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8 mind-blowing space documentaries to watch now on NOVA – PBS

For almost 50 years, NOVA has explored the cosmos, taking viewers across our solar system, into distant galaxies, and right up to the edge of a black hole. From a Mars rover swooping down to the red planet, to a probes daring encounter with an asteroid, weve followed NASA and other space missions as theyve revealed the universe to humanity. Now we present a curated selection of space documentaries from the past five years so you can explore the universe alongside the scientists who make the journey possible.

In July 2022, NASAs James Webb Space Telescope released its first images, looking further back in time than ever before to show our universe in stunningly beautiful detail. But that was just the beginning: With tons of new data and spectacular images flooding in, Webb is allowing scientists to peer deep in time to try to answer some of astronomys biggest questions. Whenand howdid the first stars and galaxies form? And can we see the fingerprints of life in the atmospheres of distant worldsor even within our own solar system?

A NASA spacecraft named Lucy blasts off from Cape Canaveral on a mission to the Trojans, a group of asteroids over 400 million miles from Earth thought to hold important clues about the origins of our solar system. Just hours before, in Senegal, West Africa, a team of scientists sets out to capture extraordinarily precise observations vital to the success of the Lucy missioncrucial data needed to help NASA navigate Lucy to its asteroid targets across millions of miles of space. The teams leader, Senegalese astronomer Maram Kaire, takes viewers on a journey to investigate his nations rich and deep history of astronomy, reaching back thousands of yearsand the promising future ahead.

How did NASA engineers build and launch the most ambitious telescope of all time? Follow the dramatic story of the James Webb Space Telescopethe most complex machine ever launched into space. If it works, scientists believe that this new eye on the universe will peer deeper back in time and space than ever before to the birth of galaxies, and may even be able to sniff the atmospheres of exoplanets as we search for signs of life beyond Earth. But getting it to work is no easy task. The telescope is far bigger than its predecessor, the famous Hubble Space Telescope, and it needs to make its observations a million miles away from Earthso there will be no chance to go out and fix it. That means theres no room for error; the most ambitious telescope ever built needs to work perfectly. Meet the engineers making it happen and join them on their high stakes journey to uncover new secrets of the universe.

In the five-part series NOVA Universe Revealed, we delve into the vastness of space to capture moments of high drama when the universe changed forever. In this episode, we tackle an age-old question: Are we alone? Or do other lifeforms and intelligences thrive on worlds far beyond our own? Ultra-sensitive telescopes and dogged detective work are transforming alien planet-hunting from science fiction into hard fact. Join NOVA on a visit to exotic worlds orbiting distant suns, from puffy planets with the density of Styrofoam to thousand-degree, broiling gas giants. Most tantalizing of all are the Super-Earths in the Goldilocks zone, just the right distance from their sun to support life, and with one of them signaling lifes essential ingredient, water, in its atmosphere. Are we on the brink of answering that haunting question?

Follow along as NASA launches the Mars 2020 Mission, perhaps the most ambitious hunt yet for signs of ancient life on Mars. In February 2021, the spacecraft blazes into the Martian atmosphere at some 12,000 miles per hour and lowers the Perseverance Rover into the rocky Jezero Crater, home to a dried-up river delta scientists think could have harbored life. Perseverance will comb the area for signs of life and collect samples for possible return to Earth. Traveling onboard is a four-pound helicopter that will conduct a series of test flightsthe first on another planet. During its journey, Perseverance will also test technology designed to produce oxygen from the Martian atmosphere, in hopes that the gas could be used for fuelor for humans to breatheon future missions.

In October 2020, a NASA spacecraft called OSIRIS-REx attempts to reach out and grab a piece of an asteroid named Bennu to bring it back to Earth. The OSIRIS-REx team has just three chances to extend its spacecrafts specialized arm, touch down for five seconds, and collect material from the surface of Bennu. But if they can pull it off, scientists could gain great insight into Earths own originsand even learn to defend against rogue asteroids that may one day threaten our planet.

On the 50th anniversary of the historic Apollo 11 Moon landing, NOVA looks ahead to the hoped-for dawn of a new age in lunar exploration. This time, governments and private industry are working together to reach our nearest celestial neighbor. But why go back? The Moon can serve as a platform for basic astronomical research; as an abundant source of rare metals and hydrogen fuel; and ultimately as a stepping stone for human missions to Mars and beyond. Join the next generation of engineers that aim to take us to the Moon, and discover how our legacy of lunar exploration won't be confined to the history books for long.

Black holes are the most enigmatic and exotic objects in the universe. Theyre also the most powerful, with gravity so strong it can trap light. And theyre destructive, swallowing entire planets, even giant stars. Anything that falls into them vanishesgone forever. Now, astrophysicists are realizing that black holes may be essential to how our universe evolvedtheir influence possibly leading to life on Earth and, ultimately, us. In this two-hour special, astrophysicist and author Janna Levin takes viewers on a journey to the frontiers of black hole science. Along the way, we meet leading astronomers and physicists on the verge of finding new answers to provocative questions about these shadowy monsters: Where do they come from? Whats inside? What happens if you fall into one? And what can they tell us about the nature of space, time, and gravity?

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Machine Learning Executive Talks Rise, Future of Generative AI – Georgetown University The Hoya

Keegan Hines, a former Georgetown adjunct professor and the current vice president of machine learning at Arthur AI, discussed the rapid rise in generative Artificial Intelligence (AI) programs and Georgetowns potential in adapting to software like ChatGPT.

The Master of Science in Data Science and Analytics program in the Graduate School of Arts & Sciences hosted the talk on March 17. The discussion centered on the rapid development of generative AI over the past six months.

Hines said generative AI has the capacity to radically change peoples daily lives, including how students are taught and how entertainment is consumed.

I definitely think were going to see a lot of personal tutoring technologies coming up for both little kids and college students, Hines said at the event. I have a feeling that in the next year, someone will try to make an entirely AI-generated TV show. Its not that hard to imagine an AI-generated script, animation and voice actors.

Imagine what Netflix becomes. Netflix is no longer recommend Keegan the best content; Netflix is now create something from scratch which is the perfect show Keegans ever wanted to see, Hines added.

Hines then discussed algorithms that generate text. He said the principal goal of these algorithms is to create deep learning systems that can understand complex patterns over longer time scales.

Hines said one challenge AI faces is that it can provide users with incorrect information.

These models say things and sometimes theyre just flatly wrong, Hines said. Google got really panned when they made a product announcement about Bard and then people pointed out Bard had made a mistake.

