learn

“China sets the pace in technology, costs and development times. We have to learn from them”

by

Company with problems, company that looks to China. Not so much to sell more cars in a very complicated market (that too), much more to see how to learn from them and get greater performance from their products. Survive by achieving wider profit margins by producing cheaper and faster. For Nissan, China holds the key. “We have to learn” “China shows us the future of the industry in terms of technology, cost competitiveness and development times. We have to learn from China and export its technical knowledge” This is what Iván Espinosa, CEO of Nissan, has expressed in statements Nikkei Asia. In an interview with the Japanese media, Espinosa has made it clear that Nissan needs to copy China if it wants to survive. The Japanese automaker is going through a very bad economic time and to get out of the hole it wants to look at its neighbor. https://www.xataka.com/movilidad/nissan-leaf-opiniones-primera-toma-contacto-fotos What’s wrong with Nissan? Nissan is going through one of the worst financial and reputational moments in its history. At the end of 2024, the company accepted that it had entered a stalemate from which it would be difficult to get out. Sales were declining, the US market (with its tariffs on Japanese cars) It got complicated, at home they didn’t get back on their feet and in China they were missing. Solution: lay off 9,000 employees. Those days, after a few days of rumors (and everything indicates that with the Japanese Government putting pressure), Nissan and Honda announced an agreement to unite their paths. The idea, it seemed, was that the second company would take over Nissan and take it under its umbrella. That idea was dissolved just a few months later.. We now know that Honda gave, for the first time in its history, losses in 2025 and that its strategy has focused on canceling its electrical projects. In March of last year, the Mexican Iván Espinosa took control of Nissan and has focused its future proposal on a new launch plan and the objective of recovering part of its prestige. Last year, the company continued to lose sales worldwide but the 13% drop in Japan is especially worrying. Reduce times. For Espinosa, one of Nissan’s big problems is in the development and production times of its models. Right now, he explains, the development of a car from when it is drawn on paper and given the go-ahead until it reaches the street is 55 months. Espinosa wants to reduce this to about 30 months. According to their calculations, these long developments prevent them from getting the economic performance they need from their cars and that is why they have proposed that the next Nissan Skyline, the return of a legendary model of the company, has set a deadline for its development of 26 months, they point out in Nikkei Asia. China has demonstrated an ability to develop, modify and produce in record time. Renault has also gone to China to learn how they work there and they presume that The Renault Twingo was developed in 20 months. From Chery they already made it clear that its high capacity to develop and produce in record time is key when it comes to prevailing over Western competitors. More and more notices. These statements by Espinosa demonstrate that traditional firms are doing everything possible to quickly adapt to China’s way of working. Some consultants have already pointed out Japanese brands that their obsession with perfection hinders them when it comes to producing faster and cheaper, which has been taken advantage of by Tesla and Chinese companies. Toyota also sent a similar message a few weeks ago. The company has detected that it is losing money because products with aesthetic defects are sometimes discarded even though these parts are fully functional and are never seen by vehicle customers. This weighs on their production costs but also on the time it takes to produce the car. More cars, more prestige. Account Nikkei Asia that Espinosa’s project involves launching new cars on the market that update the company’s range of products but, above all, it will be based on the search for its own identity with a clear intention of recovering the lost prestige. In fact, in recent weeks it has been rumored that Nissan could bring the latest Z to Europe (now only sold in Japan and the United States so as not to penalize the company’s emissions count) with the aim of attracting the public to the dealership and positioning it as a halo product. On the horizon is also the new Skyline and a future GT-R that has gone through all possible phases when it comes to defining it and moving it forward, from project it as an electric sports car to launch it again with a combustion engine. “We will provide more details in the future,” Espinosa said on this topic. The CEO of Nissan has even referred to the fact that “some of my predecessors only talked about finances all the time”, in a clear message that not everything can be done to save costs if you want to maintain the attractive image of the product. A move that diluted Nissan’s imagethat Stellantis ballast and an idea that Akio Toyoda, CEO of Toyota, has also rejected. Photo | nissan In Xataka | “We will not survive”: Toyota wants to add the turbo to match the pace of the Chinese brands

