论文标题
我们应该关注的只是我们需要的吗？统一在机器学习中的认识和道德意义
Should attention be all we need? The epistemic and ethical implications of unification in machine learning
论文作者
论文摘要
“您需要的只是您所需要的”已成为机器学习研究的基本戒律。最初是为机器翻译，变形金刚和基础的注意机制而设计的，现在他们在许多问题域中都找到了成功。凭借变形金刚的明显领域 - 不足的成功，许多研究人员感到兴奋的是，可以在视觉，语言及其他地区成功部署类似的模型体系结构。我们考虑了这些统一波在认知和道德方面的益处和风险。在认知方面，我们认为，许多支持自然科学中统一的论点未能转移到机器学习案例中，或者仅在可能不存在的假设下转移。统一还引入了与可移植性，路径依赖性，方法论多样性和增加黑箱有关的认知风险。在道德方面，我们讨论了从认知关注的关注中出现的风险，进一步的边缘化代表性不足的观点，权力集中化以及在更多应用领域的模型中更少
"Attention is all you need" has become a fundamental precept in machine learning research. Originally designed for machine translation, transformers and the attention mechanisms that underpin them now find success across many problem domains. With the apparent domain-agnostic success of transformers, many researchers are excited that similar model architectures can be successfully deployed across diverse applications in vision, language and beyond. We consider the benefits and risks of these waves of unification on both epistemic and ethical fronts. On the epistemic side, we argue that many of the arguments in favor of unification in the natural sciences fail to transfer over to the machine learning case, or transfer over only under assumptions that might not hold. Unification also introduces epistemic risks related to portability, path dependency, methodological diversity, and increased black-boxing. On the ethical side, we discuss risks emerging from epistemic concerns, further marginalizing underrepresented perspectives, the centralization of power, and having fewer models across more domains of application
