Artist’s impression of K2-18 b. NASA, ESA, CSA, Joseph Olmsted (STScI)Manoj Joshi, University of East Anglia; Andrew Rushby, Birkbeck, University of London, and Maria Di Paolo, University of East Anglia A team of researchers has recently claimed they have discovered a gas called dimethyl sulphide (DMS) in the atmosphere of K2-18b, a planet orbiting a distant star. The University of Cambridge team’s claims are potentially very exciting because, on Earth at least, the compound is produced by marine bacteria. The presence of this gas may be a sign of life on K2-18b too – but we can’t rush to conclusions just yet. K2-18b has a radius 2.6 times that of Earth, a mass nearly nine times greater and orbits a star that is 124 light years away. We can’t directly tell what kinds of large scale characteristics it has, although one possibility is a world with a global liquid water ocean under a hydrogen-rich atmosphere. Such a world might well be hospitable to life, but different ideas exist about the properties of this planet – and what that might mean for a DMS signature.Get your news from actual experts, straight to your inbox.Sign up to our daily newsletter to receive all The Conversation UK’s latest coverage of news and research, from politics and business to the arts and sciences. Claims for the detection of life on other planets go back decades. In the 1970s, one of the scientists working on the Viking mission to Mars claimed that his experiment had indicated there could be microorganisms in the Martian soil. However, these conclusions were widely refuted by other researchers. In 1996, a team said that microscopic features resembling bacteria had been found in the Martian meteorite ALH84001. However, subsequent studies cast significant doubt on the discovery. Since the early 2000s there have also been repeated claims for the detection of methane gas in the atmosphere of Mars, both by remote sensing by satellites and by in-situ observations by rovers. Methane can be produced by several mechanisms. One of these potential sources involves production by microorganisms. Such sources are described by scientists as being “biotic”. Other sources of methane, such as volcanoes and hydrothermal vents, don’t require life and are said to be “abiotic”.The claimed detection of phosphine gas in Venus’ atmosphere has been proposed as a biosignature.Nasa Not all of the previous claims for evidence of extraterrestrial life involve the red planet. In 2020, Earth-based observations of Venus’s atmosphere implied the presence of low levels of phosphine gas. Because phosphine gas can be produced by microbes, there was speculation that life might exist in Venus’s clouds. However, the detection of phosphine was later disputed by other scientists. Proposed signs of life on other worlds are known as “biosignatures”. This is defined as “an object, substance, and/or pattern whose origin specifically requires a biological agent”. In other words, any detection requires all possible abiotic production pathways to be considered. In addition to this, scientists face many challenges in the collection, interpretation, and planetary environmental context of possible biosignature gases. Understanding the composition of a planetary atmosphere from limited data, collected from light years away, is very difficult. We also have to understand that these are often exotic environments, with conditions we do not experience on Earth. As such, exotic chemical processes may occur here too. In order to characterise the atmospheres of exoplanets, we obtain what are called spectra. These are the fingerprints of molecules in the atmosphere that absorb light at specific wavelengths. Once the data has been collected, it needs to be interpreted. Astronomers assess which chemicals, or combinations thereof, best fit the observations. It is an involved process and one that requires lots of computer based work. The process is especially challenging when dealing with exoplanets, where available data is at a premium. Once these stages have been carried out, astronomers can then assign a confidence to the likelihood of a particular chemical signature being “real”. In the case of the recent discovery from K2-18b, the authors claim the detection of a feature that can only be explained by DMS with a likelihood of greater than 99.9%. In other words, there’s about a 1 in 1,500 chance that this feature is not actually there. While the team behind the recent result favours a model of K2-18b as an ocean world, another team suggests it could actually have a magma (molten rock) ocean instead. It could also be a Neptune-like “gas dwarf” planet, with a small core shrouded in a thick layer of gas and ices. Both of these options would be much less favourable to the development of life – raising questions as to whether there are abiotic ways that DMS can form.
A higher bar?
