What is the Landscape of Health-Related Information on Q&A Platforms?
What is the Landscape of Health-Related Information on Q&A Platforms? Jianyao Chen, Jin Zhang Have you ever searched online for
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What is the Landscape of Health-Related Information on Q&A Platforms? Jianyao Chen, Jin Zhang Have you ever searched online for
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This InfoFire conversation is with Ravi Venkatesan—business leader, social entrepreneur, author, and one of India’s most influential voices on leadership and inclusive development. Having led organizations as diverse as Cummins India, Microsoft India, Bank of Baroda, and the Global Alliance for Mass Entrepreneurship (GAME), while also serving on the boards of institutions such as Rockefeller Foundation, Hitachi, and Harvard Business School, Venkatesan brings a rare perspective that spans business, public policy, education, and social innovation. What makes his perspective particularly compelling is his conviction that AI is not merely a technological phenomenon but a societal one, requiring us to rethink markets, institutions, education, and entrepreneurship together.
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The rapid advancement of Artificial Intelligence (AI) has sparked a wave of anxiety across virtually every profession. From teachers and librarians to bankers, lawyers, journalists, and healthcare practitioners, many people are asking the same question: “Will AI take my job?” My answer is simple and direct: yes, AI can take your job, but only if you choose to remain superficial in your profession.
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Three days. That’s how long Fable 5 lasted before the U.S. government ordered Anthropic to switch it off worldwide, citing a vaguely described “jailbreak” and an export control directive broad enough to sweep in Anthropic’s own employees abroad. But the recall is only half the story: Anthropic had also moved fast, pushing its most capable model to the public within months of keeping its predecessor restricted to vetted partners. From Gemini’s image generator to Tay to GPT-4o’s sycophancy rollback, this is a pattern we keep repeating, and the people who pay for it are never the ones who decided to ship.
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What skills will matter most when humans work beside machines? A look at what the research and US labor data tell us about preparing the next generation workforce.
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Written communication promotes understanding, conveys authority, and inspires action. In organizations, emails often serve as a proxy for leadership presence by setting a tone, articulating expectations, and reinforcing organizational culture. Increasingly, however, this communication is no longer written solely by the sender. With a simple prompt, generative AI can instantly produce a confident response before we even have time to think or take a breath. These tools are now being suggested, and in some cases required, for drafting organizational communication. For the sender, AI-assisted writing can be helpful and even a source of relief when balancing tasks. For the recipient, AI-assisted writing may appear professional and well written. Over time, however, recipients may learn to recognize the patterns and tone of AI-generated communication. This recognition may change how people interpret the intent and authenticity of written communication.
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Health information no longer moves in a neat, straight line from the researcher’s bench to the clinician’s desk to the patient. Today, it ricochets. It travels through search engine auto-completes, TikTok feeds, private WhatsApp groups, and AI-generated summaries long before a patient ever sits down with a doctor or a medical librarian. For cancer patients and survivors exploring Complementary and Alternative Medicine (CAM), this networked reality creates both real possibility and serious risk. In digital spaces, the challenge isn’t just finding information. It is untangling how certain health claims become highly visible, endlessly repeated, and ultimately trusted.
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Research is a lifelong intellectual endeavour that transcends academic qualifications, professional status, and social background. Whether one is an undergraduate student, postgraduate scholar, healthcare practitioner, policymaker, entrepreneur, volunteer, or independent learner, research remains indispensable to growth, innovation, and societal advancement. Indeed, every meaningful improvement in human endeavour is rooted in the ability to seek, evaluate, and apply credible knowledge. Research, therefore, is not merely an academic requirement; it is a systematic and continuous process of building upon existing knowledge to solve emerging challenges and expand the frontiers of understanding.
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In academic research, novelty is one of the most important criteria for publication. A paper is expected to contribute something new, whether a method, a dataset, or a theoretical insight. But identifying novelty is not straightforward. Even experienced reviewers may disagree, and the rapid growth of scientific publications has made the task increasingly difficult. As the volume of submissions continues to rise, the peer review system faces growing pressure. This has sparked interest in whether artificial intelligence, particularly large language models (LLMs), can assist in evaluating research novelty. But before we can rely on AI for this task, a fundamental question must be answered: do LLMs actually understand novelty?
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Despite AI’s leverage on knowledge production, management, and storage, this informational revolution introduces a paradox. The expansion of information availability also presents risks of destabilizing knowledge trust. The root of this all leads us to AI hallucination, which produces believable and sophisticated results but is actually contrived and fictitious. It is in this sense that AI not only serves as an aid, but as a co-author of error.
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