Category: Academic Analysis

Source: The Conversation – USA – By Olivia Harper Wilkins, Assistant Professor of Chemistry, Dickinson College

Astronomers can use telescopes to find specific molecules in the atmospheres of neighboring planets, in nebulae – clouds of interstellar dust and gas – hundreds or thousands of light-years away, or in galaxies beyond the far reaches of the Milky Way.

So far, astronomers have found more than 350 molecules in the spaces between and around stars in just under a hundred years – the first such molecule was reported in 1937. Each year, the cosmic chemical stockroom grows by anywhere from a handful to a couple of dozen new finds. Many of these molecules are precursors to biomolecules, meaning they might provide hints about life’s origins elsewhere in the cosmos.

As an astrochemist, my research is all about chemicals found in space, especially in distant cosmic clouds where infant stars are born. Even so, the precise observations captured by these telescopes never cease to amaze me.

With this ongoing boom in astrochemical census data, there is a lot to be excited about. Sometimes, however, this excitement can be premature. Finding molecules in places people will likely never visit is no simple task, so vetting and sometimes correcting these observations is a continual process – especially for molecules whose signals aren’t as strong.

‘Seeing’ molecules in space

Astronomers can’t visit neighboring planets, let alone distant star-forming regions. So, how do they see what is out there?

Astronomers observe the cosmos with telescopes that collect all different wavelengths of electromagnetic energy. For astrochemistry, they typically use radio telescopes. These satellite-dishlike instruments are used to “see” radio waves, which have wavelengths much longer than the human eye can perceive.

When molecules freely tumble around as gases in space, they rotate, and this motion releases energy in the form of photons, or electromagnetic particles. Different types of rotations require different levels of energy. Each photon carries that energy with it to a telescope, which records its signal. The more photons of a given energy, the stronger that signal.

If a radio telescope records all of the expected signals for a given molecule – its spectrum – then astronomers can confidently say that they have detected that molecule.

Infrared telescopes, such as the James Webb Space Telescope, or telescopes that detect visible light, such as the Hubble Space Telescope, can also be used for astrochemistry. Both kinds of telescopes, however, collect chemical signals, which are often more difficult to distinguish from one another.

Knowing what to look for

Behind every discovery of a new molecule in space is months or even years of work to capture a chemical’s “fingerprints,” or its spectrum.

I spent about a year doing this kind of work at the University of Cologne in Germany as a Fulbright research fellow. There, I used computer models of astrophysically interesting chemicals to predict what their spectra would look like.

In the lab, I injected the chemicals into a glass tube held under vacuum to mimic conditions in space. Using sensitive instruments, I recorded what a radio telescope would see if it were looking at only that molecule.

Astronomers had already found some of these molecules in space, and my colleagues and I were reexamining them, but we were also looking at molecules that we predicted might exist somewhere in space.

I worked with a team of scientists to adjust the computer inputs over and over until the simulated spectra matched the experimental data. When simulated spectra matched the experiments, that meant that the simulated spectra reliably modeled what a molecule’s fingerprint looks like in space. Reliable model spectra allow astronomers to detect chemical features at frequencies beyond what they can measure in the laboratory.

While my contributions to the Cologne team didn’t lead to a discovery of a new molecule in space, I gained an appreciation for the work behind the scenes of molecule discovery. The laboratory measurements are done precisely so that astronomers can be confident in their detections.

When detections get cloudy

Even with powerful radio telescopes and thorough experiments, some detections aren’t quite as clear as astronomers would like them to be. Sometimes, the signals are too faint for astronomers to be totally confident that they represent the molecules they think they do. Other times, there are too many molecule signals crowded together, causing different signals to blend.

Scientists have detected molecules relevant to biological processes back on Earth in comets and the atmospheres of other planets. These detections are exciting, but most scientists exercise caution to avoid jumping to conclusions because those molecules generally can exist outside of living things.

Sometimes, however, the excitement overshadows the caution and leads to premature conclusions.

