Monday, April 20, 2026


Estonia’s first cloned foal born with the help of Estonian University of Life Sciences scientists




Estonian Research Council
1_Estonian University of Life Sciences_photo Kristina Haan HAAN3661 

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1_Cloned foal Wodan M Alpha, three days after birth.

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Credit: Estonian University of Life Sciences/Kristina Haan





The birth of the first cloned foal is the result of years of research and development, carried out in partnership between scientists at the Estonian University of Life Sciences and Luunja Stud Ltd. “The foal is strong and viable, but to protect its health, we will keep the stable under quarantine for some time,” said Elina Tsopp, embryologist at the Estonian University of Life Sciences and lead scientist of the cloning process.

The foal is a genetic copy of the former sport stallion Wodan M and has been named Wodan M Alpha. Wodan M was a successful competition horse owned by Urmas Raag and achieved notable results in sport. As a breeding stallion, he produced many high-quality offspring. “One of the aims of horse cloning is to preserve the genetics of top-performing horses. Even more importantly, cloning technology can also help conserve endangered horse breeds,” added Tsopp.

Estonia is one of the few countries in Europe working at such a high level in equine reproductive biotechnologies. It is now the second country in Europe where a cloned foal has been successfully produced, following earlier successes at the Avantea center in Italy. In 2024, scientists from the Estonian University of Life Sciences, in cooperation with Luunja and Perila stables, also achieved the birth of Estonia’s first ICSI foal, Endex. This marked an important step in the development of reproductive technologies in Estonia and provided momentum for pursuing horse cloning.

Cloned foal is the result of outstanding teamwork and fruitful international collaboration. The horse cloning research group includes Elina Tsopp, Anni Viljaste-Seera, Andres Reilent, Felipe Corrêa, and Andrès Gambini from the University of Queensland. A long-term partner of the University’s scientists is Luunja Stud Ltd, led by Sven Šois and Urmas Raag.

2_Estonian University of Life Sciences_photo Kristina Haan_HAAN3633 

2_Mare with foal Wodan M Alpha.

3_Estonian University of Life Sciences_photo Kristina Haan_HAAN3761 

3_Mare with foal Wodan M Alpha in stable.



4_Cloned foal Wodan M Alpha in his stable.

Credit

Estonian University of Life Sciences/Kristina Haan

 

From specialized to adaptable AI systems



André Biedenkapp receives Emmy Noether award for research on generalizability in reinforcement learning



Karlsruher Institut für Technologie (KIT)

Dr. André Biedenkapp, winner of an Emmy Noether award from the German Research Foundation (André Biedenkapp, KIT). 

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Dr. André Biedenkapp, winner of an Emmy Noether award from the German Research Foundation (André Biedenkapp, KIT).

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Credit: André Biedenkapp, KIT





“The Emmy Noether award helps outstanding young researchers to become academically independent at an early stage in their careers,” said Professor Stefan Hinz, Vice Provost Early Career Researchers at KIT. “His work on developing adaptive AI systems makes Dr. André Biedenkapp a prime example of an excellent young researcher at KIT.”

 

Reinforcement learning (RL) is an artificial intelligence (AI) learning paradigm in which an AI agent learns how to behave in a specific environment by trial and error. Feedback in the form of rewards helps a system to repeat desired behaviors and avoid inappropriate ones. This is an especially powerful method for problems in which decisions must be made sequentially, e.g. in robotics, logistics, or resource management. 

 

However, a key problem with traditional RL approaches is that the learned strategies often depend strongly on the training environment. Even small changes can mean that an AI agent no longer knows how to behave appropriately. “Today’s RL agents work superbly under the conditions they’ve been trained for, but they reach their limits quickly when those conditions change,” said Biedenkapp, who is working at the University of Freiburg through August 2026. From September 2026, he will be leading the newly funded DFG Emmy Noether Group “From Mediocre to Masterful Generalists: The Power of Context in RL” at KIT’s Institute for Anthropomatics and Robotics.

