Pulling the Curtain from AI Illusions
Many AI systems, especially those generating texts and images are starting to give an impression that they understand and even feel things. This illusion create misinformation and misdirection.
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Many AI systems, especially those generating texts and images are starting to give an impression that they understand and even feel things. This illusion create misinformation and misdirection.
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With data as the infrastructure, and machine learning and deep learning as the technology foundations of building AI systems combined, they largely determine the performance, fairness, robustness, reliability, and scalability of AI systems.
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Ethical dilemma is a common problem for humans, but this is as true for humans as it is for machines. Hence, machines need to be enhanced to make the optimal decision in the face of ethical dilemma.
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The growth and the impact of AI on the world are extensive. However, when AI algorithms introduce bias, preventable errors, and poor decision-making, it causes mistrust among the very people it is supposed to be helping.
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There is much too hype around AI systems that are acting like they know or feel. But we are not asking enough if we should even want such systems. Blindly trusting systems that make us believe as if they understand us or know everything can be dangerous.
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Techniques for uncovering who’s behind anonymous identities, and unmasking forged ones, are known as “Authorship Identification” (AID, for short), which are part of the field of “Authorship Analysis”, whose goal is to infer characteristics (such as the gender, the age, or the native language) of the authors of written documents.
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Have you ever wondered how Google helps you complete your search query by suggesting the next terms of your query? Large Language Models (LLMs) power this feature. But LLMs go beyond that feature. Today, LLMs are used in building AI systems and applications ranging from recognizing speech to writing poetry. They have become very powerful, but there are also pitfalls.
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According to privacy researcher Daphne Muller, the era of big data will soon come to an end.
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In this episode of InfoFire—the fireside chat series from Information Matters—a conversation with Gio Wiederhold, professor emeritus of computer science, electrical engineering, and medicine at Stanford University on “Different Flavors of AI.”
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We all want fairness in our lives. We want to everyone to pay their fair share, get a fair shot at opportunities, and provide fair explanations to life-changing decisions. But what we can’t agree on is what is this fairness and how to implement it in everyday systems we use.
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