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Beijing: Chinese scientists have proposed a new planetary defense strategy to deflect or destroy potentially hazardous asteroids that could collide with Earth. According to the proposed approach, placing a nuclear device inside an asteroid and detonating it could be one of the most effective ways to deal with a major impact threat. According to a report, researchers at the China Academy of Launch Vehicle Technology said that large asteroids have always posed a serious threat to human civilization. In the distant past, asteroid impacts caused mass extinctions and triggered devastating environmental changes. The scientists noted that astronomers have discovered several asteroids hundreds of meters wide passing very close to Earth in recent years prompting increased research into new planetary defense methods. The study suggests that a nuclear explosion could be the most effective strategy for either destroying a large asteroid or deflecting it away from Earth particularly when there is very little time to respond. Under the proposed plan, a spacecraft would first create a deep crater on the asteroid. A nuclear device would then be placed inside the crater and detonated to alter the asteroid’s trajectory or break it into smaller pieces. According to the research, a 100-meter-wide asteroid could be completely destroyed by an explosion with a yield equivalent to 200 times the atomic bomb dropped on Hiroshima. Similarly, detonating a nuclear device at a depth of 20 meters inside an asteroid about one kilometer wide could change its velocity enough for it to safely miss Earth. The researchers said the study could help pave the way for future planetary defense strategies against large near-Earth asteroids.

A new study has found that children who consume high amounts of sugar at an early age may face a higher risk of developing dementia later in life. The study suggests that pregnant women who avoid sweets, chocolates and other sugary foods may help reduce their child’s risk of developing the disease by up to 20 percent. The research published in the medical journal Neurology also found that consuming very small amounts of sugar during early life may provide protection against dementia. The study’s lead author, Professor Jiazheng Zheng said the findings suggest that limiting sugar intake during the early stages of life could have long-term benefits for brain health. He further said the findings highlight the potential importance of early-life nutrition in reducing the risk of dementia decades later. Researchers from the Hong Kong University of Science and Technology compared the health of 64,737 people who were born before and after sugar rationing was introduced in Britain during World War II. The participants were divided into groups based on the timing of sugar rationing: those who experienced limited access to sugar before birth and during their first year of life, those who experienced it before birth and until the age of two and those who were not exposed to sugar rationing. When the participants reached an average age of 55, the research team began analyzing their medical records to determine which individuals were diagnosed with dementia during the following 15 years. Among the 15,256 people who had limited access to sugar before birth and during their first two years of life, only 2 percent were later diagnosed with dementia. Although researchers say the findings do not conclusively prove that eating less sugar directly prevents dementia, experts believe the results are consistent with growing evidence that sugar can affect brain function. Experts also say that the brain is highly sensitive to fluctuations in blood sugar levels. Other research has found that people with high blood sugar levels may face up to a 69 percent higher risk of Alzheimer’s disease.

By Syeda Samar Zahra Zaidi When you ask an AI chatbot a question it does not know the answer to, an odd thing can happen: instead of hesitating or saying, “I am not sure!” it confidently delivers a tidy, well-structured, completely fabricated response—often with fake citations, fictional figures, and an authoritative tone. This phenomenon is known as the ‘hallucination trap ’, and it may be the most significant barrier between trust and artificial intelligence. I was introduced to the term “AI hallucination” for the first time in the workshop under the project titled “Digital Trust and Free Expression in the AI Era” at Department of Media and Development Communication, University of the Punjab, supported by the U.S. Mission to Pakistan in partnership with the Pakistan-U.S. Alumni Network (PUAN). I learned that a “hallucination” occurs when a language model produces information that sounds convincing but is factually incorrect or completely made up. Examples include inventing a legal case that never existed, citing a research paper that doesn’t exist or boldly misrepresenting a historical date. What is disturbing is not merely that AI can be wrong-humans make mistake too- but rather the way it presents that incorrect information with clearly, confidence and without a trace of doubt. Large language models are essentially highly complex pattern predictors trained to produce the most likely next word in a sequence. As a result, when a model has encountered a topic thousands of times in its training data, the predictors are typically accurate. However, when it comes across a gap, an unidentified fact or a question at the outer edge of its knowledge, it doesn’t shift into a hesitant mode. Instead, it continues doing exactly what it was trained to do: generate a logical, fluent piece of writing, producing a response that is structurally sound and grammatically correct but based on nothing. In human communication, confidence is generally perceived as a sign of strength which is important. We naturally prefer to trust a calm, reliable voice over a hesitant one. AI systems unintentionally exploit the tendency because their language remains confident whether they are right or wrong. The risks increase with the use of AI, as students may unintentionally include false information into their work and journalists have published articles citing artificially created sources and quotes, negatively affecting the credibility of their publications. Such inaccuracies influence what millions of people see, believe or share, thereby amplifying fabricated content. The reason AI does not simply say “I don’t know” is partly due to the way these systems are trained. Many models are designed to provide helpful, comprehensive-sounding answers and are rarely rewarded for expressing doubt during training. This means that a model that frequently acknowledges uncertainty may be judged as less helpful than one that consistently provides an answer, even when that confidence is occasionally misplaced. Such incorrectly aligned incentives arise when the training process gradually teaches the model that sounding confident is more valuable than being accuracy. Retrieval-augmented generation, which relies on responses in actual, verifiable records rather than internal memory alone, improved training techniques that explicitly account for uncertainty; and systems built to simply cite sources. So, users can independently verify claims are just a few of the ways developers are using to addresses this problem. While none of these solutions fully eliminate hallucination yet, taken together they represent a major shift from blind confidence toward earned trust. The most dangerous outcome is not an AI that makes mistakes but a culture of users who stop questioning because the system always sounds so sure of itself.   Therefore, until AI becomes more reliably aware of it own limitation, part of the responsibility falls on the people who use it: treating AI-generated output as a starting point rather than an endpoint, cross checking important facts against credible sources. In the end, the hallucination trap serves as a reminder that illusion is not the same as truth. As AI becomes increasingly common in education, digital media and everyday life, understanding why it occasionally lies with confidence is not only a technical curiosity but also a fundamental aspect of digital literacy. We must strengthen our ability to verify the information provided by AI as they become more persuasive. The views, thoughts, and opinions expressed in this blog are solely those of the author and do not necessarily reflect the official policy or position of the U.S. Mission to Pakistan or USEFP. The writer, Syeda Samar Zahra Zaidi, is a student of Media and Development Communication at  University of the Punjab, Lahore and can be reached at samarzahra456@gmail.com.