Scientists often get excited when new molecules, especially biologically relevant molecules, are potentially present, and they want to share those findings with the world. Some researchers are also concerned about being the first to publish a new result, especially because a lot of telescope data is publicly available after a brief proprietary period.

Perhaps one of the most exciting nondiscoveries in astrochemistry was that of glycine in interstellar space more than 20 years ago. Glycine is the simplest amino acid, a type of molecule essential for life as we know it. Finding this molecule in a nebula would change how scientists think about the evolution of life’s ingredients.

Follow-up studies showed that key signals were missing in the initial report of glycine. As a result, astrochemists now generally agree that glycine had not been found in star-forming nebulae.

More recently, another molecular discovery has been scrutinized: the potential detection of phosphine in Venus’ atmosphere. Unlike with glycine, scientists have not yet agreed on whether phosphine, which is associated with some biological processes on Earth, is indeed present on Venus.

Initial reports of phosphine on Venus spurred chatter about biosignatures and evidence of potential life on Earth’s much hotter sister planet. However, follow-up studies by other scientists couldn’t confirm the initial results.

Over the past five years, scientists have continued to try to confirm or definitively refute Venusian phosphine.

Vetting claims

When reading about discoveries of new molecules in interstellar space or on other planets, how can you be confident in the detections you are reading about? It’s important to watch out for flashy headlines that claim signs of life have been found elsewhere in the universe. Molecule discoveries that rely on only one or two signals being detected are generally less reliable than those based on five or more signals.

For discoveries that tease hints of life on other worlds, other scientists are almost certainly going to try to reproduce the results. If you wait a few months for the initial fanfare to die down, you can do a web search to see what new results have come out to support – or refute – the original claim.

Olivia Harper Wilkins receives funding from NASA and the National Radio Astronomy Observatory (NRAO).

– ref. Potential signs of life on distant planets sound exciting – but confirmation can take years – https://theconversation.com/potential-signs-of-life-on-distant-planets-sound-exciting-but-confirmation-can-take-years-277451

Source: The Conversation – USA – By Yunyao Li, Assistant Professor of Earth and Environmental Sciences, University of Texas at Arlington

Curious Kids is a series for children of all ages. If you have a question you’d like an expert to answer, send it to CuriousKidsUS@theconversation.com.

Why is water wet? – Philip S., age 12, Northville, Michigan

Spring is often a rainy season. If you get caught in a downpour without an umbrella, you will quickly learn what it means to be wet. But what is it about water that makes it wet?

I am an atmospheric scientist, and water is a fundamental part of the atmosphere. I study storms and wildfires, both of which are closely connected to water.

Why water is wet has to do with how water molecules interact with each other and the things around them.

Wet you can see

Imagine you accidentally spill water on your clothes one day. You will notice two things: First, the water spreads out on the cloth, and the wet part sticks to your body more than the dry part does; and second, the wet area feels cool.

Wet clothes stick to your body and water spreads across the fabric because water molecules are strongly attracted to other molecules, a chemical property called adhesion.

One important reason why water molecules are so attracted to other molecules is that they’re polar. Like a microscopic magnet, one end of the molecule carries a small negative charge, while the other end carries a small positive charge.

Many everyday materials, such as glass, skin and clothing, are also polar. When water touches these surfaces, the electric charges on those materials attract the water molecules and hold them in place. This strong attraction also helps water spread out over surfaces. Whether something feels “wet” to you has to do with how good a liquid is at staying in contact with a surface. Water feels wet because its molecules stick tightly to each other and to your skin.

Compared to water, mercury has much weaker attraction to surfaces. Mercury’s molecules are much more attracted to each other, meaning they have very strong cohesion. As a result, mercury does not easily stick to other surfaces.

The cool feeling of being wet comes from evaporation. Liquids need energy to change into gas because they must overcome the forces holding molecules together before they can float away. They take this energy from their surroundings in the form of heat.

When you step out of a pool and the water on your swimsuit evaporates, you might feel cold because it’s taking away heat from your body. Wet things often feel cool because evaporation takes heat away from the skin. Sometimes something that feels cool can trick you into thinking it’s also wet, even if no liquid is actually present.