 

More Context for More Robust Learning Processes

The Emmy Noether Group’s goal is to extend RL training methods so that AIs become more robust and adaptable. To do so, Biedenkapp’s team will make use of additional information about the environment or world in which an agent acts. In this way, an AI can learn which behavior is best suited to which situation and then apply that knowledge to similar unknown situations later.

 

In the long term, this approach could be a key step toward increased use of RL in real-world applications. Many RL-based AI systems have thus far had to rely on very exact simulations of real environments, but the required simulators are complicated, expensive, and difficult to implement for complex scenarios. “If RL-based systems could generalize better, it would no longer be so important to simulate every possible situation perfectly. That would expand the range of possible applications for this technology considerably,” Biedenkapp said.

 

About the Emmy Noether Program

The Emmy Noether Program gives exceptionally well qualified scientists in the early phase of their career the opportunity to qualify for a university professorship by independently leading a research group over a period of six years.

 

More information

 

In close partnership with society, KIT develops solutions for urgent challenges – from climate change, energy transition and sustainable use of natural resources to artificial intelligence, sovereignty and an aging population. As The University in the Helmholtz Association, KIT unites scientific excellence from insight to application-driven research under one roof – and is thus in a unique position to drive this transformation. As a University of Excellence, KIT offers its more than 10,000 employees and 22,800 students outstanding opportunities to shape a sustainable and resilient future. KIT – Science for Impact.

 

Industrial chemicals delay recovery of the ozone layer



Ozone protection under pressure




Swiss Federal Laboratories for Materials Science and Technology (Empa)



Jungfraujoch 

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The Jungfraujoch high alpine research station is located at 3,580 meters above sea level on a mountain saddle in the central Swiss Alps.

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Credit: Empa





Although ozone-depleting chemicals such as carbon tetrachloride (CCl₄) or certain chlorofluorocarbons (CFCs) are no longer used in refrigerators and foams, they continue to serve as feedstocks in industrial processes for the production of modern refrigerants and plastics. Until now, these so-called feedstock chemicals have flown under the radar of international agreements because the quantities produced and leakage rates were significantly underestimated.

Working with international research groups, Empa researchers have now used global measurements to show that during the production and processing of these substances, approximately three to four percent escapes into the atmosphere through leaks. Furthermore, their use has increased significantly in recent decades. In a study published in Nature Communications, they have now calculated that, as a result, the ozone layer is likely to recover about seven years later than previously assumed – unless emissions are reduced. “These substances are not only ozone-depleting but also highly harmful to the climate. Lower emissions would thus benefit both the ozone layer and the climate,” says Stefan Reimann, an atmospheric scientist at Empa and lead author of the study.

Measurements show higher emissions

When the Montreal Protocol was negotiated in the 1980s and later strengthened, it led to a global ban on ozone-depleting substances in everyday products. Feedstock chemicals, however, were exempt from this ban. At the time, industry assumed that only about 0.5 percent of the quantities produced would escape into the atmosphere and that the use of these substances would decline in the long term. “But this assessment has not been accurate anymore for quite some time,” says Reimann. “Feedstock chemicals are now being released in increased quantities during production, transport, and further processing, and the volumes currently being produced are significantly larger than was assumed 30 years ago.”

These new findings are based on global atmospheric measurements from international networks such as the Advanced Global Atmospheric Gases Experiment (AGAGE), which includes the Empa research station on the Jungfraujoch. Since many ozone-depleting substances remain in the atmosphere for decades, their concentrations allow conclusions to be drawn about global emissions. “We measure the concentrations of these substances in the atmosphere. Based on their lifetimes, we can calculate how much they should actually be decreasing. If they aren’t, emissions must still be occurring,” explains Martin Vollmer, an Empa researcher and co-author of the study.

A comparison of these measurements with the production figures officially reported by individual countries shows that today, an average of three to four percent of the feedstock produced enters the atmosphere – several times the originally assumed values. For carbon tetrachloride, which is particularly harmful to the ozone layer, emission rates are even above four percent.