It is no longer enough to count fingers to know if an image is made with AI. Now you have to learn technical drawing

by

Detecting images generated by artificial intelligence has become a game of cat and mouse. And the worst thing is that it is going to get worse. For a time, we all began to focus on the hands and in the number of fingers that the AI ​​represented in the images of people through the diffusion mechanisms of the models. A few years ago it was obvious to see when an image was created by AI. Now, with image models and video increasingly precise, the task is much more complex. The good news is that there are still ways to detect if an image has been generated by AI, although seeing the pace at which the models advance, this may soon change again. Detecting them is less intuitive than before, but just pay attention to geometry, shadows and perspective. Basically, technical drawing. Who is behind this idea. Hany Farid, a specialist at the University of California at Berkeley and one of the world’s leading experts in image forensics, has spent more than two decades dedicated to determining whether a photo or video has been manipulated. Santiago Lyon, former director of photography for the Associated Press who now works in digital security at Adobe, describes Farid in a Science report as “a kind of dean of digital forensics”, precisely because he has been at it for so long. Farid helped found this discipline more than 20 years ago, and says that AI is the biggest challenge he has faced. Farid exemplifies his method with this image. If we draw a line towards the horizon between the tiles and the skirting boards, we see that the lines do not converge at a single point, which tells us that the image is generated by AI It’s hard to know what’s true and what’s not.. We are losing the ability to trust what we see. The combination of generative AI, capable of creating images almost indistinguishable from reality, and a warm regulation on social networks It makes the hoaxes amplify, making it increasingly difficult to know if what we are seeing is real or not. And in many cases, we don’t even care. Farid speaks directly of a “global war for truth”, with consequences for people, institutions and democracies. In a TED talk He said that he believes that the percentage of fake images on the Internet is close to 50%. It is no longer useful to focus on pixels. One of the first techniques Farid developed was based on the “noise” left by real cameras. An authentic photo is born from light hitting an electronic sensor; An AI image, on the other hand, emerges from a statistical process that converts random noise into an image consistent with the text requested. This very different origin left traces detectable at the pixel level. The problem is that generators have learned to imitate even those imperfections, sensor noise and lens artifacts. As explains Science report, many of Farid’s pioneering methods based on statistical relationships between pixels “no longer work well, if at all,” because AI images are created from scratch rather than edited over a previous photo. technical drawing. AI, says Farid, “doesn’t know physics, doesn’t know geometry, and does all kinds of atrocities.” And that’s where technical drawing comes in. According to Farid, these are the three fronts that we must examine: Vanishing points. In the real world, parallel lines (train tracks, floor tiles, the sides of a wall) converge toward a single point as they move further apart. It is a principle that artists have known for centuries, but that AI ignores because it does not understand three-dimensional space. If those lines don’t meet at a single point, the scene is physically impossible. Shades. The Sun is so far away that its rays reach the Earth practically parallel. That means that the lines connecting each object to the shadow it casts should also intersect at a point consistent with the position of the light. In many AI-generated images, those lines don’t even come close to crossing. Highlights. The same principle applies to mirrors, as lines connecting one point on an object to its reflection should converge at a vanishing point. When they don’t, the image is given away. The same thing happens in this image. If we draw a line that passes through both the vertices of each cube and the vertices of its projected shadow, we see that they do not converge at a single point either. Track accumulation. No technique is infallible on its own, and Farid insist in that the method consists of accumulating clues, as in an investigation. In his TED talk he exemplified this with an image made with AI of several soldiers looking forward. In it he detected the suspicious pattern in the noise, the absence of a coherent vanishing point on the walls and shadows that did not intersect. Three anomalies that gave clues that the image was not real. The underlying reason why this approach stands up better over time is that AI companies are not looking to fool forensic experts like Farid, but rather the average user, since we are at a much lower bar. As he himself says“the visual system forgives all kinds of nonsense in photos because it doesn’t care.” In this image, if we draw a line from a point in the figure to the same point reflected in the mirror, we see that the lines do not converge at a single point either. Doubts and limits. Not everyone in the field shares the same optimism. Some researchers reaffirm that each detection technique has a very short “useful life”, sometimes a few months, because AI improves very quickly. In fact, the famous mistakes on six-fingered hands disappeared in a flash. Farid, however, is skeptical that AI will ever master complex real-world physics, like an explosion, because simulating it is devilishly difficult and companies have little incentive to go that far. Still, he acknowledges that receives a dozen emails every day from journalists … Read more