Evaporative cooling is very useful in daily life, and other liquids can also do it. For example, when you clean a wound with an alcohol wipe, it also feels cool. Like water, alcohol evaporates and carries heat away from your body. Similarly, when sweat evaporates, it removes heat from your body and cools you down.

Wet you cannot see

Sometimes you can feel damp even when you don’t see any water. This is related to the amount of water vapor in the air, also called humidity.

Air can hold only a limited amount of water vapor. When there is already a lot of water vapor in the air, evaporation slows down. This makes it harder for sweat on your skin to evaporate, so you feel sticky and wet.

When air becomes completely full of water vapor, the vapor starts to condense and turn back into liquid water to form dew or fog.

How much water vapor air can hold depends on temperature. Warm air can hold more water vapor, while cold air can hold less. As temperature increases, water molecules gain more energy and can more easily escape their attraction to each other and become a vapor.

This is why dark or shady places often feel damp. These areas get less sunlight, stay cooler and cannot hold much water vapor. As a result, water does not evaporate easily and the area stays wet.

A lot of water, but not wet

Because the air’s ability to hold water depends on temperature, sometimes the air can contain a lot of water vapor but you don’t feel wet.

For example, when you are near a fire, the burning process produces water vapor. However, because the temperature is also higher, the air can hold more water vapor. This speeds up evaporation. If there are wet clothes nearby, they may actually dry more quickly.

In weather forecasts, scientists use relative humidity to describe how humid the air feels, rather than the actual amount of water vapor in the air.

Because hot air can hold so much moisture that relative humidity stays low, people are often surprised when I tell them that wildfires release large amounts of water vapor. Fire is the last thing most people associate with being wet.

Hello, curious kids! Do you have a question you’d like an expert to answer? Ask an adult to send your question to CuriousKidsUS@theconversation.com. Please tell us your name, age and the city where you live.

And since curiosity has no age limit – adults, let us know what you’re wondering, too. We won’t be able to answer every question, but we will do our best.

Yunyao Li does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

– ref. Why is water wet? – https://theconversation.com/why-is-water-wet-279521

Source: The Conversation – USA – By Sara Konrath, Associate Professor of Philanthropic Studies, Indiana University

Most people know the basics of healthy living that become more important as you grow older: Eat plenty of vegetables, exercise regularly, sleep well, have a social life, limit your alcohol consumption and don’t smoke.

As an economist and social psychologist who study altruism and health, we wondered whether civic engagement might play a role as well.

In 2022, the American Medical Association, an organization representing doctors, noted that voting could potentially have health benefits. So we conducted a study that directly tested this idea: We examined whether older Americans – people who are 65 and up – who vote live longer than nonvoters.

Older adults vote at a higher rate than younger adults in the United States. In Wisconsin, the focus of our study, the voting rate of older adults is even higher.

We used data from the Wisconsin Longitudinal Study, a study which has followed a randomly selected sample of Wisconsin high school graduates since 1957. We compared the long-term health of older adults who voted in the 2008 presidential election to those who did not vote in that election. Using objectively verified voting records from Catalist, which tracks Americans’ voting behavior, along with official National Death Index records, we found that voters were 45% less likely to die within five years after the 2008 election, 37% less likely to die 10 years after the election, and 29% less likely to die 15 years later.

We also examined voting in the 2004 and 2012 presidential elections and found that the results were stronger for more recent elections – those held in 2008 and 2012 – compared to the earlier one held in 2004.

You may wonder whether this is just because healthier people are more likely to vote in the first place.

It’s easier to vote when you’re healthy than when you’re not, but this does not fully explain our results. Voters still had a lower risk of dying when we controlled for demographic factors such as gender, marital status and income, other forms of civic engagement such as volunteering, and a voter’s health status prior to voting.

We also found that those in poorer health to begin with benefited more from voting 15 years later than those who had been healthier before they voted.

Here’s another finding: How someone voted didn’t matter. When we compared what happened to older adults who cast their ballots in person to those who mailed their ballots, we found that both groups had about an equally lower risk of dying over the 15-year period.