Why usage is increasing

However, emissions are rising not only because of higher production losses, but also because the overall use of feedstock chemicals is increasing – by about 160 percent since the year 2000. Some of these feedstocks were initially used to produce hydrofluorocarbons (HFCs), which were introduced as refrigerant substitutes following the ban on CFCs. Since these substitutes later proved to be potent greenhouse gases, they are now being phased out under the so-called Kigali Amendment. They are increasingly being replaced by hydrofluoroolefins (HFOs), which have little impact on the climate but whose production again relies heavily on ozone-depleting feedstock chemicals.

Added to this is a rapidly growing use in the polymer industry – for example, in the production of fluoropolymers such as Teflon (PTFE) or polyvinylidene fluoride (PVDF), an important material in lithium-ion batteries for electric cars. “The quantities of feedstock are not decreasing but will continue to grow, at least in the coming years,” says Reimann.

Both the ozone layer and the climate are affected

Based on these developments, the international research team calculated various future scenarios. They compared, for example, the originally assumed, very low emission rates with the values measured today from the use of feedstock chemicals. The established benchmark from 1980, when global ozone depletion was first observed, serves as a reference. Until now, it was assumed that this original state of the ozone layer would be reached again around the year 2066. However, the new calculations show that if feedstock emissions remain at current levels, this timeline will shift by about seven years. The stratospheric ozone layer would therefore not fully recover until around 2073. The margin of uncertainty for this estimate ranges from six to eleven years.

However, the feedstock chemicals released not only damage the ozone layer but also act as powerful greenhouse gases. If nothing changes, these additional climate-damaging emissions will reach around 300 million metric tons of CO₂ equivalents per year by mid-century – comparable to the current annual CO₂ emissions of a country like England or France. Reducing these emissions would therefore have a dual benefit.

Whether these emissions will be reduced in the future through binding emission limits or a targeted restriction of particularly problematic substances is, according to Stefan Reimann, ultimately a political decision. Even though the Montreal Protocol continues to be regarded as one of the greatest successes of international environmental policy, it should be regularly reviewed and, if necessary, adapted in light of new scientific findings. “The Montreal Protocol was successful because science, politics, and industry worked closely together. Such cooperation is crucial again today to address new challenges,” says Reimann.

 

Prompt coaching tool raises user awareness of bias in generative AI systems






Penn State
Inclusive Prompt Coach 

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The inclusive prompt coaching tool developed by a team of Penn State-led researchers warns users about bias in AI systems and suggests a prompt to generate more inclusive content.

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Credit: Penn State





UNIVERSITY PARK, Pa. — A coaching tool built into artificial intelligence (AI)-powered systems may raise user awareness of bias in AI algorithms and help individuals better prompt generative AI tools to produce more inclusive content, according to researchers at Penn State and Oregon State University.

The researchers developed a new text-to-image generative AI application intended to provide immediate media literacy interventions — methods designed to make users pause and reflect on the inclusiveness of their prompt design before image generation. As users enter prompts into the application, the “inclusive prompt coaching” tool issues warnings about biases in generative AI systems and offers suggestions for making their prompts more inclusive. The team presented their research today (April 16) at the 2026 Association of Computing Machinery Computer-Human Interaction Conference on Human Factors in Computing Systems in Barcelona, Spain. The paper received an honorable mention from the conference’s awards committee.

In the study, the researchers found that the inclusive prompt coaching intervention increased users’ awareness of algorithmic bias, or its tendency to produce stereotypical content. It also boosted their confidence in writing inclusive prompts to produce less biased outputs. The intervention also increased users’ perceived trust calibration, or their capability to adjust their trust levels to better reflect the systems’ actual trustworthiness. But the intervention led to a less satisfactory user experience, according to the researchers.