Bard, Googles AI chatbot, incorrectly answered a question about the James Webb Space Telescope in a video from the programs launch Feb. 6, raising concerns about Googles rushed rollout of Bard and the possibility for generative AIs to spread misinformation.

Hines said the potential for bias and toxicity in AI is present, as seen with Microsofts ChatGPT-powered Bing search engine, which manufactured a conspiracy theory relating Tom Hanks to the Watergate scandal.

Theres been a lot of research in AI alignment, Hines said. How do we make these systems communicate the values we have?

Teaching and learning in all levels of education will need to adapt to changes in technology, according to Hines.

One example is a high school history teacher who told students to have ChatGPT write a paper and then correct it themselves, Hines said. I think this is just the next iteration of open book, internet, ChatGPT. How do you get creative testing someones critical thinking on the material?

Hines said OpenAI, the company behind ChatGPT, noticed larger, more complex language models were more accurate than smaller models due to lower levels of test loss or errors made during training.

A small model has a high test loss whereas a really big model has a much more impressive test loss, Hines said. The big model also requires less data to reach an equivalent amount of test loss.

OpenAIs hypothesis was that the secret to unlocking rapid advancement in artificial intelligence lies in creating the largest model possible, according to Hines.

There didnt seem to be an end to this trend, Hines said. Their big hypothesis was, lets just go crazy and train the biggest model we can think of and keep going. Their big bet paid off and these strange, emergent, semi-intelligent behaviors are happening along the way.

Hines said he is optimistic about the fields future, and he predicted AI will be able to produce even more complex results, such as creating a TV show. It was really only about ten years ago that deep learning was proven to be viable. Hines said. If were going to avoid the dystopian path and go down the optimistic path, generative AI will be an assistant. It will get you 80% of the way and you do the next 20%.

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Machine learning identifies ‘heart roundness’ as a new tool for diagnosing cardiovascular conditions – Medical Xpress

This article has been reviewed according to ScienceX's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility:

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Deep learning-enabled analysis of medical images identifies cardiac sphericity as an early marker of cardiomyopathy and related outcomes. Credit: Med/Vukadinovic et al.

Physicians currently use assessments like heart chamber size and systolic function to diagnose and monitor cardiomyopathy and other related heart conditions. A paper published in the journal Med on March 29 suggests that another measurementcardiac sphericity, or roundness of the heartmay one day be a useful implement to add to the diagnostic toolkit.

"Roundness of the heart isn't necessarily the problem per seit's a marker of the problem," says co-corresponding author Shoa L. Clarke, a preventive cardiologist and an instructor at Stanford University School of Medicine. "People with rounder hearts may have underlying cardiomyopathy or underlying dysfunction with the molecular and cellular functions of the heart muscle. It could be reasonable to ask whether there is any utility in incorporating measurements of sphericity into clinical decision-making."

This proof-of-concept study used big data and machine learning to look at whether other anatomical changes in the heart could improve the understanding of cardiovascular risk and pathophysiology. The investigators chose to focus on sphericity because clinical experience had suggested it is associated with heart problems. Prior research had primarily focused on sphericity after the onset of heart disease, and they hypothesized that sphericity may increase even before the onset of clinical heart disease.

"We have established traditional ways of evaluating the heart, which have been important for how we diagnose and treat heart disease," Clarke says. "Now with the ability to use deep-learning techniques to look at medical images at scale, we have the opportunity to identify new ways of evaluating the heart that maybe we haven't considered much in the past."

"They say a picture is worth a thousand words, and we show that this is very true for medical imaging," says co-corresponding author David Ouyang, a cardiologist and researcher at the Smidt Heart Institute of Cedars-Sinai. "There's a lot more information available than what physicians are currently using. And just as we've previously known that a bigger heart isn't always better, we're learning that a rounder heart is also not better."

This research employed data from the UK Biobank, which includes genetic and clinical information on 500,000 people. As part of that study, a subset of volunteers had MRI imaging of their hearts performed. The California-based team used data from a subset of about 38,000 UK Biobank study participants who had MRIs that were considered normal at the time of the scans. Subsequent medical records from the volunteers indicated which of them later went on to develop diseases like cardiomyopathy, atrial fibrillation, or heart failure and which did not.

The researchers then used deep-learning techniques to automate the measurement of sphericity. Increased cardiac sphericity appeared to be linked to future heart troubles.

The investigators also looked at genetic drivers for cardiac sphericity and found overlap with the genetic drivers for cardiomyopathy. Using Mendelian randomization, they were able to infer that intrinsic disease of the heart musclemeaning defects not caused by heart attackscaused cardiac sphericity.

"There are two ways that these findings could add value," Ouyang says. "First, they might allow physicians to gain greater clinical intuition on how patients are likely to do at a very rapid glance. In the broader picture, this research suggests there are probably many useful measurements that clinicians still don't understand or haven't discovered. We hope to identify other ways to use imaging to help us predict what will happen next."

The researchers emphasize that much more research is needed before the findings from this study can be translated to clinical practice. For one thing, the connection is still speculative and would need to be confirmed with additional data. If the link is confirmed, a threshold would need to be established to indicate what degree of sphericity might suggest that clinical interventions are needed. The team is sharing all the data from this work and making them available to other investigators to begin answering some of these questions.

Additionally, ultrasound is more commonly used than MRI to image the heart. To further advance this research, replicating these findings using ultrasound images will be useful, they note.

More information: Shoa L. Clarke, Deep learning-enabled analysis of medical images identifies cardiac sphericity as an early marker of cardiomyopathy and related outcomes, Med (2023). DOI: 10.1016/j.medj.2023.02.009. http://www.cell.com/med/fulltext/S2666-6340(23)00069-7

Journal information: Med

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What the AI-generated image of Pope Francis means for the imagination – Vox.com

A recent viral image of Pope Francis wearing an unusually hip white puffer jacket was both a fake created by generative AI and an omen that marked the accelerating collapse of a clearly distinguishable boundary between imagination and reality.

Photorealistic images of fictions like Donald Trump getting arrested while stumbling in a sea of cops can now be generated on demand by AI programs like Midjourney, DALL-E 2, and Stable Diffusion. This sets off alarm bells around how misinformation may thrive. But along with risks, AI-generated imagery also offers a great leap forward for the human imagination.

Seeing is believing goes both ways. Image-generating AI will allow us to see realistic depictions of what does not yet exist, expanding the kinds of futures we can imagine as visual realities. The human imagination doesnt build ideas from scratch. Its combinatorial: The mind cobbles together new ideas from accumulated bits and pieces it has been exposed to. AI-generated images will greatly increase the raw material of plausible worlds the mind can imagine inhabiting and, through them, the kinds of futures we perceive as possible.

