual tools that allow customers to see how well an item suits them before buying. Chatbots and virtual assistants are becoming more intelligent and intuitive, as well, which can help in customer service, finding products and answering queries in a timely manner.In short, artificial intelligence in retail isn't a future dream. It's here and helpful already, and it's proving to be one of the industry's biggest assets.Learn more about Northrop Grumman's leadership in secure and ethical AI that goes beyond the technical to deliver creative and innovative technology solutions.

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