It also did not matter whether a voter’s preferred candidate won. We found that although it can be stressful when the candidate you support loses, the people we studied experienced similar long-term health benefits of voting regardless of their political affiliation.

Why it matters

Scientists have long known that people who volunteer for nonprofits experience many health benefits, including a longer lifespan.

Voting is, arguably, also an altruistically motivated act. That’s because individual voters are aware that their one vote will not change the outcome of a national election.

What still isn’t known

If you are wondering why voting predicts lower mortality risk, well, so did we.

One possibility is that as with other civic engagement activities, including volunteering, voting may trigger positive biological responses that support well-being. Other researchers have found ample evidence showing that volunteering can boost the brain’s reward system, reduce stress and even slow some aspects of aging.
Although we didn’t test for these in the Wisconsin Longitudinal Study, they may help explain why people who vote tend to have better health outcomes than those who don’t.

Voting might also improve health through a sense of self-efficacy, civic duty and social connection, since it is both an altruistic and shared activity.

Although the exact explanations aren’t known, studies consistently show a link between volunteering and a lower mortality risk, which suggests that participating in civic life – even something as simple as casting a ballot – may be good for your health, like going for a run or eating vegetables.

The authors do not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and have disclosed no relevant affiliations beyond their academic appointment.

– ref. Older Americans who vote live longer than those who don’t – new research – https://theconversation.com/older-americans-who-vote-live-longer-than-those-who-dont-new-research-279933

Source: The Conversation – USA (2) – By Scott Friend, Professor and Schaefer Endowed Chair in Marketing, University of Dayton

Across the business world, companies often double down on struggling ideas, retreating only after clear evidence shows they won’t work.

A recent spectacular example was Meta’s metaverse push. After the organization invested US$80 billion over several years, it announced changes in March 2026 that all but abandoned its grand strategy.

But many companies are following the opposite approach of quickly walking away from failure instead of blindly sticking to a vision. Google ended its cloud gaming service Stadia when it failed to take off, choosing instead to reuse the technology elsewhere. Mercedes abandoned its zero-sidepod F1 concept once it clearly hit a competitive dead end. And Slack transitioned from a failed gaming app to a ubiquitous intra-office messaging platform.

What drove all these decisions wasn’t a tolerance for failure. Instead, executives read signals of weakness early, confronted inconvenient evidence and changed course before greater losses accumulated. In other words, they embraced “failing fast.”

As business professors who study sales performance and sales failure, we argue that this concept is one of the most important yet most misunderstood ideas in our field. It’s not about celebrating mistakes or lowering standards, nor does it give leaders permission to abandon rigor or give up easily.

At its core, it’s about creating the conditions for faster learning: building the managerial discipline to recognize when an opportunity is unlikely to pay off, stopping before sunk costs deepen, and redirecting scarce resources to more promising bets. And this is a strategy that can work for any company, at any level, no matter how high or low the stakes.

The Slack model

Slack is everywhere these days. But few recall that it was actually founded in 2011 as a multiplayer online game called Glitch that failed to take off. The company, then known as Tiny Speck, shut it down in 2012, but in the process its leaders identified hidden value in an internal communication tool they had built simply to coordinate their own work.

This practical side project looked like a tool that could do well in the burgeoning market for team-collaboration software. So the company pivoted by deploying its remaining capital and talent to launch Slack in 2013. Since that time, Slack has become one of the fastest-growing enterprise software platforms in history, eventually leading to a
$27.7 billion acquisition by the business platform Salesforce in 2021.

Stories like these are often told as tales of persistence, but they’re actually examples of disciplined quitting. Similar cases include 3M’s accidental invention of Post-it Notes (first used as ad hoc bookmarks for hymnals); Shopify’s pivot from selling snowboards to enabling e-commerce infrastructure; and Instagram’s shift from a cluttered check-in app to a focused photo-sharing platform.

Together, these stories suggest that success depends not only on staying the course but also on recognizing early when the course is no longer worth pursuing and changing to a better one.

Know when (and how) to fold ‘em

Despite this history, much of business culture still promotes a simpler message that grit drives success.