For example, its one thing to read a description or see an illustration of a futuristic city with inspiring architecture, public transportation woven through greenery, and spaces designed for human interaction, not cars. Its another to see a spread of photorealistic images of what that could actually look like. By creating realistic representations of imagined realities, text-to-image-generating AI can make it easier for the mind to include new possibilities in how it imagines the world, reducing the barriers to believing that they could become a lived reality.

Last Friday, Reddit user u/trippy_art_special posted the image of the pope to the Midjourney subreddit, the generative AI platform used to produce it. The post contained four variations (a hallmark of Midjourney) of the pope ensconced in an on-trend long, puffy, white coat. One even had him in dark sunglasses, which looked especially smooth, even mysterious, in contrast to the radiant white of the coat and the deep chain.

The image was widely mistaken as real, and the popes outfit was big news over the weekend. Once people caught on that the image was fake, it became even bigger news. No way am I surviving the future of technology, the American model Chrissy Teigen tweeted.

Debates over why this particular image went viral or why so many people believed it to be real will soon be moot. For something that appears so convincing, why wouldnt we believe it? Neither was this the first media brush between Pope Francis and high fashion. In 2008, the Vatican daily newspaper quashed rumors of designer loafers, stating, The pope, therefore, does not wear Prada, but Christ.

For those who scrutinized the image, you could still find clues of falsehood. A few inconspicuous smudges and blurs. But Midjourneys pace of improvement suggests correcting these remaining signs will happen swiftly. What then?

At The Verge, senior reporter James Vincent likened AI-generated imagery to the dawn of hyperreality, a concept developed by the French philosopher Jean Baudrillard. Sooner or later, Vincent wrote, AI fakes are going to become hyperreal, masking the distinction entirely between the imaginary and the real.

Its easy to imagine the nightmare that could follow. Hyperreality is usually invoked as a concern over simulations displacing reality, posing real and looming threats. AI fakes will offer fertile grounds for a new and potentially harrowing era of misinformation, rabbit holes unmoored from reality, and all manners of harassment. Adapting media literacy habits and protective regulations will be crucial.

But there is an upside: While AI fakes threaten to displace what the mind perceives as reality, they can also expand it.

In 1998, two leading philosophers Andy Clark and David Chalmers published a paper on their idea of the extended mind. They argued that cognitive processes are not confined within the boundaries of the skull, but extend out through the tools we use to interact with the world. These aids a notebook, for example are tangled up in how we think and are part of our extended minds. In this view, tools can become something like cognitive limbs: not separate from our capacities, but part of them.

You can flip this around: Building new tools is a way of building new mental capabilities. Until last weekend, most people could have imagined some image of what the pope might look like in a fashion-week puffer jacket (unless you have aphantasia, in which mental imagery is not part of your internal experience). But those mental images can be slippery. The more artistic among us could have drawn a few ideas, prompting a richer image. But soon, anyone will be able to imagine anything and render it into photorealistic quality, seeing it as though it were real. Making the visual concrete gives the mind something solid to grab hold of. That is a new trick for the extended mind.

You should understand these tools as aids to your imagination, says Tony Chemero, a professor of philosophy and psychology at the University of Cincinnati and member of the Center for Cognition, Action, and Perception. But imagining isnt something that just happens in your brain, he added. Its interacting skillfully with the world around you. The imagination is in the activity, like an architect doing sketches.

There is disagreement among cognitive scientists on which kinds of tools merge with our extended minds, and which retain separate identities as tools we think with rather than through. Chemero distinguished between tools of the extended mind, like spoons or bicycles, and computers that run generative AI software like Midjourney. When riding a bicycle and suddenly wobbling through an inconveniently placed crater in the concrete, people tend to say, I hit a pothole, instead of, The bicycle wheel hit the pothole. The tool is conceived as a part of you. Youd be less likely to say, I fell on the floor, after dropping your laptop.

Still, he told me that any tool that changes how we interact with the world also changes how we understand ourselves. Especially what we understand ourselves as being capable of, he added.

Clark and Chalmers end their paper with an unusually fun line for academic philosophy: once the hegemony of skin and skull is usurped, we may be able to see ourselves more truly as creatures of the world. Thinking with AI image generators, we may be able to see ourselves in picture-perfect quality as creatures of many different potential worlds, flush with imaginative possibilities that blend fact and fiction.

It might be that you can use this to see different possible futures, Chemero told me, to build them as a kind of image that a young person can imagine themselves as moving toward. G20 summits where all the world leaders are women; factories with warm lighting, jovial atmospheres, and flyers on how to form unions. These are now fictional realities we can see, rather than dimly imagine through flickers in the mind.

Of course, reality is real, as the world was reminded earlier this week when 86-year-old Pope Francis was taken into medical care for what the Vatican is calling a respiratory infection, though by Thursday he was reportedly improving and tweeting from the hospital. But if seeing is believing, these tools will make it easier for us to believe that an incredible diversity of worlds is possible, and to hold on to their solid images in our minds so that we can formulate goals around them. Turning imagination into reality starts with clear pictures. Now that we can generate them, we can get to work.

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Where nature meets technology: Machine learning as a tool for … – McGill Tribune

With the dangers of continued fossil fuel use and environmental mismanagement unfolding before our eyes in the form of intense heat waves, droughts, and wildfires, its obvious that dramatic, transformative action must be taken.

Throughout the pessimistic debate about the effectiveness of climate change policy and methods of pollution mitigation, almost every solution under the sun has been proposed. Some have suggested the widespread use of carbon capture technology, while others, like Boyan Slat, have developed ways to remove garbage from our oceans. But one technology has the potential to revolutionize climate action: Artificial intelligence (AI).

In a recent paper spearheaded by professor David Rolnick of the Department of Computer Science, researchers studied the application of machine learning to climate science in great detail. Each section of the article explored a specific sectorincluding electricity, industry, or infrastructureand explained the ways machine learning could be used to reduce the sectors impact on the climate.

Machine learning is an offshoot of AI. While the aim of AI is to develop computers that can think like a human, machine learning is more about training computers on experiences and data to recognize patterns and make decisions.

Machine learning is looking at large amounts of data, finding the patterns that are common across that data and linking those to what the algorithm is asked to do, Rolnick said in an interview with The McGill Tribune.

Uses for machine learning fall into a few categories, according to Rolnick: Monitoring, optimization, simulation, and forecasting. Take, for example, how forecasting can be applied to the study of electricity.

Machine learning is used to predict the amount of electricity that will be in demand at a given point in time so there is enough supply to meet that but not more than there needs to be, Rolnick explained. Understanding how much power is needed and how much power is available is important to make sure the grid is running effectively and without waste.

