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© 2020 Relevant Protocols Inc.
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About the dangers of our increasing reliance on artificial intelligence. Zittrain considers the vulnerabilities and risks inherent in automated thinking technology — and how these might be exploited for nefarious purposes.
About the dangers of our increasing reliance on artificial intelligence. Zittrain considers the vulnerabilities and risks inherent in automated thinking technology — and how these might be exploited for nefarious purposes.
Interesting thoughts on the concept of "Intellectual debt" and it ramifications to systems such as AI where iteration upon unexplained correlations may become problematic and leave systems open to failures and adversarial gaming. "In the past, intellectual debt has been confined to a few areas amenable to trial-and-error discovery, such as medicine. But that may be changing, as new techniques in artificial intelligence—specifically, machine learning—increase our collective intellectual credit line. Machine-learning systems work by identifying patterns in oceans of data. Using those patterns, they hazard answers to fuzzy, open-ended questions. Provide a neural network with labelled pictures of cats and other, non-feline objects, and it will learn to distinguish cats from everything else; give it access to medical records, and it can attempt to predict a new hospital patient’s likelihood of dying. And yet, most machine-learning systems don’t uncover causal mechanisms."
Interesting thoughts on the concept of "Intellectual debt" and it ramifications to systems such as AI where iteration upon unexplained correlations may become problematic and leave systems open to failures and adversarial gaming. "In the past, intellectual debt has been confined to a few areas amenable to trial-and-error discovery, such as medicine. But that may be changing, as new techniques in artificial intelligence—specifically, machine learning—increase our collective intellectual credit line. Machine-learning systems work by identifying patterns in oceans of data. Using those patterns, they hazard answers to fuzzy, open-ended questions. Provide a neural network with labelled pictures of cats and other, non-feline objects, and it will learn to distinguish cats from everything else; give it access to medical records, and it can attempt to predict a new hospital patient’s likelihood of dying. And yet, most machine-learning systems don’t uncover causal mechanisms."
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