This mindset, however, can also foster a sunk cost fallacy. Myriad examples of this trap linger across business lore to this day: Blockbuster failing to accept an offer to purchase Netflix and instead expanding its physical footprint model; Kodak inventing digital cameras but opting to prioritize its dominant film business; and the persistent joint venture funding of the Concord supersonic airliner despite strong evidence that the project wouldn’t become commercially viable. All three businesses eventually went bankrupt after once dominating their respective industry.

Sunk costs, in short, come into direct tension with notions of failing fast. But our research underscores the latter’s benefits, showing that associated payoffs extend beyond high-profile corporate pivots and even apply to everyday decision-making. Studies in business-to-business sales, for example, find that walking away early from low-potential opportunities can improve motivation and performance.

That said, there’s an important condition: This approach only works when executives and customer-facing personnel have a grounded understanding of what the company can do and what customers want – rather than treating early exit as a suboptimal default.

Across these varied cases, our research has pointed to another clear pattern that emerges: Failing fast is typically structured in a way to make decisions under uncertainty, with three distinct stages. Again, the origin story of Slack is a good example.

The first step is to gather information that suggests whether any given project will succeed. These signals can come from direct observation or data. The goal is to build an early, evidence-based picture of whether an effort is gaining traction. In the case of Slack, CEO Stewart Butterfield and his team recognized through direct user experience that Glitch, the game, just wasn’t fun. But they also saw other signals that showed structural limitations preventing a viable path to succeed on mobile devices.

The next step is to interpret the collected data – combining experience, contextual awareness and analytical tools to distinguish between ideas that warrant investment and those that don’t. Structured approaches, like comparing goals to historical benchmarks, can make sure that assessments are consistent and grounded in evidence rather than intuition alone. In Slack’s case with Glitch, Butterfield synthesized the early signals and concluded that, despite significant sunk costs, the game didn’t justify further resources.

The final and most difficult step is execution. When signals and analysis point to early exit as the most effective course, acting on that conclusion is hard. Withdrawing, even when continuing no longer makes strategic sense, feels counterintuitive in an environment that rewards persistence. That’s why executives need to make the case that there’s a smarter way to allocate time, capital and attention. With Slack, Butterfield followed through on his analytical convictions by shutting down the game and repurposing internal technology to create Slack – reframing this “failure” as a strategic reallocation.

A lesson for everyone

These lessons extend far beyond the world of sales, startup culture and Big Tech. Managers face similar choices in product development, partnerships and hiring – situations where the real risk is not failure, but failing late. This way, strong organizations understand how to fail by design. That is, defining success and failure criteria early, testing assumptions quickly and containing any downside before commitment becomes wasteful. These are, in fact, universal lessons that apply across industries, up and down the chain.

As a more poetic analogy, we turn to the sea. No skilled sailor tries to cross every channel. Some waters will test their endurance, while others will open up new routes. The best sailors prove sound judgment by reading the winds early and changing course before a storm takes hold.

Business leaders face the same choice. Growth comes from neither persistence alone nor reflexive retreat, but from knowing when the effort no longer creates value.

The authors do not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and have disclosed no relevant affiliations beyond their academic appointment.

– ref. Perseverance doesn’t always pay off for companies – sometimes it’s better to ‘fail fast’ – https://theconversation.com/perseverance-doesnt-always-pay-off-for-companies-sometimes-its-better-to-fail-fast-279946

Source: The Conversation – USA (2) – By Ahmed Elgammal, Professor of Computer Science and Director of the Art & AI Lab, Rutgers University

OpenAI officially discontinued its video generation tool, Sora, on April 26, 2026.

I’m a computer scientist who’s been developing AI tools and studying their evolution and adoption for the past decade, and I wasn’t surprised by OpenAI’s decision to shut down Sora.

To me, the challenges Sora faced reflect deeper limitations of AI’s creative capacities that are becoming harder to ignore.

Problems from the start

OpenAI unveiled Sora on Feb. 15, 2024, as an AI tool that gave users the ability to create short videos from text prompts. To pull this off, the technology essentially predicted how images would change from frame to frame based on what it had “learned” from millions of hours of existing footage.

