Spain launches radio frequency detectors to hunt penguins and AI

June and July are two months that almost 300,000 students in Spain have marked on their calendars, as they face the University Access Test (PAU). And in some classrooms they also face it with extra surveillance measures for those who copy. We are referring to radio frequency detectors, small devices designed to hunt down hidden devices that some students could use to copy, especially if there is AI involved. What exactly are they? They are not signal inhibitors, but detectors. In this sense, a jammer blocks communications, while these devices only locate them. Héctor Esteban, professor in the area of ​​Signal Theory and Communications at the Polytechnic University of Valencia, counted to El Español that are electromagnetic radiation detectors costing about 10 or 12 euros that track WiFi, Bluetooth and 3G, 4G and 5G networks in a very broad spectrum. When they detect a nearby signal, they warn with a beep or vibration. Stephen himself describes them such as devices as small “as a pen”, that the teacher can carry in his pocket in vibration mode so that the alert goes unnoticed by the rest of the classroom. What are they aiming for? The objective is not so much conventional mobile phones as technology that is difficult to see with the naked eye. The vice-rector of Students of the Complutense University, Rosa de la Fuente, counted that “we are concerned about everything that could be used to commit fraud”, such as micro-earphones and AI glasses generative, since they are devices with which we can easily obtain responses from another person abroad or from any chatbot. Where are they used? The measure does not currently apply throughout Spain. The six public universities of Madrid launched the detectors at the beginning of the month for their more than 42,000 students. Added to these are communities such as Galicia, Murcia, Aragon, Catalonia, the Valencian Community, Andalusia, the Balearic Islands and the Basque Country, among others. The devices are not in all classrooms at the same time. Cristina Moreno, vice-rector of the University of the Balearic Islands assured that the devices rotate through the different locations, but not necessarily during all the tests. What happens if the alert goes off. If the detector vibrates, the exam is “flagged” and the student continues taking the exam as normal. Afterwards, it is the court of headquarters that analyzes the case and decides. However, the sanctions are not identical throughout Spain, because each community sets its own framework. In Madrid, according to counted de la Fuente, three levels are distinguished: a minor fault leaves the exam marked but preserves the grade; a serious one, such as having your cell phone on, can cancel that exam; and a very serious one, such as the active use of a earpiece, can invalidate the entire Selectivity. In other locations the criteria is more severe, as is the case of the Polytechnic of Valencia, where in some cases it is enough for them to find a mobile phone on them, even if it is turned off, to fail the subject. It is not a perfect method. Jesús Alcalde, cybersecurity specialist, counted to The Objective that the scope is limited, because the devices only alert active signals, can give false positives in full classrooms and do not always allow them to prove themselves that there has been copying. Its greatest value, in reality, is as a deterrent. Héctor Esteban illustrated it counting that, in one of the first tests, it was enough to announce that the detector was going to be passed for fifteen students to get up to hand over the cell phone that they should not have brought. Why is it coming just now? The trigger is the emergence of generative AI, which has turned the old problem of copying into something much more complex to deal with. However, the universities themselves recognize that this is a pilot project that they will have to review each course, because at the end of the day the technology for cheating advances as quickly as the tools to detect it. And now what. Radiofrequency covers only part of the problem, and many in the academic field believe that the underlying solution is not in the devices, but in changing the way of evaluating. Stephen himself point towards oral exams, common in countries like Italy, or the in-person defense of papers. Cover image | Ben Mullins and Alberto Ortega (Europa Press) In Xataka | Someone has created the website “is AI profitable anymore?” to answer the question of our time in real time

AI text detectors are terrible. And there are writers winning awards thanks to it

AI does many things well, but writing is not one of them. And detecting if something is written by her is even less so. From the first generations of ChatGPT, to advanced models like ClaudeAI has not been able to write in a human way. The tone, the imperfection, the non-repetition of hackneyed phrases… For the writer, it is relatively easy to identify when a text has been written with AI. Text detectors written by AI do not seem to have it so clear. Writing well has become a “this was probably written by an AI”, to the point that there are AIs detecting some of the great books of Spanish literature as created by AI. And since there is no way to fix this piphostio, there are those who are taking advantage. The mess. One of the news of the week shows the problem we have in identifying whether or not a text is written by AI. Three of the five regional winners of the Commonwalth Short Story Prizeorganized by the British literary magazine Granta, are suspected of having written their fiction works with AI. The accusations come from the readers of the works themselves, as well as from the writers who have participated. It is a contest with a very high reputation in the country, in which different short stories are presented and a prize is awarded to a writer for each of the major regions (Africa, Asia, Canada, Europe…). The prizes amount to up to $6,700 and it is one of the English references in short literature. How do people know? One of the winning works, The Serpent in the Grove, began to raise suspicions. Phrases like “not X, not Y, but Z.” (“Not the neat work of bees nor the harsh sound of a machete against the vine, but a harsh sound, as if the earth swallowed a scream and held it back.”) Strange words without context (“the forest hums at noon”). Some fragments detected by AI tools as 100% created by AI. The author did not make any statements in this regard and, browsing his social media accounts… one finds that they are also generated by AI. In fact, the matter is so murky that an effort even had to be made to prove that the author really existed, and that he was not a character created by AI. “We do not currently use AI systems in our judging process as this is an award for unpublished fiction. Providing an unpublished original work to an AI system would raise serious questions about consent and intellectual property. We also do not use AI to assess stories at any stage of the process. By submitting their stories to the award, authors accept our rules and guidelines for participation. These include confirmation that their submitted work is original. All shortlisted authors have personally declared that no AI was used and, after subsequent consultation, the Foundation has confirmed it”. Undetectable. In the case of Granta, they did not want to use AI systems to recognize whether or not the texts were artificially created. But if it had been done, it would be of no use. Well-known services such as ZeroGPT or Grammarly have significant limitations when it comes to detecting technical texts. In fact, there are already have detected recognized works or fragments of the Bible as AI-generated content. The same thing happens the other way around: there are texts that are 100% generated by AI that the detectors can interpret as 100% human, although it is somewhat more complicated. LLMs (language models like ChatGPT or Claude) don’t actually write, they just make predictions. Its basic mechanism is to calculate, word by word, which is the most likely next given the previous context. This produces coherent, well-structured, grammatically impeccable texts… and flat, very flat and robotic. AI almost always chooses the most predictable option, because that is what it is optimized for, and it has no qualms about repeating patterns in the results it offers to each and every person who uses it. Bad writing as a solution? It is easy to find examples that illustrate ways to circumvent these systems. In the case of yours truly, I am preparing a systematic review on a fairly academic, quite technical topic. The University uses AI detectors, so I usually run the text through it to check the percentage. My surprise lies precisely in how AI detectors penalize correct writing. 100% human texts detected with an 80% probability of having been generated by AI. Solution? Write them but with somewhat more disjointed phrases and without absolute rigor. Be that as it may, the reflection is clear: if not even AI knows how to distinguish a text written with AI… how can humans confirm it at a legal level? In Xataka | We have a problem with AI. Those who were most enthusiastic at the beginning are starting to get tired of it.

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