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AI learns to follow predefined norms through a combination of logic and machine learning

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AI learns to follow predefined norms through a combination of logic and machine learning


Credit: Vienna University of Technology

Artificial intelligence is becoming increasingly versatile—from route planning to text translation, it has long become a standard tool. But it is not enough for AI to simply deliver useful results: it is becoming ever more important that it also complies with legal, ethical, and social norms. But how can such norms be taught to a machine?

At TU Wien, a new approach has now been developed. By combining machine learning and logic, can be trained to follow predefined norms. It is even possible to establish a hierarchy of these norms—declaring some to be more important than others. At IJCAI 2025, an AI conference held this year in Montreal, Canada, this work was recognized with the Distinguished Paper Award.

Trial and error

Teaching AI new abilities sometimes works a bit like teaching tricks to a pet: reward if the task is performed correctly, punishment if the response is wrong. The AI tries out different behaviors and, through trial and error, learns how to maximize its reward. This method is called and plays a key role in AI research.

“One could try to teach AI certain rules by rewarding the agent for following norms. This technique works well in the case of safety constraints,” says Prof. Agata Ciabattoni from the Institute of Logic and Computation at TU Wien. “But this wouldn’t work, for instance, with conditional norms (‘do A under condition B’). If the agent finds a way to earn a reward, it might delay finishing its actual job on purpose, to have more time for scoring easy points.”

Norms as logical formulas

The TU Wien team chose a fundamentally different path, inspired by old philosophical works: norms are still represented as logical formulas, but agents get a punishment when they do not comply with them. For example, “you must not exceed the speed limit” is translated as “if you exceed the speed limit you get a punishment of X.” Most importantly, each norm is treated as an independent objective.

“The artificial agent is given a goal to pursue—for example, to find the best route to a list of destinations. At the same time, we also define additional rules and norms that it must observe along the way,” explains Emery Neufeld, the first author of the paper. “The fact that each norm is treated as a different objective allows us to algorithmically compute the relative weight that we have to assign to these objectives in order to get a good overall result.”

With this technique, it becomes possible to encode even complicated sets of rules—for instance, norms that apply only under certain conditions, or norms that depend on the violation of other norms.

Flexible norms

“The great thing is that when the norms change, the training does not have to start all over again,” says Agata Ciabattoni. “We have a system that learns to comply with norms—but we can then still adjust these norms afterwards, or change their relative importance, declaring one rule to be more important than another.”

In their paper, Ciabattoni and her team were able to show that this technique allows a wide range of norms to be imposed, while the AI continues to pursue its primary goals.

More information:
Preprint paper: Combining MORL with Restraining Bolts to Learn Normative Behaviour

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AI learns to follow predefined norms through a combination of logic and machine learning (2025, September 15)
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Top Design Within Reach Promo Codes for March 2026

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Top Design Within Reach Promo Codes for March 2026


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A Billionaire-Backed Startup Wants to Grow ‘Organ Sacks’ to Replace Animal Testing

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A Billionaire-Backed Startup Wants to Grow ‘Organ Sacks’ to Replace Animal Testing


As the Trump administration phases out the use of animal experimentation across the federal government, a biotech startup has a bold idea for an alternative to animal testing: nonsentient “organ sacks.”

Bay Area-based R3 Bio has been quietly pitching the idea to investors and in industry publications as a way to replace lab animals without the ethical issues that come with living organisms. That’s because these structures would contain all of the typical organs—except a brain, rendering them unable to think or feel pain. The company’s long-term goal, cofounder Alice Gilman says, is to make human versions that could be used as a source of tissues and organs for people who need them.

For Immortal Dragons, a Singapore-based longevity fund that’s invested in R3, the idea of replacement is a core strategy for human longevity. “We think replacement is probably better than repair when it comes to treating diseases or regulating the aging process in the human body,” says CEO Boyang Wang. “If we can create a nonsentient, headless bodyoid for a human being, that will be a great source of organs.”

For now, R3 is aiming to make monkey organ sacks. “The benefit of using models that are more ethical and are exclusively organ systems would be that testing can be meaningfully more scalable,” Gilman says. (R3’s name comes from the philosophy in animal research known as the three R’s—replacement, reduction, and refinement—developed by British scientists William Russell and Rex Burch in 1959 to promote humane experimentation.)

New drugs are often tested in monkeys before they’re given to human participants in clinical trials. For instance, monkeys were critical during the Covid-19 pandemic for testing vaccines and therapeutics. But they’re also an expensive resource, and their numbers are dwindling in the US after China banned the export of nonhuman primates in 2020.

Animal rights activists have long pushed to end research on monkeys, and one of the seven federally funded primate research facilities across the country has signaled it would consider shutting down and transitioning into a sanctuary amid growing pressure. The US Centers for Disease Control and Prevention is also winding down monkey research, part of a bigger trend across the government to reduce reliance on animal testing.