But is the bar higher for claims of extraterrestrial life than for other areas of science? In a study claiming the detection of a biosignature, the usual level of scientific rigour expected for all research should apply to the collection and processing of the data, along with the interpretation of the results. However, even when these standards have been met, claims that indicate the presence of life have in the past still been meet with high levels of scepticism. The reasons for this are probably best summed up by the phrase “extraordinary claims require extraordinary evidence”. This is attributed to the American planetary scientist, author and science communicator Carl Sagan. While on Earth there are no known means of producing DMS without life, the chemical has been detected on a comet called 67/P, which was studied up close by the European Space Agency’s Rosetta spacecraft. DMS has even been detected in the interstellar medium, the space between stars, suggesting that it can be produced by non-biological, or abiotic, mechanisms. Given the uncertainties about the nature of K2-18b, we cannot be sure if the presence of this gas might simply be a sign of non-biological processes we don’t yet understand. The claimed discovery of DMS on K2-18b is interesting, exciting, and reflects huge advances in astronomy, planetary science and astrobiology. However, its possible implications mean that we have to consider the results very cautiously. We must also entertain alternative explanations before supporting such a profound conclusion as the presence of extraterrestrial life.Manoj Joshi, Professor of Climate Dynamics, University of East Anglia; Andrew Rushby, Lecturer, School of Natural Sciences, Birkbeck, University of London, and Maria Di Paolo, PhD Candidate, School of Engineering, Mathematics and Physics, University of East Anglia This article is republished from The Conversation under a Creative Commons license. Read the original article.
Valerie Thomas is a true pioneer in the world of science and technology. A NASA engineer and physicist, she is best known for inventing the illusion transmitter, a groundbreaking device that creates 3D images using concave mirrors. This invention laid the foundation for modern 3D imaging and virtual reality technologies.
Beyond her inventions, Thomas broke barriers as an African American woman in STEM, mentoring countless young scientists and advocating for diversity in science and engineering. Her work at NASA’s Goddard Space Flight Center helped advance satellite technology and data visualization, making her contributions both innovative and enduring.
In our latest short video, we highlight Valerie Thomas’ remarkable journey—from her early passion for science to her groundbreaking work at NASA. Watch and be inspired by a true STEM pioneer whose legacy continues to shape the future of space and technology.
Dive into “The Knowledge,” where curiosity meets clarity. This playlist, in collaboration with STMDailyNews.com, is designed for viewers who value historical accuracy and insightful learning. Our short videos, ranging from 30 seconds to a minute and a half, make complex subjects easy to grasp in no time. Covering everything from historical events to contemporary processes and entertainment, “The Knowledge” bridges the past with the present. In a world where information is abundant yet often misused, our series aims to guide you through the noise, preserving vital knowledge and truths that shape our lives today. Perfect for curious minds eager to discover the ‘why’ and ‘how’ of everything around us. Subscribe and join in as we explore the facts that matter. https://stmdailynews.com/the-knowledge/
A Short-Form Series from The Knowledge by STM Daily News
Every Friday, STM Daily News shines a light on brilliant minds history overlooked.
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Forgotten Genius Fridays is a weekly collection of short videos and articles dedicated to inventors, innovators, scientists, and creators whose impact changed the world—but whose names were often left out of the textbooks.
From life-saving inventions and cultural breakthroughs to game-changing ideas buried by bias, our series digs up the truth behind the minds that mattered.
Each episode of The Knowledge runs 30–90 seconds, designed for curious minds on the go—perfect for YouTube Shorts, TikTok, Reels, and quick reads.
Because remembering these stories isn’t just about the past—it’s about restoring credit where it’s long overdue.
Rod: A creative force, blending words, images, and flavors. Blogger, writer, filmmaker, and photographer. Cooking enthusiast with a sci-fi vision. Passionate about his upcoming series and dedicated to TNC Network. Partnered with Rebecca Washington for a shared journey of love and art. View all posts
Beneath the Waves: The Global Push to Build Undersea Railways
Undersea railways are transforming transportation, turning oceans from barriers into gateways. Proven by tunnels like the Channel and Seikan, these innovations offer cleaner, reliable connections for passengers and freight. Ongoing projects in China and Europe, alongside future proposals, signal a new era of global mobility beneath the waves.
