Killer robots. Dystopian AI. Machine uprisings. Why are we utterly fascinated with the idea of our gadgets turning against us? Here I unravel our obsession with the rise of the machines and show that a big part of the appeal is really the projection of our own fears and dilemmas.
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Given AI’s dominance and growth potential, there is naturally enormous interest in decoding how AI will impact and shape India’s growth trajectory. To understand the landscape of artificial intelligence (AI) and the Information Technology (IT) industry in India, few voices resonate as profoundly as that of NR Narayana Murthy, the legendary co-founder of Infosys and a pioneering figure in India’s technological journey. With a career spanning decades, Narayana Murthy’s (NRN) insights have not only shaped the trajectory of Indian IT but have also influenced global perceptions of the industry.
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Individuals with intellectual disabilities require long-term education in specialized schools or institutions due to their unique learning needs and behavioral habits. Teachers with specialized training are essential in their education process. Based on volunteer experience at non-governmental organizations (NGOs) serving individuals with intellectual disabilities, we summarize common teaching strategies employed by teachers and challenges encountered in teaching to present the landscape of special education in China and to enlighten future LIS research.
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In the last two decades, AI has advanced leaps and bounds to the point where we are seriously having conversations about ‘it’ being ‘intelligent’ as humans or even surpassing humans. But before we get too wrapped up in that excitement or fear, it’s important that we look inwards and ask — what makes us intelligent, conscious, and human?
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Gemini is a new and powerful artificial intelligence model from Google that can understand not only text, but also images, videos and sounds. As a multimodal model, Gemini is capable of performing complex tasks in mathematics, physics, and other fields, as well as understanding and generating high-quality code in various programming languages.
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In this InfoFire episode, I am in conversation with eScience pioneer Tony Hey on the topic of “AI for Science.” Our fireside chat commences with the profound words of Richard Feynman, who had a significant influence on Tony Hey, and centers around the two books— Feynman Lectures on Computation: Anniversary Edition and Artificial Intelligence for Science: A Deep Learning Revolution, edited/coedited by Tony Heyand, published in 2023.
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Large language models (LLMs) are starting to get integrated in many of our daily information access activities. But they often hallucinate and provide information that can be harmful. We can’t and shouldn’t trust them blindly, but what choice we have if they become our primary method of accessing information? We want to leverage the enormous potential LLMs have, but we also want them to be trustworthy. How do we do this? I offer three specific suggestions.
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AI offers tremendous benefits for deepening and widening access to information. But there are also many problem and challenges. The future of AI-driven information access is not set yet, but it’s constantly being shaped through our discourse, policies, and actions. What will we choose and do? Is it possible to leverage AI’s potentials while minimizing risks and harms? I propose three ethos to make that happen.
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AI is a critical tool in addressing complex global issues, including climate change, by analyzing vast datasets and modeling potential solutions. However, this journey towards harnessing AI’s potential is accompanied by a pressing concern: the proliferation of misinformation generated by AI systems, such as fabricating references by ChatGPT.
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We examined the reliability of image recognition services by conducting a cross-comparison across three popular image recognition services: Google Vision AI, Microsoft Azure Computer Vision and AWS Rekognition.
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