But from the start, there were problems with it.

First, Sora was expensive to run. Generating video requires far more computing power than creating text or images, making it challenging for OpenAI to keep costs under control. Nor was it bringing in enough revenue to justify those costs, especially compared with other AI products that are cheaper to operate and easier to monetize. According to The Wall Street Journal, Sora was losing US$1 million per day.

Second, the early hype – TechPowerUp declared Sora the “Text-to-Video AI Model Beyond Our Wildest Imagination” – didn’t seem to translate into lasting engagement. After the initial buzz faded, users seemed to struggle to find consistent, practical uses for the technology.

Finally, tools like Sora exist in a legal gray area, where concerns about copyright and ownership of visual content force companies into a cautious, defensive stance. In practice, this has meant strict prompt controls that prevent references to copyrighted characters or films; blocking outputs that look like living people or intellectual property; and establishing legal safeguards, such as watermarks and metadata tags, on outputs.

Put together, these challenges likely forced OpenAI to redirect its resources elsewhere, especially as competition across the AI industry has intensified.

A symptom of larger issues

But there’s also a pattern that isn’t unique to Sora’s failure to thrive.

Many generative AI programs geared toward creative fields have encountered a common problem: rapid initial adoption, followed by declining sustained engagement.

Many users appear to try image and video generation tools like Midjourney and Stability AI out of curiosity. But if stagnating traffic data is any indication, few creative professionals seem to be integrating them into their regular workflows.

OpenAI and other companies rolled out prompt-based image and video tools with the hope that the efficiency of their product would provide an attractive alternative to the time-consuming process of producing films, photographs and graphic design. Instead of spending a lot of time and money filming a video, you could simply write a prompt, and AI – trained on billions of pieces of human-generated content – would render it for you.

Generative AI’s counter-creative bias

So what happened?

AI-generated outputs of text and images can look impressively real. The bots seem to follow instructions well and appear to give users control.

But there’s an important catch. Under the hood, these systems are built to imitate what they’ve already seen, and that’s especially the case for images and videos. They’ve been trained on massive collections of visual data and rewarded for producing results that closely match the patterns contained in those visuals. That’s why the outputs can look so realistic and recognizable.

Because they’re optimized to produce familiar outputs, they end up suppressing novelty. This, it goes without saying, doesn’t lend itself to true creative breakthroughs. Even the benchmarks used by researchers to evaluate the performance of such systems tend to favor outputs that look “right,” rather than those that truly shatter expectations or take an image to the next level.

Furthermore, these systems don’t learn from a vast repository of data that encompasses the visual world and all human artistic outputs. Instead, the data used to train these models has often been curated to favor certain images and videos that are polished, clear and visually appealing. In effect, the training process teaches models not just what things look like, but what good-looking content is supposed to be.

In a recent paper, I highlighted this problem, which I call the “counter-creative bias” – the tendency of these systems to favor familiarity over meaningful novelty.

Counter-creative bias explains why so many AI-generated images and videos, even when they vary in subject or style, end up sharing a similar look and feel. And I think it explains why so many artists and other creatives don’t seem to be widely adopting these tools. Good creative work involves pushing boundaries, not simply coming up with something that’s passable and palatable.

The limits of prompting

There’s another problem with these tools.

When someone uses AI to generate an image or a video via a prompt, they’re already operating within the constraints of language.

An artist who wishes to use AI has to learn how to write elaborate prompts with the right keywords that compel the system to generate the desired composition, colors, lighting and aesthetics. To create an interesting image or a video, you have to cleverly manipulate words, combine odd concepts and deploy metaphors. It’s an entirely different skill set.

This was obvious from the beginning. When OpenAI launched DALL-E 2 in July 2022, the company demonstrated the range of interesting images by using crafted prompts like “an espresso machine that makes coffee from human souls” or “panda mad scientist mixing sparkling chemicals.”