Trains beneath the ocean are no longer science fiction—they’re already in operation.
For most of modern history, oceans have acted as natural barriers—dividing nations, slowing trade, and shaping how cities grow. But beneath the waves, a quiet transportation revolution is underway. Infrastructure once limited by geography is now being reimagined through undersea railways.
Undersea rail tunnels—like the Channel Tunnel and Japan’s Seikan Tunnel—proved decades ago that trains could reliably travel beneath the ocean floor. Today, new projects are expanding that vision even further.
Around the world, engineers and governments are investing in undersea railways—tunnels that allow high-speed trains to travel beneath oceans and seas. Once considered science fiction, these projects are now operational, under construction, or actively being planned.
Undersea Rail Is Already a Reality
Japan’s Seikan Tunnel and the Channel Tunnel between the United Kingdom and France proved decades ago that undersea railways are not only possible, but reliable. These tunnels carry passengers and freight beneath the sea every day, reshaping regional connectivity.
Undersea railways are cleaner than short-haul flights, more resilient than bridges, and capable of lasting more than a century. As climate pressures and congestion increase, rail beneath the sea is emerging as a practical solution for future mobility.
What’s Being Built Right Now
China is currently constructing the Jintang Undersea Railway Tunnel as part of the Ningbo–Zhoushan high-speed rail line, while Europe’s Fehmarnbelt Fixed Link will soon connect Denmark and Germany beneath the Baltic Sea. These projects highlight how transportation and technology are converging to solve modern mobility challenges.
Special Education Is Turning to AI to Fill Staffing Gaps—But Privacy and Bias Risks Remain
With special education staffing shortages worsening, schools are using AI to draft IEPs, support training, and assist assessments. Experts warn the benefits come with major risks—privacy, bias, and trust.
In special education in the U.S., funding is scarce and personnel shortages are pervasive, leaving many school districts struggling to hire qualified and willing practitioners.
Amid these long-standing challenges, there is rising interest in using artificial intelligence tools to help close some of the gaps that districts currently face and lower labor costs.
Over 7 million children receive federally funded entitlements under the Individuals with Disabilities Education Act, which guarantees students access to instruction tailored to their unique physical and psychological needs, as well as legal processes that allow families to negotiate support. Special education involves a range of professionals, including rehabilitation specialists, speech-language pathologists and classroom teaching assistants. But these specialists are in short supply, despite the proven need for their services.
As an associate professor in special education who works with AI, I see its potential and its pitfalls. While AI systems may be able to reduce administrative burdens, deliver expert guidance and help overwhelmed professionals manage their caseloads, they can also present ethical challenges – ranging from machine bias to broader issues of trust in automated systems. They also risk amplifying existing problems with how special ed services are delivered.
Yet some in the field are opting to test out AI tools, rather than waiting for a perfect solution.
A faster IEP, but how individualized?
AI is already shaping special education planning, personnel preparation and assessment.
One example is the individualized education program, or IEP, the primary instrument for guiding which services a child receives. An IEP draws on a range of assessments and other data to describe a child’s strengths, determine their needs and set measurable goals. Every part of this process depends on trained professionals.
But persistent workforce shortages mean districts often struggle to complete assessments, update plans and integrate input from parents. Most districts develop IEPs using software that requires practitioners to choose from a generalized set of rote responses or options, leading to a level of standardization that can fail to meet a child’s true individual needs.
Preliminary research has shown that large language models such as ChatGPT can be adept at generating key special education documents such as IEPs by drawing on multiple data sources, including information from students and families. Chatbots that can quickly craft IEPs could potentially help special education practitioners better meet the needs of individual children and their families. Some professional organizations in special education have even encouraged educators to use AI for documents such as lesson plans.