learn the technology behind AI

by

We bring you a collection of eight advanced AI courses created by Stanford University. These are video courses that have been released on YouTube so that any user can access them and learn the technology behind the artificial intelligence. These are very technical courses, in which you will learn the entire framework of mathematics and technology that makes AI work today. You will not need to spend hundreds of euros on these trainings because you will have them for free. Free advanced AI video courses CS221 – Artificial Intelligence: How AI finds solutions, analyzes problems and makes decisions: A starter course uploaded to YouTube. It has 60 videos of different lengths in which you are introduced to machine learning and the inner workings of artificial intelligence. Almost all the videos are from 2021, with some from 2023. Link. CS229 – Machine Learning (Andrew Ng): A series of 21 videos, each over an hour long, that introduce you to machine learning and statistical pattern recognition. They are theoretical courses on this technology before it exploded, in 2018. Link. CS230 – Deep Learning (Andrew Ng): Another course with 10 videos lasting more than an hour. It is from 2018 and they explain how convolutional networks, RNN, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization and much more work. Link. CS229M – Machine Learning Theory: A course in which you will learn the mathematics that explains why machine learning really works. With this you will learn the essence of how it works with 20 videos of almost an hour and a half each. Link. CS224N – NLP with deep learning: A course of 23 videos of around an hour in which it is explained in depth how AI reads, understands and generates language. These are the fundamentals of all artificial intelligence tools. Link. CS224U – Natural Language Understanding: A course made up of 63 videos in which you will learn the mechanics that artificial intelligence follows not only to learn words, but also what each one means. Link. CS231n – Deep learning for computer vision: A course in which you will learn the theory behind how AI is capable of interpreting images and videos in contexts such as medicine or even autonomous vehicle driving. Composed of 18 videos of more than an hour each. Link. CS234 – Reinforcement Learning: You will learn how AI agents learn to make decisions, measure results, and adjust their actions. There are 16 videos of more than 1 hour each. Link. In Xataka Basics | Free courses ChatGPT, Claude, Gemini, Copilot: 53 courses to get started or take advantage of the main artificial intelligences

“If you have time, use it to learn more about AI”

by

The arrival of artificial intelligence to our life It is no longer the stuff of science fiction movies. The young people of generation Z are going to have to deal with the different forms of integration of this technology in the world of work. One of the main pieces of advice from millionaire and investor Mark Cuban to generation Z, who are beginning to join the labor marketis understanding that mastering this technology is not an option, it’s a necessity. Mark Cuban’s advice. Millionaire investor Mark Cuban, co-founder of the video portal Broadcast.com and former owner of the NBA team Dallas Mavericks, assured last year at the SXSW conference that throughout his life he had tried his luck in all kinds of businesses. ELON MUSK VS JEFF BEZOS: STAR WARS However, the millionaire acknowledged that, if he were to become a young man with an entire career ahead of him, he would dedicate all his time to one thing: “If I were 16, 18, 20 or 21 years old from today, I would dedicate every minute of the day to learning about AI. Even while I was sleeping, I would listen to podcasts about AI.” AI is the future and they must prepare for it. In his speech, Cuban emphasized that AI not only will automate tasksbut will also create new employment opportunities and transform existing industries. So the young people who are now entering the labor market are going to have to deal with AI-based tools or, what is even better, create them. According to Cuban, the key is not to be afraid of AI, but to embrace it and learn to work with it. This involves acquiring skills in areas such as machine learning, data analysis, and algorithm development. “Those people who dedicated the necessary time will achieve it,” insisted the businessman and investor, whose fortune it is estimated about $6 billion according to Forbes. Skills beyond AI. The millionaire highlighted that the domain of AI is not limited to computer engineers or data scientists who train AI models. In fact, businessmen consider that those who can combine AI skills with knowledge in other fields, such as marketing, finance or design, will be the more in demand in the future since the main task of generation Z will be to integrate AI into business processes. The businessman stressed that the jobs of the future They will require some type of AI-related skill. Therefore, investing time and effort in learning about AI is an investment in your professional future. The next billionaire will be thanks to AI. Since speaking at SXSW last year, Cuban has continued to insist on numerous occasions on the importance of AI. Among other things, has predicted that the first billionaire (“trillionaire” in English) will be thanks to AI and that it could perfectly well be a guy in a basement. A foundation on which to undertake. Until last year, Cuban was one of the protagonists of the television program Shark Tank from the North American ABC, in which entrepreneurs must captivate investors to put their money into their ideas. That’s why Cuban not only invites young people to learn everything they can about AI to guarantee a good job. The millionaire assures that knowing how AI works can open new avenues of entrepreneurship for young people and generate new successful companies, in the same way that today’s great technological millionaires did. a decade ago. “AI is never the solution. It is a tool,” Cuban highlighted in his speech during the conference for entrepreneurs. In Xataka | “Humans will not be necessary for most things”: Bill Gates does not believe that doctors and teachers have a future In Xataka | AI promises to boost the productivity of companies: the problem is that we are not measuring it well Image | Wikimedia Commons (Gage Skidmore) A previous version of this topic was published in 2025. We have updated the topic with new statements from Mark Cuban

