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Five years ago, Microsoft released its infamous bot, Tay, into the world of Twitter. Tay used a machine learning algorithm to learn from interactions on the platform, to then echo novel responses back based on that learning. Within short time it became obvious that Twitter is not ideal ground for unsupervised learning. It turns out that the people who scream the loudest arenât always the best teachers.
Without a filtering mechanism, Tay started parroting back all kinds of racist, bigoted and misogynistic tweets. So naturally a question arose: how can we expect AI to learn on its own by engaging the world without letting the world corrupt it with its worst demons?
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Linda C. Smith, then a doctoral student at Syracuse University s School of Information Studies, published a landmark paper in 1976 on the role of AI in information retrieval. Forty years later, her thesis came to fruition.
Over the last few years, software vendors have positioned AI as the solution to every known enterprise search problems. Yet surveys carried out from 2016 to 2020 (and indeed way back to 2007) all indicate that search is not in any way a solved problem and indeed, only in a minority of cases is search regarded as very satisfactory. Why are companies prepared to accept anything less?
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