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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Modern AI advancements are full of hopes and hypes and some horrors. The question is – can we address those horrors enough? Or is there a choice to make between autonomy and authority? How will that choice play out in a real world scenario?
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As educational institutions strive to accommodate the needs of this diverse demographic of students, information professionals are presented with a unique opportunity to harness the power of AI technology and elevate their role in enhancing the international student experience.
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To date, the selection of AI tools has lagged behind work on other software-specific aspects of modern learning platform development. We designed a novel technology relevance assessment methodology based on an expert Delphi survey and multicriteria analysis. Then, we applied the results to plan further development of the prototype platform and to build its exploitation strategy. The lessons learned while planning and developing this platform can be applied to a large class of similar systems.
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