Science explains why your brain prefers paper to keyboard to learn

by

In an era dominated by mechanical keyboards, touch screens and cutting-edge tablets, the ancestral gesture of slide a pen over the paper seems like an anachronism reserved for people who haven’t updated. And this is a reality in areas of study such as, for example, universities, where it is rare to see someone studying by hand. But the reality is that, to be much more productive while studying, it may be best to put the laptop aside and start writing. The ‘magic’ of the pen. Here science, in its different disciplines, has concluded that taking notes by hand significantly improves retention and comprehension compared to actively using digital devices. And it’s not a question of romanticism, it’s a question of neural processing. A transcription effect. One of the pillars of this evidence is in a study published in 2014 which pointed out that students who use laptops to take notes become authentic transcribers of what the teacher says. And we have reached the point where many people can write faster than teachers speak and become “transcription machines” without processing the information. and stay with what is most important. I have even seen that even jokes end up being copied. On the contrary, who writes by hand You can’t write it all down. This requires you to engage in active cognitive processing: you must listen, digest, synthesize, and rephrase the idea in your own words. This “desirable difficulty” generates a much deeper encoding in memory that lasts even a week after the study. Better paper. Beyond the transcription effect, neuroscience has confirmed that the benefit is not only strategic, but also physical. Here, a study from 2021 published by the University of Tokyo demonstrated using electroencephalograms that handwriting activates brain areas critical for memory, language and fine movement. This is why, when using a pen, it has been seen that the hippocampus is significantly activated, which is essential for memory and spatial coding of information. But it does not stop there, since up to 25% more neuronal connectivity has been detected in complex tasks when the analog method is used. This explains why students in highly demanding careers tend to perform better cognitively when they opt for neural methods such as engineering. Less distractions. Beyond neurons, paper offers a competitive advantage in the study environment, since it is a closed system. This is very important because a tablet or laptop It is also a tool with open doors to notificationssocial networks and messaging apps that can be a temptation when it comes to interrupting the study quite easily. In addition, it facilitates word recognition and visual memorization, something vital for competitive exams or high-level exams such as a competition. And while well-designed digital notes may be superior for quickly remembering a single fact, paper wins by a landslide in conceptual understanding. Images | yanalya in Magnific In Xataka | The 2-7-30 method has become one of the fashionable systems to study faster. Science has doubts

“It takes two years to learn to speak and sixty to learn to be silent”