Since AI cannot plant trees or pass legislation, its practical application may seem abstract. However, its effects are tangible: AI has been used to increase crop yield in India, improve electricity efficiency on wind farms by planning for weather, and improve data centres efficiency.

Most of the technologies that I am talking about are at some level of deployment. For example, the U.K.s national grid has already integrated deep learning models into forecasting supply and demand of electricity and has greatly increased efficiency as a result, Rolnick said. The UN uses AI to guide interventions in flooded areas [.] These are not just research projects and its fundamentally important.

Although AI is an incredibly promising technology, there are a couple of drawbacks to be addressed. One of these drawbacks is human biassince humans write the algorithms and supply the human-collected data to train machine learning, these tools can replicate human biases. To prevent these biases, then, human bias needs to be correctedthere is no software fix.

We cannot technology our way out of most biases, Rolnick said. The solutions to biases in technology are the same as solutions to biases in any other part of human endeavour. That means they are hard, but they are solvable via human choices.

This technology also requires enormous quantities of energy for algorithms to be trained and maintained, but the energy can be minimized by designing efficient algorithms and planning applications carefully.

Its also worth noting that most of the negative climate impacts of AI globally come from how it is used, not the direct energy consumption, Rolnick wrote in a follow-up email.

Although machine learning models can be quite energy hungry, the models Rolnick uses are not exceedingly energy-intensive. With careful planning, scientists hope that the emissions benefits from these models outweigh their energy consumption.

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Machine-learning-powered extraction of molecular diffusivity from … – Nature.com

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By the Book: Sarah Bakewell Is No Fan of Thrillers and Mysteries – The New York Times

How do you organize your books?

Most of them are organized with sinister precision by genre and author, except that biographies are by subject and history is roughly chronological. I cant help it; Im a librarian. Not only that, but I tend to spot anomalies. If someone has moved a book out of order, I fix it with my gimlet eye almost as soon as I walk in the room. Of course, this leads to people moving my books around for fun, to see if Ill notice. (And sometimes I dont.)

Every year, I receive a book of stories, memoirs, drawings or clerihews, as well as a wall-calendar of splendid literary caricatures, all created by my generous and gifted friend in Seattle, Brad Craft. Nothing can ever beat that.

As a child I read books manically, greedily and repeatedly, and loved anything with an animal in it. My two favorite series were Willard Prices gung-ho stories about two brothers collecting wild creatures for their fathers zoo, and the Adventure series by Enid Blyton, which sent four children and a parrot into dangerous situations up a river, out to sea, inside a hollow mountain and away with a traveling circus.

By my early teens, I was grabbing any book for adults that came within my reach, and making whatever skewed, half-baked sense of it I could. Woolfs The Waves, Nabokovs Lolita, Ginsbergs Howl, Luke Rhineharts The Dice Man, David Nivens The Moons a Balloon, a bit of Shakespeare it all went into the ravenous maw. I do remember being more perplexed than usual by The Sex-Life Letters: Fascinating Correspondence From Todays Men and Women About the Variety of Their Sexual Attitudes and Experiences, edited by Harold and Ruth Greenwald. I think that had animals in it too.

Ive long liked both philosophy and biography, but the balance keeps shifting toward the biography end. In my 20s, a night in with Heidegger was my idea of fun. Now, given a choice between contemplating the being of beings and finding out, for example, that Vita Sackville-Wests mother once papered an entire room with used postage stamps well, its the stamps every time.

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Top 9 Ways Ethical Hackers Will Use Machine Learning to Launch … – Analytics Insight

The top 9 ways ethical hackers will use machine learning to launch attacks are enlisted here

Several threat detection and response platforms are using machine learning and artificial intelligence (AI) as essential technologies. Security teams benefit from being able to learn on the go and automatically adjust to evolving cyber threats.

Yet, certain ethical hackers are also evading security measures, finding new vulnerabilities, and scaling up their cyberattacks at an unprecedented rate and with fatal outcomes by utilizing machine learning and AI. Below are the top 9 ways ethical hackers will use machine learning to launch attacks.

Machine learning has been used by defenders for decades to identify spam. The attacker can alter their behavior if the spam filter they are using offers explanations for why an email message was rejected or creates a score of some sort. They would be utilizing lawful technology to boost the effectiveness of their attacks.

Ethical Hackers will use machine learning to creatively alter phishing emails so that they dont appear in bulk email lists and are designed to encourage interaction and clicks. They go beyond simply reading the emails text. AI can produce realistic-looking images, social media profiles, and other content to give communication the best possible legitimacy.

Machine learning is also being used by criminals to improve their password-guessing skills. Moreover, they use machine learning to recognize security measures so they can guess better passwords with fewer attempts, increasing the likelihood that they will succeed in gaining access to a system.

The most ominous use of artificial intelligence is the creation of deep fake technologies that can produce audio or video that is difficult to differentiate from actual human speech. To make their messages seem more credible, fraudsters are now leveraging AI to create realistic-looking user profiles, photographs, and phishing emails. Its a huge industry.

Nowadays, a lot of widely used security technologies come equipped with artificial intelligence or machine learning. For instance, anti-virus technologies are increasingly searching for suspicious activities outside the fundamental signs. Attackers might use these tools to modify their malware so that it can avoid detection rather than defend against attacks.

Attackers can employ machine learning for reconnaissance to examine the traffic patterns, defenses, and possible weaknesses of their target. Its unlikely that the typical cybercriminal would take on anything like this because its difficult to do. It may, however, become more publicly available if, at some time, the technology is marketed and offered as a service through the criminal underworld.

Malware may not be able to link back to its command-and-control servers for instructions if a business recognizes that it is under assault and disables internet connectivity for impacted computers.

A machine learning model can be deceived by an attacker by being fed fresh data. For instance, a compromised user account may log into a system every day at 2 a.m. to perform unimportant tasks, fooling the system into thinking that working at that hour is normal, and reducing the number of security checks the user must complete.

Fuzzing software is used by reputable software engineers and penetration testers to create random sample inputs to crash a program or discover a vulnerability. The most advanced versions of this software prioritize inputs such as text strings most likely to create issues using machine learning to generate inputs that are more targeted and ordered. Because of this, fuzzing technologies are not only more effective for businesses but also more lethal in the hands of attackers.

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[VIDEO] Terrifying Tree Well Rescue Highlights Why You Should … – SnowBrains

Terrifying.Its one of those superlatives websites use to get you to click. But in this case, its justified. Its terrifying to think how quickly your day can change from shredding deep powder and having the time of your life to actually fighting for your life. Its terrifying to imagine the thoughts going through that boarders mind. Its terrifying to think what would have happened had that skier not been in the right place at the right time. Terrifying.

