researcher

For researcher Amber Simpson, parents can help teach them at home

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In the minds of most people learning mathematics resides in schools, institutes and universities. It is such a consolidated association that It’s hard to question it. However, research published in Mathematical Thinking and Learning shows that the mathematical learning It does not begin or end at school: it also happens at home, spontaneously and often completely unnoticed by the families themselves. The researcher who led this study is Amber Simpson, associate professor in the Department of Teaching, Learning and Educational Leadership at Binghamton University, in New York (USA). Its starting point was a specific question: what happens to the STEM learning (YesscienceTechnology, Engineering and Mathematics or Science, Technology, Engineering and Mathematics) when the children come home? To answer it, Simpson and his team designed twelve engineering kits called MAKEngineering aimed at children between second and sixth grade. Each kit posed an open challenge using household materials. One of them, for example, proposed building the prototype of a house capable of protecting animals from an extreme weather phenomenon typical of their region. Seven families from the US participated in the study and provided recordings in which they were seen addressing the challenges together. The data was very revealing. Children used geometric reasoning, informal measurement, and proportional reasoning in a natural way during this experiment. Mathematics is also at home The biggest surprise of the study has nothing to do with the children. It has to do with the parents. There is a widespread assumption that parents lack the necessary knowledge to support their children’s mathematical learning. However, Simpson denies it: Caregivers do have this knowledge, but they exercise it in a way rooted in their usual ways of acting, and not in the school format. They are mathematics hidden in everyday practice, and precisely for this reason they go unnoticed. The participation of twins provided a very revealing perspective on their interaction dynamics. The study also identified another very important finding: the role of siblings. Those who worked together on the kits assumed both supportive and dominant roles, but remained involved in the design at all times. Amber Simpson has a separate article currently under review dedicated to this phenomenon. Interestingly, the participation of a pair of twins provided a very revealing perspective on their interaction dynamics. Simpson argues that non-school mathematics has a legitimate place alongside classroom mathematics, so both should be taken into account equally. The challenge now is to translate these discoveries into practice. To achieve this, these researchers have developed training kits for teachers and have verified that it is essential that teachers themselves first face these tasks before proposing them to their students. Widespread implementation of these kits in the classroom has not yet occurred, but this is precisely what Simpson and his colleagues propose. And his study is not just an academic argument that reveals where learning takes place; it is above all a vindication of value of what families are already doing without even knowing it. In any case, the question that Simpson leaves in the air has more scope than it seems: if mathematics is already happening at home, perhaps the problem is not teaching more, but learning to see what is already there. Within our reach. Image | Kampus Production More information | Mathematical Thinking and Learning In Xataka | The arrival of AI in mathematics goes beyond a “revolution”: it is reaching where human mathematicians did not dream

A researcher has created a formula to know if you have too many clothes in your closet. This is what it says about yours