by

Whether or not you are part of their legion of userssomething must be recognized about X, the old Twitter: it has become a gigantic social laboratory. Also definitive proof that it is often less difficult for people to open their mouths (or type) than to think beforehand about what we are going to use them for. It doesn’t matter that it’s the last game of the League, the war in Iran, a video of kittens or issues as sensitive as euthanasia: There will always be someone willing to take out their phone and share their opinion, even if that opinion has just been formed. Hence in this world verbose Ernest Hemingway resonates strongly: “It takes two years to learn to speak and 60 to learn to be silent.” Speak and be silent. The history of Philosophy (thus, with capital letters) is full of good ideas… and suggestive phrases of uncertain origin and dubious attribution. We have told it more times. A quick Google search arrives to find alleged statements by Marcus Aurelius, Da Vinci or Marie Curie (among a very long list of thinkers) whose authorship is impossible to confirm. Something similar happens with the sentence that concerns us today. We have been putting the phrase “It takes two years to learn to speak and sixty to learn to be silent” on Hemingway’s lips for decades when in reality it is impossible to know if he ever uttered it. In 2019 Quote Researcher tried to confirm it and came to three conclusions. First, it dates back to at least 1909, when Hemingway was still a boy from Illinois. Second, that it has been associated (with variants) with other intellectuals, including Mark Twain either Lydia Allen DeVilbiss. Third, it is very difficult to go beyond the two previous conclusions. The value of each word. In view of all the above, we might ask ourselves why pay attention to a proverb of diffuse authorship. The answer is simple. Perhaps we cannot confirm if it came from Hemmingway’s lips (or pen), but it certainly connects with the style of a novelist who was characterized by concise sentences and maximum economy of language. In the works of Hemingway every word counts. And that is also a valuable lesson if we think about the fact that humanity (or at least a large part of it) has never had it so easy when it comes to expressing its opinions and participating in public debate. The torrent of public opinion is so powerful that it has even overflowed and has been carried forward the 140 characters of Twitter. In defense of silence. If Hemingway’s supposed phrase has been captivating us for more than a century, it is not only because of its ironic point. To a large extent it also connects with an idea that has permeated philosophy since the time of Pythagoras, to whom another similar phrase is attributed: “Listen, you will be wise”. People express themselves naturally. It is part of our elemental baggage, which we develop during the first years of life along with other skills such as walking. The complicated thing, in fact, is to do the opposite: embrace silence. In silence you think, reflect and listen, tasks that often require active effort. “It takes sixty years to learn to be silent,” reminds us Hemingway sarcastically, implying that silence is a complex virtue that we must work on and takes a lifetime to master. Is it that important? Yes. Educated in a world in which from a very young age we are instilled that ‘he who remains silent grants’ it is easy to forget it, but silence is sometimes an art. To begin with, it requires self-control. It is not always easy to remain silent. As they comment our colleagues TrendsIt also requires discipline, tolerance and a certain dose of humility and generosity. Against polarization. In exchange, silence offers us other things. It leaves us more room for reflection, to form more informed opinions and, above all, to measure our words and avoid regrets. In the age of networks, the debate held from anonymity and with society increasingly polarizedalso helps to ask certain questions: Can I contribute something to the conversation? Am I sure of what I’m going to say or will I just contribute to generating noise? What repercussions might what I say have on others? The virtues of silence and contemplation have been defended by many thinkers throughout history, from Pythagoras to the Stoics (including Epictetus either Marcus Aurelius) to the great humanists of the Renaissance. Even neuroscience has endorsed the advantages of giving yourself some time before opening your mouth. I already said it Aristotle himself in another equally ingenious phrase: “The Wise Man never says everything he thinks, but he always thinks everything he says.” Images | Wikipedia 1 and 2 Via | Trends In Xataka | “A place of joy with pain”: the phrase that summarizes the Aztec philosophy to be happier in this life