I just watched this twice, astonished by how lucky to be alive the snowboarder is. Both men were riding through trees in the Mount Baker, WA, sidecountry in extremely deep snow, and its pure serendipity that the rescuer skied over the buried boarder.

Despite finding myself urging the skier to move quicker while watching, he does everything right and saves this strangers life. I cant imagine what was going through the boarders mindhe was rendered immobile by his powdery tomb.

Thank you to Francis Zuber for sharing this video, being in the right place at the right time, and for an expert rescue.

Tree well and deep snow suffocation are serious problems in the Western USA and Canada.

Incidents occur with deep snow accumulations and tree well immersions, where a skier orsnowboarder falls into an area of deep, unconsolidated snow and becomes immobilized.

Since 2001, California has had more snow immersion deaths than any other state.

A tree well is a void or depression that forms around the base of a tree and most likely under thebranches that hang from those trees, disguising the void. This void may contain a mix of low-hangingbranches, loose snow, and air. While skiing or snowboarding, it is tough to determine if a treewell exists, so skiers and riders should treat every tree the same.

Skiers and snowboarders must understand the risks of deep snow, educate themselves, and strictlyadhere to safety recommendations, including always skiing or riding within sight of a partner, especiallywhen off a designated trail, within the trees, or in a gladed area.

Tree Well Fall. Image: Ski California

Each skier or snowboarder controls their level of risk. Only you can preventthis type of accident from happening. To minimize your risk, you must know how to travel safely with your partner(s) in theseungroomed deep-snow areas. Always ski or ride with a partner and within close sight.

Always stay in visual contact so your partner(s) can see you if you fall. Visualcontact means stopping and watching your partner descend at all times, then proceedingdownhill while they watch you at all times. It does no good if your partner is alreadywaiting for you in the lift line while still descending the slope.

Stay close enough to either pull or dig out. Hold your breath while reading this if you have questions about what is close enough to assist someone in a tree well. The time before your partner needs air may be how long you have to pull or dig the person out of danger. Other factors, such as creating an air pocket or the entrapped skiers position, may also affect this critical timeframe.

Remember, if you lose visual contact with your partner, you could lose your friend. It is essential to know that most people who have died in deep snow or tree well accidents had been skiing or riding with a partner at the time of their accident. Unfortunately, none of these partners had immediate visual contact, so they could not help promptly.

Use appropriate equipment to minimize risks. When skiing or snowboarding in high-risk areas for deep snow or tree wells, wear a helmet, enter the ski patrols phone number into your smartphone, and carry a whistle if you need to get someones attention if you become entrapped in deep snow or a tree well.

If you still have questions, contact your ski patrol. Ask your ski patrol about the current risks and conditions with deep snow at your local ski area before you explore risky terrainsuch as tree areas, glades, or off-trail terrain where deep snow and tree well risksexist.

Follow these helpful tips to stay safe on the Mountain. All the recent snowfall in California, Utah, and the west, along with more in the forecast, makes for dangerous conditions, so always take necessary precautions and never venture out alone.Stay Safe Out There. Image: Ski California

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Human behavior and performance in deep space exploration: next … – Nature.com

The next phase of human space exploration includes the establishment and habitation of a lunar gateway, a permanent base on the Moons surface, and exploratory crewed missions to Mars. As human activity in space moves from Low Earth Orbit (LEO) operations, such as those that take place on the ISS, to deep space exploration, the crews will face a different set of psychological challenges1. These include extended mission durations, increased distance from Earth, prolonged isolation and confinement, reduced size of crew quarters, lack of privacy, communication latency, need for increased autonomy in decision-making processes, and lack of short-term rescue possibilities, amongst other known and as yet unknown demands2,3.

There is good evidence to suggest that astronauts behavior, health, and performance can be impacted by the demands encountered in future space missions4. For the purposes of this work, issues of behavior, health, and performance are separated and considered according to discrete cognitive, affective, behavioral, social, and mental health components. While these function areas are related, distinguishing between them provides the opportunity for conducting well-specified and targeted psychological studies. Cognitive dimensions include issues of perception, vigilance, judgment, memory, and reaction time, amongst other executive functions5. When discussing cognition, there are clear synergies with the neurosciences. Affective experiences refer to emotions, feelings, and moods, which can be shaped by a persons physiology and subjective experience. Behaviors include observable individual and interpersonal actions and the execution of skilled performances. For instance, before they are executed, motor actions must be planned in the brain and rely on complex neuronal networks. Team-level social functions include relevant team process dynamics, such as experiences of cohesion and conflict. Mental health is a relevant component, which is related to the psychosocial, affective, cognitive, emotional, and physical challenges that astronauts face during missions. Mental health includes the importance of managing psychophysical stress and promoting well-being, both on individual and team levels.

This white paper is the result of a consensus among experts invited by the European Space Agency to update the roadmap for scientific research for the next decade6. The psychology working group (corresponding to the authors of this paper) was composed of experts in psychological science with a large research experience in the space context, currently engaged in research with ESA, in the space environment, analogue environments, and ground-based research. Research gaps were identified by the experts, referring to their direct research experience and to their knowledge of the scientific literature, and discussed to reach a consensus. The work specifically focused on the behavioral and performance aspects. Some of the relevant aspects that could impact the astronauts life, including the effects of space radiations, were not addressed by this group, as they have been included in other working groups report.

In this report, we broadly refer to long-duration (i.e., >30 days) space exploration (LDSE) missions, with a particular focus on deep space voyages, outside the earths atmosphere, which are distinct from current long missions in LEO. Even with this differentiation, it is worth keeping in mind that the psychological challenges of relatively near-Earth explorations, including incoming Moon missions, may be qualitatively different from those that will be experienced during long-distance deep space voyages, such as human missions to Marswhich also include long travels and an extreme routine. In the following, many of the open questions related to psychological function in space are framed in the context of LDSE missions. However, where we refer to space missions broadly, questions are pertinent to LEO, lunar and deep space missions. With respect to an understanding of human performance and behavior issues of spaceflight, the European Space Agency and its partners can build on several years of studies and experiences on the ISS and sub-orbital flights, as well as during simulations studies and in analogue environments. Deep space exploration, though, has some different characteristics that will require ad-hoc preparation and new studies to answer currently open questions. For this reason, further studies will be needed not only to be conducted during long-duration space missions, but also in other settings, including analogue environments and other isolated and confined settings. These environments share some similarities with the space context, including isolation and confinement. Some analogue missions are conducted in specifically designed facilities, such as HERA (Human Exploration Research Analog), the underwater research station NEEMO (NASA Extreme Environment Mission Operations), and the MARS500 isolation chamber. Other facilities include Antarctic stations, such as the Concordia base, an Italian/French research facility considered one of the most remote outposts on Earth. Most of these environments allow to study behavioral, physical, and team dynamics, and to test countermeasures that can be implemented in space missions. Other analogue environments allow to recreate specific challenges of space research, including radiations (e.g., the NASA Space Radiation Lab) and bedrest implications (e.g., the: envihab facility, in Cologne, Germany).