The sources of creativity in these examples were the human-written prompts themselves, not how the AI generated the image. To make something visually creative, you have to become clever at manipulating words. Users are forced to fiddle with any number of prompt variations to reach a desired or even satisfactory result.

Wading through the slop

There’s a reason Merriam-Webster and the American Dialect Society chose “slop” as their 2025 words of the year: The internet is brimming with viral AI-generated images of world leaders and wide-eyed children, designed to coax engagement but bereft of creative value. The counter-creative bias inherent to these models is reflected in the fact that many people are becoming accustomed to an AI aesthetic characterized by hyper-polished, well-lit, perfectly composed, generically pretty images.

There was a time when AI art was seen as a burgeoning form of conceptual art.

In the summer of 2019, London’s Barbican Centre included AI art in its exhibition, “AI: More Than Human.” In November of that year, the National Museum of China in Beijing showcased 120 AI-integrated artworks, which were viewed by over 1 million people. I championed some of the artists incorporating this new technology into their work.

Back then, creating art with AI involved constant experimentation. The AI these artists used hadn’t been trained on billions of copyrighted, curated images from the internet. Instead, artists trained AI models using their own images and inspiration, while AI was allowed to manipulate pixels free of any language constraints. No universal aesthetic emerged; every AI artist seemed to come up with something unique, and their existing artistic identity shined through the medium, rather than becoming overshadowed by it.

That hopeful period appears to be over. Once pixels had to be rendered through the control of language, I think it hampered its potential as an artistic medium. And now we’re left with a technology that seems best suited for memes, spam, deepfakes and porn.

Ahmed Elgammal does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

– ref. Sora’s downfall signals broader problems with AI’s creative utility – https://theconversation.com/soras-downfall-signals-broader-problems-with-ais-creative-utility-280013

Source: The Conversation – USA (3) – By David Mislin, Assistant Professor of Intellectual Heritage, Temple University

As the United States marks the 250th anniversary of its independence, old questions have returned about who belongs and whose religious practices are truly protected in the country.

At the start of the year, an arson attack significantly damaged the oldest synagogue in Mississippi. Two days later, local officials in Oklahoma rejected a proposal to build a mosque after opponents declared Islam “hostile to our Constitution.” A Texas GOP congressman complained on social media that a Hindu festival was a “third world” practice. These incidents come amid resurgent claims that the United States is a Christian nation.

All this has happened even as President Donald Trump has emphasized a particular idea of religious liberty throughout his second term. In his proclamation for Religious Freedom Day in 2026, he emphasized familiar ideas of Americans’ “God-given right to practice their faith, follow their conscience, and worship their God freely and without fear.” But the statement also seemed to reflect a broader project of lending government support to Christianity. The proclamation linked support for religious liberty with projects to eliminate “anti-Christian bias.”

The tension between embracing religious liberty and the marginalization of other religions in favor of Christianity is not new. As a historian of U.S. religion, I recognize that ideals of religious freedom have long coexisted with religious discrimination or outright bigotry. Importantly, however, history also offers a lesson for the present by showing the important role U.S. Christians have played in combating such bigotry.

Religious freedom in theory

As the founders built a new nation, many emphasized the importance of religious liberty. Shortly after independence in 1776, Thomas Jefferson began drafting the Virginia Statute of Religious Freedom. When enacted a decade later, the law declared that Virginians’ “civil rights” did not depend on their “religious opinions.” Civic participation was not limited to members of particular traditions, and there was no state-funded church. The law was a foundational step to prevent government from discriminating against citizens on the basis of their beliefs.

The Virginia statute provided a template for the First Amendment to the U.S. Constitution, ratified in 1791. The amendment prohibits Congress from enacting laws that favor particular religions or interfering with free religious practice. It represents a key safeguard for personal belief against the power of the federal government.

Legal safeguards did not mean that all religious groups were treated equally, however. In reality, many Americans imagined the new nation to be a Protestant country.

Official and unofficial religious discrimination

Despite protections at the federal level and in some states, including Virginia, state and local governments were not bound by First Amendment protections until the 1920s. Religious discrimination in civic life was commonplace for the nation’s first 100 years.