15 official courses with certification to learn and squeeze your AI

by

We bring you a collection of 15 free Claude courses created by Anthropicthe company that created Claude. They are self-paced courses, free, and provide an official Anthropic certification when you complete them. You won’t need a subscription to Claude to do them either. We are talking about a series of courses for anyone who wants to learn how to use the artificial intelligence of this company. The only way to take the courses is to enter the platform skilljar.comsince it is the one in which they are uploaded. It is a free platform, so you don’t have to pay anything either. Claude’s free courses by Anthropic Here you have the list with official courses created by Anthropic to master the use of its artificial intelligence. For each one we will give you a description and the link to access it. Claude 101: The entry point for new Claude users. It helps you with the first steps in the tool, explaining functions such as projects, artifacts, the use of skills or the connection with third-party tools, in addition to the research mode. Link: anthropic.skilljar.com/claude-101. Claude Code in Action: A complete training on how to use Claude Code for software development. It covers the architecture of AI code assistants, context management, creation of custom commands, and integration with MCP or Github servers. Link: anthropic.skilljar.com/claude-code-in-action. AI Fluency: Framework & Foundations: A foundation course on AI literacy and collaboration, developed with academic experts. It teaches you the practical skills to interact with AI effectively, efficiently, ethically and safely. Link: anthropic.skilljar.com/ai-fluency-framework-foundations. AI Fluency for educators: Another AI literacy course, in this case aimed at teachers and those responsible for instructional design. It helps you use AI in teaching practices, including subject design, learning outcomes, and materials and tasks for students. It requires having completed the base course that we set before. Link: anthropic.skilljar.com/ai-fluency-for-educators. AI Fluency for Students: Adaptation of the AI ​​Fluency course for university students. It teaches you how to use AI as a learning tool, for planning your professional career and for academic success, always in a responsible manner. Also requires the base course. Link: anthropic.skilljar.com/ai-fluency-for-students. Teaching AI Fluency: Course for teachers and instructors who want to teach and evaluate AI literacy in face-to-face or digital training environments. Link: anthropic.skilljar.com/teaching-ai-fluency. AI Fluency for nonprofits: Course developed in collaboration with GivingTuesday, designed for non-profit organizations. It has modules on AI research, writing, data analysis, privacy, and scaling the use of AI. Link: anthropic.skilljar.com/ai-fluency-for-nonprofits. Introduction to Claude Cowork: An introductory course to Claude Cowork, the functionality that allows AI to directly use your computer as an agent. It covers plugins, scheduled tasks, document workflows, and more. Link: anthropic.skilljar.com/introduction-to-claude-cowork. Introduction to agent skills: Teaches you how to create, configure, and share skills in Claude Code, which are markdown instructions that Claude automatically applies depending on the task. Link: anthropic.skilljar.com/introduction-to-agent-skills. Introduction to subagents: A course on using subagents in Claude Code to delegate tasks to isolated assistants. These subagents allow you to manage context efficiently by working in separate context windows, returning only the relevant information. Link: anthropic.skilljar.com/introduction-to-subagents. Building with the Claude API: A comprehensive technical course for developers who want to integrate Claude into their applications by using the Claide API. It covers everything from basic operations to advanced prompting techniques, tool integration or construction of RAG systems. Link: anthropic.skilljar.com/claude-with-the-anthropic-api. Introduction to Model Context Protocol: An introductory course to the MCP protocol for developers who want to create their own MCP servers and clients using Python. Link: anthropic.skilljar.com/introduction-to-model-context-protocol. Model Context Protocol: Advanced Topics: Continuation of the MCP course from the previous point, aimed at experienced developers. Explore other advanced patterns such as sampling, real-time notification systems, file system access control, and more. Link: anthropic.skilljar.com/model-context-protocol-advanced-topics. Claude with Amazon Bedrock: A technical course for integrating and deploying Claude models through Amazon Bedrock, covering everything from basic API usage to RAG, autonomous agents, Claude Code, and more. Requires knowledge of Python and AWS. Link: anthropic.skilljar.com/claude-in-amazon-bedrock. Claude with Google Cloud’s Vertex AI: The equivalent of the previous course, but aimed at Google Cloud. Covers the full spectrum of Claude integration through Vertex AI. Requires Python, experience with Google Cloud Platform and handling of JSON. Link: anthropic.skilljar.com/claude-with-google-vertex. In Xataka Basics | What is Claude Cowork, how it works, and what things you can do with this AI assistant on your computer

We thought it took us a long time to learn to cook. Until some 780,000-year-old carp teeth rewrote history