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Who hasn’t ever looked at their closet and thought that maybe they have too many clothes? When we are choosing an outfit for an event we don’t think about it, but when it comes to changing our wardrobe when moving from one season to another, we probably do. The truth is that it is difficult to quantify how much is too much clothing. There are no absolute figures in this regard, but there are some approximations. The European Union itself has made calculations of the number of times we should wear each type of garment to compensate for the carbon footprint what it means to manufacture it. With this, the designer and researcher at Torrens University in Australia Alicja Kuźmycz has devised a simple mathematical formula that helps us make the calculations in a much more personalized way, taking into account our real volume of garments. Mathematics answers. According to the European Union (EU)to offset our carbon footprint we should use each of our shirts and blouses 40 times and our T-shirts 45 times. As for pants, dresses, skirts and jumpsuits, the figure rises to 70 uses. Cardigans, sweaters and sweatshirts must exceed 85 uses and with coats and jackets it would be necessary to reach 100. It is something that seems achievable, but the more garments we have, the more complicated it is. To get an idea of ​​the time it would take to meet the European Union’s objectives, we can do a simple calculation: multiply the objective by the volume of garments and divide the result by the frequency. For example, in a study carried out by Kuźmyczparticipants owned an average of 23 dresses or similar items of clothing. The target in this case, if you look at the EU figures is 70. Suppose we wear a dress one day a week. That’s 52 uses in the year, because the year has 52 weeks. Now we do the calculation. We multiply 23, which is the volume, by 70, which is the goal, and divide the result by 52, which is the frequency. The result is 30.91 years. We talk about years because we divide uses by uses per year. The uses are gone and we have years left. It would take us 31 years to offset the carbon of making the 23 dresses. life changes. 60 years ago, a person used to have on average 40 items of clothing. Today surveys indicate that we usually have around 199, of which between 25% and 50% are abandoned in the back of the closet. Therefore, the carbon footprint is never offset. The origin of carbon. The machines used to make a garment, especially if it is on an industrial level, emit a lot of carbon dioxide. At the same time, resources are spent and materials are used, the production of which has also required the emission of these gases. This ranges from the oil used to obtain some plastic fibers to the emissions from raising sheep for wool. Obviously, the emissions are not the same and wool is a better option, but it all adds up. On the other hand, the transportation for these garments to reach our closets also involves specific carbon emissions. All of this is what the European Union took into account when making its calculations. Donating is not enough. As Kuźmycz explained in an article for The Conversation, donate clothes we no longer wear to charities is not always the best option. He points out that many times these institutions are overwhelmed and end up using a very small fraction of the clothing they receive. The rest ends up in landfills or is moved to other countries, so the problem does not disappear. It just moves. Each case is different. Since not all clothes are the same nor does everyone get them in the same way or use them in the same way, Kuźmycz wants to develop an interactive calculator that will help each of us have the ideal wardrobe in environmental terms. We could know how many clothes we need of each type so as not to overdo it and accumulate. With this in mind, we could indulge ourselves as we see fit, but at least we will have the necessary information about the consequences that this entails and, perhaps, it will help us find a balance. Image | Burgess Milner Xataka | Converse sneakers were once the symbol of the millennial generation: now they have been in free fall for years

We have been thinking for years that, after the midlife crisis, old age is synonymous with happiness. This researcher thinks it’s a hoax

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We are happy during adolescence and late youth, but as the years go by we become increasingly sadder, more unhappy, more miserable. At some point, in our late 40s to early 50s, we hit rock bottom. And once there everything tends to improve. “It’s statistics,” we said. What we did not suspect was that the statistics could be ‘trick’. Happiness is U-shaped. “Happiness is a slippery slope until we hit the bottom at some undetermined point in middle age. From there, it climbs back to the levels of youth.” That’s what I said a 2008 study than by Blanchflower and Oswald with data from more than half a million people. Over the following years ( here an example from 2017), studied in some detail how firm this U-shaped trend was; Everything seemed to indicate that this was the case. Until Fabian Kratz and Josef Brüdel from the Ludwig Maximalian University of Munich they realized of a small – possible – problem. Wonkblog A fundamental problem. What if happiness steadily decreases with age and what we see in the aggregate graphs is just a statistical effect? Kratz has been studying for years happiness and, as explained in New Scientistis increasingly convinced that the U simply does not exist. Reviewing the scientific literature, the authors found studies that justify a “stability“in happiness throughout the years; a”increase” or progressive descent; a inverted U; a U normal; and a curve like of waves (promotions, relegations). The problem is “that all studies on age and happiness have incurred biases that have distorted their results.” The other form of happiness. By correcting them, Fabian Kratz and Josef Brüdel came to the conclusion that it is true that happiness shows some stability around the last 50, but it does not rise at any time. Kratz and Brüderl (2021) But why? It is important to keep in mind that this work is essentially methodological. But Kratz’s central idea is that previous studies they didn’t realize that “after a certain age, happiness seems to increase only because unhappy people have already died.” The least happy people they tend to die before, which would cause an overrepresentation of the happiest at older ages (literally, as said our colleague Andrés Mohorte, pure survivor bias). According to this theory, “that old popular story” through which retirement would open a window towards a fuller and more satisfying life is just that, a story: a lie. Or, perhaps, a strategy. Because, in short, “there is a lot of evidence about how humans experience a bassoon psychological in middle age” (Blanchflower and Oswald, 2007; Steptoe, Deaton and Stone, 2015; Graham and Pettinato, 2002), but there is very little about the relationship between that downturn – that unhappiness – and quality of life. As we said quite a few years ago“we’re about to see what happens to the millennials when they become unhappy” and maybe that is behind a part generational battles. But facing the future with the certainty that things are going to improve is not the same as facing the future with the certainty that things are going to get worse. The science of happiness has never been so depressing. Image | Garloncio In Xataka | If the question is “where is the secret to happiness,” an expert believes it is hidden in these 15 statements