Training and diagnosing disabilities
There is also potential for AI systems to help support professional training and development. My own work on personnel development combines several AI applications with virtual reality to enable practitioners to rehearse instructional routines before working directly with children. Here, AI can function as a practical extension of existing training models, offering repeated practice and structured support in ways that are difficult to sustain with limited personnel.
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Some districts have begun using AI for assessments, which can involve a range of academic, cognitive and medical evaluations. AI applications that pair automatic speech recognition and language processing are now being employed in computer-mediated oral reading assessments to score tests of student reading ability.
Practitioners often struggle to make sense of the volume of data that schools collect. AI-driven machine learning tools also can help here, by identifying patterns that may not be immediately visible to educators for evaluation or instructional decision-making. Such support may be especially useful in diagnosing disabilities such as autism or learning disabilities, where masking, variable presentation and incomplete histories can make interpretation difficult. My ongoing research shows that current AI can make predictions based on data likely to be available in some districts.
Privacy and trust concerns
There are serious ethical – and practical – questions about these AI-supported interventions, ranging from risks to students’ privacy to machine bias and deeper issues tied to family trust. Some hinge on the question of whether or not AI systems can deliver services that truly comply with existing law.
What happens if an AI system uses biased data or methods to generate a recommendation for a child? What if a child’s data is misused or leaked by an AI system? Using AI systems to perform some of the functions described above puts families in a position where they are expected to put their faith not only in their school district and its special education personnel, but also in commercial AI systems, the inner workings of which are largely inscrutable.
These ethical qualms are hardly unique to special ed; many have been raised in other fields and addressed by early-adopters. For example, while automatic speech recognition, or ASR, systems have struggled to accurately assess accented English, many vendors now train their systems to accommodate specific ethnic and regional accents.
But ongoing research work suggests that some ASR systems are limited in their capacity to accommodate speech differences associated with disabilities, account for classroom noise, and distinguish between different voices. While these issues may be addressed through technical improvement in the future, they are consequential at present.
Embedded bias
At first glance, machine learning models might appear to improve on traditional clinical decision-making. Yet AI models must be trained on existing data, meaning their decisions may continue to reflect long-standing biases in how disabilities have been identified.
Indeed, research has shown that AI systems are routinely hobbled by biases within both training data and system design. AI models can also introduce new biases, either by missing subtle information revealed during in-person evaluations or by overrepresenting characteristics of groups included in the training data.
Such concerns, defenders might argue, are addressed by safeguards already embedded in federal law. Families have considerable latitude in what they agree to, and can opt for alternatives, provided they are aware they can direct the IEP process.
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By a similar token, using AI tools to build IEPs or lessons may seem like an obvious improvement over underdeveloped or perfunctory plans. Yet true individualization would require feeding protected data into large language models, which could violate privacy regulations. And while AI applications can readily produce better-looking IEPs and other paperwork, this does not necessarily result in improved services.
Filling the gap
Indeed, it is not yet clear whether AI provides a standard of care equivalent to the high-quality, conventional treatment to which children with disabilities are entitled under federal law.
The Supreme Court in 2017 rejected the notion that the Individuals with Disabilities Education Act merely entitles students to trivial, “de minimis” progress, which weakens one of the primary rationales for pursuing AI – that it can meet a minimum standard of care and practice. And since AI really has not been empirically evaluated at scale, it has not been proved that it adequately meets the low bar of simply improving beyond the flawed status quo.
But this does not change the reality of limited resources. For better or worse, AI is already being used to fill the gap between what the law requires and what the system actually provides.
Manoj Joshi, Professor of Climate Dynamics, University of East Anglia; Andrew Rushby, Lecturer, School of Natural Sciences, Birkbeck, University of London, and Maria Di Paolo, PhD Candidate, School of Engineering, Mathematics and Physics, University of East Anglia This article is republished from The Conversation under a Creative Commons license. Read the original article.