by

If we think about the technology that has most transformed humanity, it is easy for the wheel, the steam engine or the microchip to come to mind in a more current way. However, there is a much older and more fundamental “technology” that literally changed our anatomy: the kitchen. The evolution. For decades, paleoanthropologists have debated At what exact moment did our ancestors stop consuming raw foods to start processing them through the control of fire. The most recent evidence not only rewrites our chronology, but confirms that mastering cooking was the true driving force of human evolution. How do you know? Date something as precise as the beginning of cooking, but the reality is that Until recently, indisputable evidence of the continued use of fire for cooking They were around 600,000 years old. However, a great finding published in the prestigious magazine Nature in 2022 set back this evolutionary clock. In this case it was at the site of Gesher Benot Ya’aqovin Israel, remains of large carp teeth were found. With these samples and through advanced techniques such as X-ray diffraction, the researchers demonstrated that these remains had been exposed to controlled and relatively low temperatures, being less than 500 °C. The first date. With this evidence it seemed quite clear that it was not an accidental fire, but rather that it was dated 780,000 years ago these animals began to be cooked. This is consistent with the fact that Acheulean hunter-gatherers were already exploiting aquatic habitats, selecting nutrient-rich fish and cooking them in what archaeologists call “ghost hearths,” which were structured fire zones. Another hypothesis. Although direct evidence pointed us back to 780,000 years ago, biological clues suggest that the culinary revolution began much earlier. This is what primatologist Richard Wrangham pointed out, in his book Catching Fire and in subsequent studies published in Current Anthropology, proposing that systematic cooking emerged with Homo erectus approximately 1.9 million years ago. Your arguments. To be able to give this date, this expert focuses mainly on energy efficiency, since he points out that cooking predigests food, breaking down fibers and starches. This allows you to obtain many more calories with minimal effort. But the most relevant thing is that by facilitating digestion, the Homo erectus It no longer needed a massive intestinal tract to process hard, raw vegetables. And here size matters, since intestinal tissue and brain tissue are energetically very expensive, and so, by shrinking the intestine, the excess energy could be redirected to the growth of a much larger and more complex brain. But this softer diet also explains why the molars of the Homo erectus They shrank and their jaws became less prominent. Beyond nutrition. The implementation of cooking not only brought anatomical benefits, but studies indicate that in the case of the first hominids, this was essential for roasting raw meat and killing the bacteria that were inside. But in addition, fire control and the ability to process food were key tools that facilitated human migration. In reassessments of classic sites, such as the Zhoukoudian caves in China, they confirm that the Homo erectus pekinensis used controlled fire to cook deer meat in specific stratademonstrating that this practice was essential for adapting to colder climates outside of Africa. Images | Michael Lock

These tips can help you take advantage of it and really learn

by

Artificial intelligence (AI) is changing many things, and one of them is how we study and learn. But it is one thing to talk about AI in the abstract and another, very different, to use it seriously to advance a subject. That’s where the real doubts begin: which tool is worth it, where to start or, above all, how to prevent the chatbot from becoming a a summary machine and you don’t have anything left. In a new video from Xataka’s YouTube channel We wanted to go right to that point, with a practical approach and a real user experience that will probably be familiar to you if you are a student. Francisco Franconi shares his experience with us today. He is a university student and along this path he has discovered real gems to squeeze out. Gemini (yes, on this occasion we momentarily put aside ChatGPT to try a different and, on paper, very promising alternative). Even so, the interesting thing about his proposal is not so much in the specific tool as in the method: our colleague insists that his advice can be applied to any chatbot, because what makes the difference is not the model, but how you use it. The key is not in the chatbot, but in the method In fact, his first advice is very direct: “The first advice I am going to give you is that, If you are going to use AI passively, you are not going to learn anything“And it’s hard to argue. Franconi delves into one of the most common traps: asking AI to summarize a topic, copying it into a document and assuming that this is equivalent to studying. As he explains, this approach only creates an illusion of productivity. The alternative is to use it as real support, as a learning companion, not as a substitute that saves you mental work, and in the video he drops concrete advice to start doing it well from minute one. The second piece of advice points to one of the most powerful advantages of AI in the educational context: its ability to adapt to different levels of difficulty. The key here is to turn the chatbot into a kind of on-demand private teacher, capable of explaining the same thing to you with progressive approaches until you really understand it. “What I do is ask the AI ​​to explain the topics to me in the simplest way, let’s say, as I would explain it to a child. From there you increase the difficulty,” Franconi tells us. And nuance is important: it’s not just about simplification, but about building understanding in layers. “What I do is ask the AI ​​to explain the topics to me in the simplest way” More equally valuable tips appear throughout the video. One of the most interesting has to do with context, something that many overlook when using a chatbot to study: content matters, but how it is taught also matters. “When we take a subject, not only the syllabus matters, but also how the teacher gives the content. The important thing here is that the AI ​​has all the notestexts or slides necessary so that it can use them as a basis to teach you.” That is, if you want the AI ​​to really help you, it is not enough to ask it a generic question: you have to feed it with the real material that you are using in class so that its answer makes sense within the framework of your subject. And, of course, Franconi also enters the field where the casual user is separated from the one who really knows what he is doing: that of prompts. In the video he shares very worked examples, like this one: “You are a studio companion who is an expert in Contemporary Art. Your objective is to show me the contents of the documents that I have uploaded, following a strict and slow order. Your way of working is as follows:”, and from there he displays detailed instructions to guide the model. What is powerful is not just the text, but the logic behind it: defining a role, setting objectives, imposing a rhythm and establishing rules. And the best thing is that this scheme can be easily adapted to any subject. As we say, there is much more in the video. It’s already published in it Xataka YouTube channelso if you want to see all the tips in context, we invite you to see it and leave us your comments both there and in this article. Images | Xataka In Xataka | The change of Google’s search engine with AI was a mystery about its monetization. Finally it will be another subscription