As a result, Gilman says, there aren’t enough research monkeys left in the US to allow for necessary research if another pandemic threat emerges. Enter organ sacks.

Organ sacks would in theory offer advantages over existing organs-on-chips or tissue models, which lack the full complexity of whole organs, including blood vessels.

Gilman says it’s already possible to create mouse organ sacks that lack a brain, though she and cofounder John Schloendorn deny that R3 has made them. (For the record, Gilman doesn’t like the term “brainless” to describe the organ sacks. “It’s not missing anything, because we design it to only have the things we want,” she says.) Gilman and Schloendorn would not say how exactly they plan to create the monkey and human organ sacks, but said they are exploring a combination of stem-cell technology and gene editing.

It’s plausible that organ sacks could be grown from induced pluripotent stem cells, says Paul Knoepfler, a stem cell biologist at the University of California, Davis. These stem cells come from adult skin cells and are reprogrammed to an embryonic-like state. They have the potential to form into any cell or tissue in the body and have been used to create embryo-like structures that resemble the real thing. By editing these stem cells, scientists could disable genes needed for brain development. The resulting embryo could then be incubated until it grows into organized organ structures.



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A Mysterious Numbers Station Is Broadcasting Through the Iran War

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“Tavajoh! Tavajoh! Tavajoh!” a man’s voice announces, before going on to narrate a string of numbers in no apparent order, slowly and rhythmically. After nearly two hours, the calls of “Attention!” in Persian stop, only to resume again hours later.

The broadcast has been playing twice a day on a shortwave frequency since the start of the US-Israel attack on Iran on February 28.

According to Priyom, an organization which tracks and analyses global military and intelligence use of shortwave radio, using established radio-location techniques, the broadcast was first heard as the US bombing of Iran began. It has since played on the 7910 kHz shortwave frequency like clockwork—at 02.00 UTC and again at 18.00 UTC.

Over the weekend, Priyom said it had identified the likely origin of the broadcast. Using multilateration and triangulation techniques, the group traced the signal to a shortwave transmission facility inside a US military base in Böblingen, southwest of Stuttgart, Germany.

The site lies within a restricted training area between Panzer Kaserne and Patch Barracks, with technical operations possibly linked to the US army’s 52nd Strategic Signal Battalion, headquartered nearby.

That identification narrows the field, but it does not reveal who is behind the transmissions or who they are meant for.

The two-hour-long transmission is divided into five to six segments, each lasting up to 20 minutes. Each opens with “Tavajoh!” before shifting into a string of numbers in Persian, sometimes punctuated with an English word or two. Five days into the broadcast, radio jammers were heard attempting to block the frequency. The following day, the transmission shifted to a different frequency—7842 kHz.

Radio communication experts believe the broadcast is likely part of a Cold War–era system known as number stations.

The Return of the Numbers

Number stations are shortwave radio broadcasts that play strings of numbers or codes that sound random—like the one now heard in Iran. “It is an encrypted radio message used by foreign intelligence services, often as part of a complex operation by intelligence agencies and militaries,” says Maris Goldmanis, a Latvian historian and avid numbers stations researcher.

Number stations are most commonly associated with espionage. “For intelligence agencies, it is important to communicate with their spies to gather intelligence,” says John Sipher, a former US intelligence officer who served 28 years in the CIA’s National Clandestine Service. “This is not always possible in person due to political constraints or conflict. This is where number stations come in.”

While the use of number stations can be traced back to the First World War, they gained prominence during the US-Soviet Cold War. As espionage grew more sophisticated, governments used automated voice transmissions of coded numbers to communicate with agents, Goldmanis says. Citing declassified KGB and CIA documents, he adds that number stations were widely used during this period, often as Morse code transmissions and, in many cases, as two-way communications, with agents reporting back using their own shortwave transmitters.

“Nowadays, you have various satellite and encrypted communications technologies,” Sipher says. “But during the Cold War and even before that, governments had to find ways to do this without being noticed, and broadcasting coded messages was one way to communicate with your assets discreetly.”

The apparent randomness of the numbers means they can be understood only with a codebook, Sipher adds. “Nobody can make heads or tails of it or understand what it says unless you have the codebook that can give you hints to decrypt the code,” he says, noting that such systems must be set up and coordinated in advance.

A Signal Without a Sender

While the likely origin of the signal may now be clearer, its purpose and intended recipient remain unknown.

Because the broadcasts are encrypted and designed to be covert, those details may remain unclear for years, Goldmanis says. The structured nature of the transmission—its fixed schedule and consistent use of frequencies—further suggests it is part of a planned operation.



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