US warns consumers over recalled beef

In the United States, the growing shift from traditional cigarettes to e-cigarettes and other nicotine products could potentially lead to a decline in cancer rates in the future. A new study published in JNCI Cancer Spectrum analyzed data from US federal health surveys collected between 2015 and 2023. According to the study, traditional cigarette smoking declined significantly across almost all age groups during this period with the decline being particularly noticeable among people aged 18 to 34. In contrast, the number of people using only alternative nicotine products particularly e-cigarettes, increased. The study’s lead researcher, Professor Kenneth Michael Cummings of the Medical University of South Carolina said the trend suggests that many smokers are completely switching to vaping rather than using both products simultaneously. The study found that the proportion of people who used both cigarettes and e-cigarettes remained around 2% to 3% throughout the study period. Scientists also developed a Population Harm Reduction Index to compare traditional cigarette smoking with the use of non-combustible nicotine products. According to the researchers, an increase in the index indicates that more people are using alternatives to traditional cigarettes that may expose them to lower levels of potentially cancer-causing chemicals. The study found that people aged 55 and above were much less likely than younger people to quit smoking and switch to alternative products even though older adults could gain significant immediate health benefits from quitting smoking. According to the study, vaping has now become more common than cigarette smoking among young people aged 18 to 24. Professor Cummings said the greatest concern is that older people who have smoked for many years are not switching to lower-risk, non-combustible nicotine products as quickly. This is the group that could potentially gain the greatest immediate health benefits from quitting smoking. According to the report, the study was funded by the US National Cancer Institute and the Food and Drug Administration (FDA) while the vaping industry provided no financial support.

Written by Muhammad Naseer It is deeply disappointing that in a big, developed and educated city like Turbat, there is not a single drawing academy. Art is no longer just a hobby. In today’s generation, it is a great field and a promising career option. Students can make a future in graphic design, animation, architecture, fashion designing, and fine arts. However, young people in Turbat have no platform, no teacher, and no institution to learn and improve their skills. I myself am practicing for a college-level art competition and I am planning to pursue this field after completing my FSc. But due to the absence of any academy in our city, students like me are forced to learn alone without proper guidance. The government, education department, and local organizations should take serious steps to establish a drawing and art academy in Turbat. This will help our talented youth explore their creativity and build a secure future in the field of art.

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.