Risks to behavior, health, and performance during deep space exploration could be mitigated and astronaut function optimized with the application of effective countermeasures. However, research is required to identify and further develop or refine the strategies and approaches that might be used to enable astronauts to maintain elevated well-being and high-performance standards on LDSE missions. In the following section, we distinguish between questions related to basic issues of adaptation and countermeasures. The former largely deals with the understanding of psychological aspects of deep space exploration and the impact of unique deep space mission demands upon behavior, health, and performance. This includes the role of individual differences in adaptation, and broader mechanisms underlying individual and team phenomena that are relevant to human spaceflight7,8,9. Important information in this area could be inferred from different types of observational and correlational studies. Countermeasures deal with the specific actions and interventions that space agencies can enact to mitigate the risks of future missions. This might include a refined selection process or the application of inflight psychological training and support. While most questions in the white paper focus on pre-mission and inflight activities, it is also important to consider psychological experiences in the post-mission phase, to ensure that astronauts well-being is robust after the end of what are likely to be physically and psychologically demanding voyages. Astronauts constitute a very limited community on Earth. When addressing these fundamental questions, it is therefore critical to consider whether and how the findings can be transferred to the general public as many activities parallel, to some extent, what space travelers will undergo.

Most knowledge in space psychology has focused on short-duration mission, relatively close to Earth, and with synchronous contacts with mission control. New incoming space missions pose different challenges, in terms of psychological adaptation and the definition of countermeasures to mitigate risks.

To inform the identification of effective preparatory and preventative countermeasures for future space missions, there are several questions related to basic issues of psychological adaptation that need to be resolved. These questions relate to both in-mission dynamics influenced by the interaction between individual and team factors and contextual demands, and what happens in the post-mission phase. Open questions identified by the expert scientific group, largely reflect unknowns associated with missions beyond Low Earth Orbit (LEO).

For the ISS, there already exists a standard human behavior and performance competency framework for crewmembers10. There have also been efforts to standardize the psychological/psychosocial and behavioral data collected during space agency sponsored research activities11,12,13. This research has been used in ground-based studies in analogue environments, such as NASA-HERA, VaPER, AGBRESA, SIRIUS 19 & 20, as well as Antarctica. Standard measures have been used on the International Space Station in multiple expeditions14. These standard measures have included cognition batteries, personality surveys, and neuroscience assessments of sensorimotor measures, together with biomarkers, immune markers, and actigraphy. All these data will help create useful benchmarks for LDSE missions. Nevertheless, despite the important research and the progress made so far, there is a need to continue this research in analog environment and long-term spaceflight. In addition, the scope of this research should be enlarged with respect to including also physiological measures as possible indicators of mental health and performance state, as well as measures of team cohesion and climate which can capture team dynamics in the course of LDSE missions. Questions that must be addressed include:

What variables capture relevant information on cognitive, affective, behavioral function, and team functions? And what outcomes are informative about mental health issues?

What physiological biomarkers provide a valid insight to the psychological experience of astronauts?

To date, there have been many studies on spaceflight stressors that have utilized both space platforms and ground-based analogs. However, for LDSE missions the type and magnitude of stressors encountered will be different from those experienced in the past. Research is needed to evaluate the impact of such stressors on individual and team function. Certain stressors could be evaluated in analog environments (e.g., isolation, confinement, chronic threat, sleep deprivation, sensory deprivation), while the effects of others will need to be examined in space (microgravity, hypogravity). Research conducted during spaceflight, in analog environments and simulation studies, thus far, has already provided important insights into the effects of microgravity, prolonged confinement, and isolation, as well as sleep deprivation on human performance, well-being, and behavior15. However, the impact of stressors such as prolonged hypogravity, transitions between different levels of hypogravity, prolonged radiation exposure, chronic threat due to lacking rescue possibilities, extreme levels of crew autonomy, and Earth-out-of-view unique to LDSE missions, have not been addressed yet, or only to a very limited degree (e.g., effects of autonomy during simulation studies)16,17. Their impact on cognition, affective experiences, behavior and performance, team dynamics, mental health, and performance should be carefully assessed. Some of these stressors can be studied in analog environments (e.g., autonomy), others require spaceflight experiments during upcoming missions to the Moon (e.g., hypogravity) and still others might be only investigated during actual LDSE missions (e.g., Earth-out-of-view effects). Moreover, the relationship among stress biomarker dynamics (e.g., heart rate variability, cortisol), related biological processes, and individual and team function within these settings will have to be clearly unfolded.

While there have been many studies on individual differences in relation to human spaceflight, data that exist remains relatively limited in a predictive sense1. Additional research is needed to examine how baseline individual and team characteristics are likely to impact upon in-mission individual and team function. Moreover, day-to-day team performance indicators need further exploration in the context of extreme environments.

How do demographic criteria (e.g., age, gender identity, and ethnicity) influence adaptability and individual function during space missions?

What is the unique impact of sexuality on the psychological function of crewmembers in space?

How do individual psychological differences (e.g., personality, motivation, and values) influence adaptability and individual and team function during space missions?

What individual difference factors should be used to inform effective team composition decisions for LDSE missions?

How do team structure and composition influence crew adaptability and function during space missions?

How do social dynamics, values, norms, and culture influence crew adaptability and function during space missions?

How can we assess team performance and dynamics on a small scale, and how does that relate to the overall mission success?

The high levels of autonomy that will be encountered on LDSE missions mean that crew will have increased responsibility for their own self-care/self-management18. New research is needed to examine the coping and health and performance self-regulation strategies that individuals and teams use to maintain their function. Although there have been initial studies on coping in analog settings19, there is limited empirical data on what astronauts do to manage themselves and their teams during space missions (LDSE or otherwise). Open questions are:

What resources and equipment (e.g., food variety, entertainment systems) contribute to effective coping and self-regulation during LDSE missions?

What coping and regulatory strategies are effective for optimizing cognition, affective experiences, behavior and performance, individual and team function, and, more in general, to promote mental health during LDSE missions?

