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The paper Fair and Unbiased Algorithmic Decision Making: Current State and Future Challenges argues that ensuring fairness is not a trivial task and that the current statistical formalizations of fairness lead to a long list of criteria that are each flawed (or even harmful) in different contexts, that is, there are trade-offs between the proposed ...


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It might be of note to comment/update that the SuperGLUE benchmark, which is a suite of common sense reasoning tasks, incorporates the aforementioned Winnograd Schema challenge, among other tests that are said to be reflective of natural language understanding (as opposed to simply its processing or the optimal statistical generation of language). The most ...


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Disclaimer: The intent of this answer is to suggest a a parallel between methods of acting and machine learning, both in intent and application, and theory. A large number of links are included for the convenience of readers new to the field, and there is not an exact correspondence of AI concepts to acting preparation techniques. In my prior answer, I ...


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The great acting teacher Stella Adler wrote about mannerisms being a powerful tool for actors. Method acting in general focuses on natural performances based roughly on understanding the mindset of the character portrayed. It's possible actors who have portrayed androids have observed industrial robots to inform their physicality, and many performances ...


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Automation has been involved in Religion since at least the 4th century in Buddhist Prayer Wheels. Recently there has manifested a Buddhist robot that chants sutras. The makers certainly intend to eventually integrate machine learning, and, with the acceleration in the capabilities of chatbots, I suspect there will be robots capable of giving humans ...


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My sense is that AI begins with automation. Originally I believed water clocks were the first embodied algorithms, but now I think the first simple traps and snares: Are simple animal snares and traps a form of automation? Of computation? (Mechanism, in and of itself, is not understood to be intelligence, but when mechanism is selected for fitness, it ...


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It is a part of the article Karen Cadora "Feminism cyberpunk" "Until very recently, cyberpunk has been a predominantly masculinist project with few strong female characters. Often characterized by a nostalgia for an organic, pastoral past, feminist sf remains largely untouched by cyberpunk's enthusiasm for technology. In the last few years, however, a ...


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I am unsure about the cyberpunk portion of this, but there has been an extensive feminist literature developed under the topic of "Gynoids" and the gendering of robots and other forms of AI (like virtual assistants). These papers tend to argue that the choice of gender assigned by AI developers to their creations is reflective of implicit social views and ...


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Offloading of responsibility may the single greatest danger. Where algorithmic bias may be the core issue of Machine Learning, it can be identified and mitigated. Transferring responsibility to a robot or algorithm requires an intentional choice with moral dimension. As the scholar Joanna Bryson put it: In humans consciousness and ethics are ...


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IMHO the greatest risk is that AI can make people lazy. If you can ask an AI for an answer to any problem, what's your motivation to figure out how to figure out the answer for yourself? I have run into a lot of young people who can't add or multiply two three-digit numbers without using a calculator. When it's possible to dump a huge mass of data into an ...


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One risk that’s already realized: large online vendors think they have implemented artificial intelligence in their “help” pages and therefore they can (try to) make it impossible to get to someone who can actually think. And since the artificial stupidity (AS) usually feeds the customer articles completely unrelated to the issue, anyone sufficiently ...


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The biggest risk is algorithmic bias. As more and more decision-making processes are taken on by AI systems, there will be an abdication of responsibility to the computer; people in charge will simply claim the computer did it, and they cannot change it. The real problem is that training data for machine learning often contains bias, which is usually ...


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