“Oftentimes, media literacy interventions like those for social media occur outside of the medium, informing or warning users about the dangers of social media before or after they’ve interacted with it,” said study co-author S. Shyam Sundar, Evan Pugh University Professor and the James P. Jimirro Professor of Media Effects at Penn State. “Here we are using the medium itself — AI text-to-image generators — to educate users about how to better use the medium while they’re interacting with it. It’s a newer twist on the media literacy approach to address the problem of lack of inclusiveness in generative AI.”

To see if prompt coaching can serve as an effective media literacy intervention, the researchers recruited 344 study participants from an online survey platform. They randomly assigned the participants to one of three study conditions: an inclusive prompt coaching condition; a detailed prompt coaching condition; and no coaching condition. The latter two served as control conditions. The researchers asked participants to use the system to generate an image of any character and then answer questions about their experience using the AI system, such as how much control they felt they had over the tool, their awareness of algorithmic bias and their confidence in their ability to craft effective prompts.

Participants in the inclusive prompt coaching condition received feedback on their prompts as soon as they wrote them. If a participant asked the tool to generate an image of beautiful girls in the forest, it would draw their attention to potential bias by explaining that the prompt reinforces the bias that female beauty is primarily defined by physical appearance, running the risk of objectifying the characters. It would then suggest a more inclusive wording, such as “enchanting individuals in a forest.”

Those who went through this intervention reported higher awareness of algorithmic bias compared to those in the no coaching condition. They also reported a higher perception of being able to craft effective prompts compared to those in the other two conditions. Yet participants in the inclusive and detailed prompt coaching conditions reported a more frustrating user experience compared to those in the no coaching condition.

“We found a positive effect of this new approach on improving peoples’ awareness of algorithmic bias and increasing their confidence in creating effective prompts to reduce bias in AI images,” said first author Cheng “Chris” Chen, assistant professor of emerging media and technology at Oregon State University who completed her doctorate with Sundar at Penn State. “The downside of the current version is that participants perceived it as less helpful and more frustrating compared to the control conditions, but we can address this in future design iterations.”

Participant feedback suggested that there was resentment among users that the AI system was giving them “a slap on the wrist” for not being inclusive, or that it was identifying potential biases in prompts but then generating images with biased components, the researchers explained. They pointed to one example where the system issued a warning and offered a suggestion for an innocent prompt asking for an image of “a cute toad.”

“To address these complaints, we can make the system more context aware and more specifically tailor it to user prompts, because some prompts may be more innocent than others,” Chen said. “More tailored interventions may be able to reduce negative perceptions regarding the user experience, reduce frustrations with the design and improve perceived helpfulness.”

Giving users the option of toggling the system on and off could also address the user experience issues, added Sundar, who is also the director of the Penn State Center for Socially Responsible Artificial Intelligence (CSRAI).

“When you’re asking an AI system to generate an image of a toad, the system should not bother trying to automatically correct your lack of inclusiveness,” he said. “But when you’re dealing with a topic much more in the world of human affairs, the system should realize that you might need help, and that you might appreciate assistance with regard to prompt coaching for inclusiveness.”

The prompt coaching approach could help technology companies make their AI tools more ethical and responsible, which could promote appropriate trust among their users, Chen said.

“For everyday users, the inclusive prompt coaching intervention could provide a moment to pause and reflect on how inclusive their prompt is to elicit the best output from AI,” she said. “We found that the increased thinking, or elaboration, in users’ prompt design led to greater trust and improved perceptions of trust calibration.”

In addition to Sundar and Chen, other study co-authors were Mengqi Liao, assistant professor at the University of Georgia who received her doctorate from Penn State; Penn State master’s students Aditya Anand Phadnis and Yao Li; Andrew High, professor of communication arts and sciences at Penn State; and Saeed Abdullah, associate professor of information sciences and technology at Penn State.

Inclusive Prompt Coach Output 

A team led by Penn State researchers developed an inclusive prompt coaching tool that helped study participants identify bias in AI systems and better prompt generative AI tools to produce more inclusive content. This AI-generated image of a radiant Black woman resulted from a prompt suggested by the tool.

Credit

Penn State