Integrative studies that examine the interactive effects of psychosocial factors alongside physiological responses and other features of the environment, such as habitat design and resource availability, are required to provide a deeper insight to the human experience in space. This is especially important for LDSE where new systems, equipment, and habitats will be used. Among the unanswered questions, we find:

How do spaceflight stressors, demographic criteria, individual differences, coping, and regulatory strategies interact to impact individual and team function during LDSE missions?

What factors predict the extent to which skill fade occurs during LDSE missions?

How do physical features of the environment (e.g., habitat, architecture, internal conditions, and plants) impact upon individual and team adaptation?

How do food perception (e.g., taste and olfaction), texture, and variety impact upon astronauts affective, experiences?

How do astronauts interact with reporting systems designed to capture safety-critical information (e.g., medication use)?

What individual and team factors impact upon compliance with reporting systems designed to capture safety-critical information (e.g., medication use)?

The post-mission phase has addressed by research one both overwinter missions in Antarctica (e.g., ref. 20), and NASA post-flight standard measures14. However, it still requires a structured and deepened exploration, which has been sometimes overlooked. Anecdotal reports from the astronauts of the lunar missions in the 1960ies and 70ies suggest, that the mental processing of such extreme experiences represents a challenge also after the actual mission. With LDSE missions, the importance of questions related to reintegration, recovery, and mental processing of the mission experiences will significantly raise. Specifically, crucial open questions which need to become addressed more systematically relate to what positive or negative after-effects might occur after prolonged spaceflights, and what regulatory strategies might be effective to support reintegration, recovery, and rehabilitation upon return from LDSE missions. For example, there is limited empirical information on how individuals cope during their return from space and what strategies they use to maintain their health and well-being during reintegration and recovery. Research is needed to identify the strategies that individuals use and what impact that has upon their cognition, affect, and behavior in the post-mission phase. Open questions include:

What individual coping and regulatory strategies are effective for optimizing cognition, affective experiences, behavior, and performance, and, more in general, mental health, during the return, transition, and recovery following LDSE missions?

How do social networks contribute to effective astronaut coping and self-regulation during their return?

How do individuals prepare themselves and their families to redeploy on new missions?

Addressing open questions related to basic issues of adaptation should provide the knowledge to develop effective countermeasures for the envisaged future space missions. Psychological countermeasures might target selection and training, in-mission, and post-mission phases. The emphasis in this white paper is on identification and testing and evaluating the impact of applied measures.

Current selection and training protocols have been designed for LEO missions. Research is needed to identify how individual and team psychological selection should be adapted for LDSE missions. Specifying and developing the training needed to ensure optimal crew function on LDSE is also needed. While existing processes might continue to have utility, this should be confirmed with empirical evidence. Questions that still have to be addressed include:

What individual difference factors inform on psychological suitability for LDSE missions?

How should psychological suitability be assessed during the assessment and selection of astronauts for LDSE missions?

What methods are available to inform the selection of psychologically compatible or incompatible teams?

Do these methods raise any ethical concerns?

How should current selection processes be adapted and validated to inform the effective psychological selection of crewmembers for LDSE missions?

What unique training protocols need to be developed and how should they be delivered (e.g., what strategies, tools, and techniques) to prepare individuals and teams to respond effectively to the demands of LDSE missions?

How should individuals and teams be trained to respond effectively to critical or off-nominal incidents when operating autonomously in space? What protocols and policies need to be developed?

How should approaches and methods for optimizing affective experiences and cognition (e.g., mind-body strategies, emotion regulation, and flexible coping) during space missions be trained?

How should approaches and methods for optimizing team function (e.g., communication, cooperation, collaboration, and conflict resolution) during space missions be trained?

How should astronauts be trained to deal with extreme and unexpected events (e.g., deaths and psychiatric issues) that might occur during LDSE missions?

Support during and after LDSE missions will rely on accurate monitoring, diagnosis, and deployment of effective countermeasures. Although research in these areas is currently being undertaken, there remain a number of open questions about how to best support individuals and teams in space. Studies conducted in microgravity and on ground-based analogs can be used to identify and evaluate the efficacy of approaches to support individuals and crew during and after return from LDSE missions.

What methods, measures, and metrics should be used to monitor individual and team function, sleep, and fatigue during space missions?

How should work/life balance be managed during different phases of a LDSE mission?

How can astronauts be supported and what resources do they need to allow them to rest and relax away from work tasks?

How should sleep and fatigue management skills for LDSE missions be trained and maintained?

What non-pharmaceutical approaches are effective for sleep and fatigue management during LDSE missions?

How should methods used to minimize skill fade and degradations in task performance during LDSE missions be administered?

How should astronauts be supported to maintain their motivation to engage in healthy behaviors (e.g., exercise) across the duration of a LDSE mission?

What and how should support be provided following the occurrence of extreme and unexpected events (e.g., deaths and psychiatric issues)?

How should approaches and methods for optimizing mental health, affective experiences, cognition, behavior, and performance (e.g., mind-body strategies, emotion regulation, and flexible coping) during space missions be maintained?

How should approaches and methods for optimizing team function (e.g., communication, cooperation, collaboration, and conflict resolution) during space missions be maintained?

How should autonomous and digital systems be used to effectively support individual and team functions during LDSE missions?

How do human factors impact upon autonomous and digital system interaction?

What features must be included in autonomous and digital systems for effective use in space?

How do trust and privacy impact the likelihood of astronauts engaging with autonomous and digitally delivered countermeasures?

What communication types/methods are effective as a mechanism for support during autonomous missions?

How should communications be adapted to effectively support team function during autonomous LDSE missions?

What family support mechanisms need to be established to minimize potential issues due to separation and lack of family contact during LDSE missions and what would be the optimal communication frequency and duration?

How should families and social groups be effectively-prepared to support those returning from space?

Psychosocial function of astronauts can be impacted by the system that the individual and team are operating in. The constraints of LDSE missions mean that new systems, architectures, and habitats will need to be developed. There are open questions about how to engineer and design the systems, architectures, and habitats to facilitate optimal function in space:

How should autonomous and digital systems be designed for use during LDSE missions? In particular, what would be the benefits of using virtual reality-based approaches?

How should communications be designed to effectively support individual functions during autonomous LDSE missions?

What architectural and habitat design features should be implemented to enhance individual and interpersonal function during LDSE missions?

What features should be considered and designed into safety-critical reporting systems (e.g., medication reporting systems)?

How might an astronauts connection to nature be established through architecture and habitat design?