Behind this year’s Nobel Prize in Medicine there is a whole lesson in scientific policy for Spain and it does not seem that we are going to learn it

by

The Nobel Prizes arrive and, like every year, the media they are filled with reports on why Spain resists the great scientific awards of the contemporary world. And it is not a lie: the last Spaniard to win one in science, Severo Ochoa, did so 66 years ago. Being a relatively important country internationally, it is a real problem. What we did not suspect is that the Karolisnka Institute was going to make it so clear how ‘real’ this problem is. A little highlighted detail. At this point in the week, the history of the 2025 Nobel Prize in Medicine It has been counted as active and passive; But there is a detail that is worth dwelling on. In the late 1980s and early 1990s, Shimon Sakaguchi discovered a subset of T lymphocytes that did not attack anyone or anything. They were a kind of “riot police” of the immune system: they suppressed the activity of other T lymphocytes. The discovery was momentous, but what came next was an enormous silence. Silence? But they just gave him the Nobel Prize! They just gave it to him now, but it was not a bed of roses. Sakaguchi’s idea made sense, but no one was quite clear why that was happening. And, in fact, many people were vehemently against his theses. It took almost a decade for two different teams to reach the same conclusion: the Japanese researcher was right and the key to everything. the problem was in the FOXP3 gene. It seems like a minor issue, but “this double discovery, the cellular discovery of Sakaguchi and the genetic discovery of Brunkow and Ramsdell, has completely changed the paradigm of immunology and has opened two great therapeutic avenues with immense potential.” The relevant question in Spain. This is all very well, but the really relevant question for our country is why in 2020, when the Nobel Prize in Chemistry awarded CRISPR, it did not follow the same logic. Because yes, there are big differences between one discovery and the other: while the former rewarded the technological tool, this one has rewarded the discovery of the fundamental scientific bases. But it is not lost on anyone that the narrative of the award is not just an explanation: it is a framework that justifies inclusions and exclusions. The “forgetfulness” of the 2020 Nobel Prize. Francis Mojica himself he explained to us that “when we discovered CRISPR, I said to myself: “this is going to be crazy in biology” and then absolutely nothing happened.” In fact, that “nothing” lasted for many years. Years in which CRISPR seemed like a scientific curiosity without much importance and working on the subject, as Mojica did, was seen as an eccentricity. And finally, when the award came, it focused on “the development of a gene editing method (CRISPR-Cas9)” and was awarded to the two researchers who discovered that we could use the mechanism to our advantage; but no one remembered the person who discovered this mechanism. And it would be naive not to ask ourselves why. Even if we cannot know what really happened (the prize selection process has been hidden for 50 years), it is a good time to compare the abysmal differences between the research policy of Spain and that of Japan. While in the country of the rising sun, it has been investing in “scientific diplomacy” since the 90s; while Spain has made some isolated effort, yes; but insufficient. This is not about creating intricate conspiracy theories. It is clear that we will not be able to say what would have happened if Francis Mojica were Japanese, but we can ask ourselves what extra-scientific factors intervene in this type of awards and what Spain is doing to value its contribution to current contemporary science. That is, not only what resources are dedicated to research; but what is Spain’s ‘soft-power’, what resources does it put to make our researchers visible, to spread favorable stories or to amplify the work of our teams. The answer to all this, I’m afraid, is “too little.” Image | Ryan Faulkner | Daniel Prado In Xataka | A Nobel with 30 years of history: the discovery of the “peacekeeping gene” that controls our defenses is the 2025 Nobel Prize in Medicine

Log In

Forgot password?

Forgot password?

Enter your account data and we will send you a link to reset your password.

Your password reset link appears to be invalid or expired.

Log in

Privacy Policy

Add to Collection

No Collections

Here you'll find all collections you've created before.