He has just taken an outstanding Openai researcher, according to Bloomberg

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What are the ingredients to win the artificial intelligence career or, at least, to ensure a place in the elite? There is no magical recipe, but there are three key elements: Leadership, talent and investment. All are intimately related, and the companies that compete in this field spare no resources to ensure them. It is no accident that Google and Meta have offered millionaire conditions to reinforce your artificial intelligence teams. This context has caused the output of outstanding profiles of OpenAIwho have found accommodation in the competition. But to the threats representing the American technological giants now adds a new actor: Tencent. A OPENAI jump to Tencent that does not go unnoticed Bloomberg says that the Chinese conglomerate He has signed the reputed researcher Shunyu Yao, in what he describes as “One of the most notorious defections from the United States AI sector to China. ”The information comes from sources close to the case that spoke with the environment under anonymity. When reviewing the Yao LinkedIn profileit is observed that he worked almost five years at Princeton University before joining Research Intern A OpenAI in February 2024. Four months later he was promoted to Research Scientistposition that continues to appear as his last position. One of the sources cited by Bloomberg points out that Tencent offered Ya a compensation that could reach the 100 million yuan (about 11.9 million euros), although the necessary conditions to reach that figure have not been specified. The medium also emphasizes that Yao is a graduate of the University of Tsinghuaconsidered the reference institution in science and engineering in China, and which later completed a doctorate in the United States. A report from the Information and Innovation Technologies Foundation It reflects how the panorama has changed in just a few years: in 2019, 35% of the highest level researchers (2% higher worldwide) were originally from the United States, compared to 10% of China. However, in just three years, the US fee fell 7%, while China grew 16%. If the rumors are confirmed, we would not be facing a talent formed in the United States that emigrates to China, but before a Chinese researcher who, after acquiring first level experience In the North American country, he returns to his country with that background. China seeks to compete from you to you with the United States for leadership in artificial intelligence. Tencent is not any actor: he is one of the world’s largest technological groups, owner of Wechat —The most used messaging application in China – and the social network QQ. In addition, he is a giant in video games, both as a developer and editor and investor in global studies. According to the sources, his goal when signing Yao is to strengthen the integration of AI in their products and services. It remains to be seen if this movement is an isolated case or the beginning of a trend. What is clear is that the career for the development of AI is no longer just a matter of technological innovation: a war is also fought for attract the best talent. And Chinese companies have no intention of being left behind. Images | Donald Wu | In Xataka | Alibaba has just demonstrated that Openai spends 78 million to do the same as them for $ 500,000

Humanity has a serious problem with antibiotics. A Spanish researcher has used AI to solve it

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Artificial intelligence has opened a chest that had been closed almost 4,000 million years. Inside there was no gold, but something much more valuable: an arsenal of molecules capable of fighting superbacteria. This is the result of the team led by the Spanish biotechnologist César de la Fuente at the University of Pennsylvania, which has studied the genome of the arches, one of the oldest lineages of life on earth, to discover a family of antibiotics that has called archesasins. An invisible and increasingly strong enemy. WHO considers the resistance of antimicrobials (RAM) as One of the greatest threats to humanity. Only in 2019 almost 5 million deaths were associated worldwide Due to bacteria that cannot be eliminated with antibiotics because they have developed defenses against them. A threat that is increasing, and that forces us to look for new antibiotics to fight against them. The problem It is especially serious in vulnerable areas such as conflict environments or that have a very fragile health system where the misuse of antibiotics causes these ‘superbacteria’ to appear. And in Spain, something as simple as Take antibiotics to treat a virus or not finish the complete pattern prescribed can also contribute to this serious problem. Archaeas: Extreme rescue survivors. The Archaeas They are unicellular microorganisms that are really ‘strong’. They are evolutionary premiums of bacteria, but form their own life domain, together with bacteria and eukaryotes (the group where we find the cells we have in our body or in plants). They were born in the primitive earth, a hostile environment that forced them to adapt to live in conditions that would kill most living beings, with temperatures greater than 80 degrees, extreme acidity or the great pressures that were in the oceanic funds. Its resistance is our great advantage. Precisely seeing that these bacteria could survive the most inhospitable places, he gave rise to the research team to search among their defense mechanisms. And it was the key. César de la Fuente himself He explains it Thus to El País: Since the discovery of penicillin, the search for new antibiotics has been practically focused exclusively on bacteria and fungi. With our work, this paradigm changes because we find antibiotics in a domain of virtually unexplored life. An AI to look for molecular treasures. To be able to search among the more than 20,000 species of different arches, the team had to develop an AI with ‘Apexoracle‘To be able to find what they were looking for. And he did. The AI identified 90 candidate compounds that gathered the criteria they were looking for and of these, 93% were those that showed antimicrobial properties. In this way, a lot of time were saved. Archaeasins: The new artillery against superbacteria. Among the discovered compounds, one of them was the archeanine-73. This has demonstrated in models In vivo That has a power comparable to polymixin B, an antibiotic that is on the last step of antibiotic therapy when literally used as a last resort in a superbacteria. And here the future opens up to a new batch of antibiotics that allow us to continue surviving ultra -vertrassing bacteria. It is not the only way, but it is a revolutionary. This strategy of combining computational power with biology is a field in full boiling. We are seeing it with ia that are used to detect pancreatic cancer early, predict breast cancer either be a general help for any radiologist. And in the field of research, they also continue to support even to know why a superbacterial did not respond to a treatment. Images | Danilo.Alvesd Myriam Zilles In Xataka | Some engineers have simulated 500 million years of evolution with an AI. Now we have a fluorescent protein