Several of the identified knowledge gaps have direct relevance for micro- and hypogravity research. In particular, this holds true for a better understanding of the effects of hypogravity on human cognition and performance, which are already relatively well understood for some basic cognitive functions, but which lack knowledge with respect to higher executive functions or issues related to skill maintenance across different levels of (hypo-)gravity. The clear majority of the key knowledge gaps previously identified, however, relate to basic issues of individual or crew adaptation to long-term confinement and isolation and to effective countermeasures for maintaining well-being and performance of crews under such conditions. To close these knowledge gaps is of most relevance for future exploration missions to the Moon and Mars which will involve more extreme conditions of isolation and confinement than has been known from other environments, thus far. Even though the conditions of travel to the Moon will be more extreme than what we know from near-Earth orbital spaceflight and overwintering in Antarctica, they do not seem to be different in a qualitative sense (i.e., the demands are amplified rather than being especially unique). Thus, it might be expected that at least some of the current knowledge about the psychological effects of isolation and confinement as well as hypogravity might be generalized to missions in lunar orbits or even stays on the lunar surface. In contrast, future deep space missions to Mars will represent a qualitatively much more extreme change (e.g., with respect to autonomy, restricted means of crew-ground communication, lack of evacuation possibilities) compared to what has been known about effects of isolation and confinement from other fields already, and, thus, will provide completely new psychological challenges which currently are not well understood. In a sense, future missions to Mars will resemble past ambitious naval explorations, such as those conducted by Vespucci and Colombo, in which humans pushed their limits beyond a line that had never been crossed before21. However, on the other hand, we are arguably more prepared than a crew on a ship that did not know what they were about to find, as, we can prepare such missions using probes, satellites, and many other remote observation techniques. Among the preparation activity, psychological research addressing the knowledge identified gaps will be a fundamental step in any space program focusing on exploratory human missions to Mars and beyond. While microgravity and hypogravity pose serious challenges to the central nervous system22, most of the knowledge gaps about behavioral and psychological aspects are not related. Therefore, the research needed does not necessarily involve research during actual space missions. Naturalistically extreme environments on Earth (e.g., Antarctica) and, even better with respect to experimental control, simulation studies on the ground will, in many cases, provide appropriate environments for such research.

The research gaps highlighted in this report are in line with the ones identified by NASA23. The need to identify and validate countermeasures to promote health and performance, to define improved monitoring and assessment strategies, and to investigate and optimize team dynamics, for example, are shared concerns between this report and the NASAs Evidence Book for Risk of Adverse Cognitive or Behavioral Conditions and Psychiatric Disorders23. Similar conclusions have been described in a recent NASA report24 about team research, highlighting the lack of data availability from the space context, and the need for further research on the topic, including studies in analogue environments and subject matter expert interviews.

Space travels magnify the challenges posed to a team of astronauts, such as confinement, and lack of external communication. However, there are also many situations that regular workers can face on the Earth and that includealthough to a lesser extent, some features astronauts can meet. For example, teams sometimes work in remote places, where communication is constrained. Therefore, more classical Earth-based activities can benefit from the transfer of this fundamental research.

Research conducted to fill knowledge gaps identified in the psychological phenomenon linked to space exploration may be applied to optimize the behavior, health, and performance of crewmembers in these extreme conditions. Once the processes that might contribute to the possible impairment have been identified, it could be envisaged to elaborate specific countermeasures that could help crewmembers to maintain and enhance their health and performance. For example, innovative and new technologies like virtual reality may be stimulated by this kind of challenge and be used to provide sensory stimulation and train cognitive and psychomotor performance of crew without there being a requirement to undertake live operations. New technologies (e.g., artificial intelligence) may also be used to reduce communication delays and, thus, mitigate isolation consequences.

As frequently observed with space research, many new devices, technology, or stress management techniques, may, once tested in space, be efficiently applied to adaptation and performance on Earth in specific conditions. For example, during the sanitary crisis period, some results concerning adaptation to isolation and confinement obtained in space or in polar environments have been useful for people during confinement periods. Some operational or mental strategies identified and validated in space may be transferred to life on Earth in isolation, confined, and extreme conditions. In many instances, this might be in settings that have societal important e.g., climate scientists, defense and security personnel, and anti-poaching wildlife rangers. Since constraints on the design of such techniques can be largely relaxed for Earth applications compared to Space applications, more flexibility is a promise for wider applications for the public. Finally, the space brand exerts great charm on the public and can be a channel for the promotion of societal and psychological improvements. For example, pro-environment behaviors studied and reported by the astronauts may be mimicked on Earth; well-being promotion strategies that are currently developed for space explorations, such as certain mind-body techniques, can also be implemented on the planet, following the examples from the space context. There are therefore several environments in which behavioral space research can have a positive impact on Earth research and society, including educational, organizational, professional, and recreational contexts. To facilitate these benefits, the communication strategy implemented by all the involved actors (national agencies, private companies, astronauts) should be mindful of these potential implications.

Table 1 reports the overarching categories representing the key open psychological research questions related to lunar and LDSE missions. Many of the open questions could be partly addressed in ground-based analogs. However, where the unique demands of missions beyond LEO and in deep space are relevant, ongoing research across various platforms will be needed. To effectively prepare for future LDSE missions, such as a Mars expedition, we suggest these questions should be addressed during a short to medium timeframe. There are certain unknowns that will only be elucidated over longer time periods and perhaps during a Mars mission itself. We recommend these timelines (3, 5, and 10 years) as a suggestion for addressing research gaps, although we are aware that research often requires longer times, so they do not necessarily correspond to expected research results.

The countermeasures below have all been used to mitigate the psychological demands of spaceflight. However, beyond a few initial studies, there has been a relatively limited attempt to empirically test the impact of their application (see Table 2). Before these methods can be considered evidence-based further space and/or analog-based research would be needed.

This white paper reported the results of a consensus statement among experts invited by ESA about the existing knowledge gaps on behavioral and performance topics of space research. This is particularly timely, as exploration missions are moving from low orbit to deep space destinations, with new psychological and team challenges forthcoming. While this is a non-systematic review of these research gaps, the working group consisted of experts in space psychology, who have been engaged in space research for ESA. Pre-, during-, and post-mission challenges and research gaps were considered, referring to promising countermeasures, either with preliminary evidence of their effectiveness, or to be developed and tested. The results summarize a set of challenges and questions to be addressed, but also some potential answers that have already been provided by the scientific community over decades of space psychology research. New empirical evidence is required to address most of these gaps, collected with specifically designed studies. It must be noted that to address the contextual constraints (e.g., number of active astronauts, or available analogue environment), some of these gaps can be tackled with thorough reviews or white papers incorporating all extant research findings. While space psychology is likely to have an important future, researchers should also be mindful of previously developed knowledge.

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