North Carolina prohibited Catholics from holding public office until the 1830s and Jews from doing so until the 1860s. New Hampshire’s Constitution banned all non-Protestants from holding public office until 1877.

Smaller instances of religious bigotry abounded as well. In some public schools, including in large cities such as Philadelphia, students of all religions were required to read the Bible and sing Protestant hymns. Jewish Americans were often forced to work on their Sabbath and found themselves barred from some hotels and resorts, especially in the second half of the 1800s.

At times, hostility to religious minorities even fueled outright violence. The Philadelphia Bible Riots of 1844 began when the city’s growing Catholic population challenged the use of a Protestant Bible translation in public schools. Anti-Catholic nativists responded with force, and the ensuing conflict left over a dozen people dead.

Toward a ‘Judeo-Christian’ America

Things slowly began to change soon after the nation’s centennial in 1876. As I explore in my work, rising indifference toward religion among many Americans, as well as outright atheism, pushed many Protestant leaders to reevaluate how they treated their Catholic and Jewish neighbors.

Echoing a distrust of atheists that runs deep in U.S. history, these Protestants believed that any religion – even a non-Protestant one – was better for society than no religion at all. This conclusion prompted many Protestants to more fully affirm Catholicism and Judaism. By the early 1900s, it had become common for Protestant ministers to challenge religious bigotry, as one Minnesota clergyman did when he publicly lamented the “false notions and wretched prejudices” held against Jews.

This attitude gained support among the nation’s leaders. President Theodore Roosevelt took a major step by publicly praising Catholics and Jews. He insisted that their religious affiliations did not keep them from being “full Americans.”

After appointing the first Jewish Cabinet member in U.S. history, Roosevelt boasted “in my cabinet at present, there sit side by side Catholic and Protestant, Christian and Jew.”

There was soon a backlash to the growing acceptance of religious diversity. The 1920s witnessed the resurgence of the Ku Klux Klan. Its anti-immigrant campaigns targeted Catholics and Jews with particular force.

Still, the idea that Jewish and Catholic Americans were equal stakeholders in American society took root. By the 1950s, politicians, academics and religious leaders described the United States not as a Protestant country but a “Judeo-Christian” one.

Expanding multiculturalism

The Immigration and Nationality Act of 1965 opened a new chapter for religious pluralism in the United States. The law ended restrictions on immigration from non-European countries. Consequently, the number of practitioners of Hinduism, Buddhism and Islam increased significantly.

Christian groups lobbied strongly for these changes. The National Council of Churches, which represented the country’s major Protestant denominations, lent its significant clout to support the legislation. U.S. Catholic organizations likewise endorsed the 1965 law. For many Catholics, earlier experiences of discrimination and prejudice guided their desire for a more welcoming, inclusive immigration policy.

After 1965, religious diversity became far more visible to ordinary Americans. Earlier generations of immigrants – including Catholics and Jews in the 1800s – typically settled in ethnic enclaves. By contrast, immigrants now settled in diverse suburban communities. Newly arrived Hindus, Buddhists and Muslims often lived next door to Protestant, Catholic and Jewish families.

As in earlier periods, these developments were not entirely harmonious. The 1980s and ’90s witnessed violent attacks against both the institutions and individual practitioners of minority religions. Islamic centers and Buddhist temples were targeted in places ranging from Massachusetts to Minnesota, to Tennessee. The large population of Hindu Americans in northern New Jersey endured a wave of violence against individuals. Despite these instances, scholar of religion Diana L. Eck chronicled in her 2001 book “A New Religious America” how fully the religious nature of the U.S. had been transformed as the nation became characterized by multiculturalism.

While religious minorities have often faced exclusion and hostility, many Americans have long believed that guarantees of religious liberty promise a more inclusive society. In its 250th year, that promise is being tested once again.

David Mislin does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

– ref. America’s founding promise of religious freedom has long coexisted with prejudice, even as many Christians have worked to confront it – https://theconversation.com/americas-founding-promise-of-religious-freedom-has-long-coexisted-with-prejudice-even-as-many-christians-have-worked-to-confront-it-272035