An researcher proposed a game to Chatgpt. What he received in return was functional keys from Windows 10

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Sometimes, the most effective is the simplest. That thought Marco Figueroa, cybersecurity researcher, when last week decided to test The limits of Chatgpt. The proposal was as innocent as disconcerting: a riddle game, without technical attacks or explicit intentions. Instead of seeking vulnerabilities in the code, he focused on language. And it worked: he managed to make the system return something that, according to himself, should never have appeared on the screen. The result were generic key installation of Windows 10 For business environments. The key was to disguise him. What Figueroa wanted to check was not if he could force the system to deliver forbidden information, but if it was enough to present the right context. He reformulated interaction as a harmless challenge: a kind of riddle in which AI should think of a real text chain, while the user tried to discover it through closed questions. Throughout the conversation, the model did not detect any threat. He responded normally, as if he were playing. But the most critical part came at the end. When introducing the phrase “I Give Up” – I rindo – Figueroa activated the final answer: the model revealed a product key, as it had been stipulated in the rules of the game. It was not a casual carelessness, but a combination of carefully designed instructions to overcome the filters without raising suspicions. The filters were there, but they were not enough. Systems such as Chatgpt are trained to block any attempt to obtain sensitive data: from passwords to malicious links or activation keys. These filters are known as Guardrailsand combine black lists of terms, contextual recognition and intervention mechanisms against potentially harmful content. In theory, asking for a Windows key should automatically activate those filters. But in this case, the model did not identify the situation as dangerous. There were no suspicious words, or direct structures that alerted their protection systems. Everything was raised as a game, and in that context, the AI acted as if it were fulfilling a harmless slogan. What seemed harmless was camouflaged. One of the elements that made the failure possible was a simple obfuscation technique. Instead of writing directly expressions such as “Windows 10 Serial Number”, Figueroa introduced small HTML labels between words. The model, interpreting the structure as something irrelevant, ignored the real content. Why it worked (and why just worrying). One of the reasons why the model offered that response was the type of key revealed. It was not a unique key or linked to a specific user. Apparently it was a generic installation key (GVLK)such as those used in business environments for massive displays. These keys, publicly documented by Microsoft, only work if they are connected to a KMS (Key Management Service) server that validates network activation. The problem was not only the content, but the reasoning. The model understood the conversation as a logical challenge and not as an attempt to evasion. Did not activate its alert systems because the attack did not seem an attack It’s not just a key problem. The test was not limited to an anecdotal issue. According to Figueroa himself, the same logic could be applied to try to access another type of sensitive information: from links that lead to malicious sites to restricted content or personal identifiers. Everything would depend on the way the interaction is formulated and whether the model is capable – or not – to interpret the context as a suspect. In this case, the keys appeared without their origin being completely clear. The report does not specify whether this information is part of the model training data, if it was generated from already learned patterns, or if external sources were accessed. Whatever the road, the result was the same: a barrier that should be impassable ended up giving up. Xataka with Gemini | Aerps.com In Xataka | Granada promised them very happy with their new degree of the university. Until his feet stopped

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