论文标题
部分可观测时空混沌系统的无模型预测
Is Decentralized AI Safer?
论文作者
论文摘要
储层计算是预测湍流的有力工具，其简单的架构具有处理大型系统的计算效率。然而，其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间，然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用，该系统模拟了湍流的若干特征。我们表明，使用径向基函数作为非线性投影器，即使只有部分观测并且不知道控制方程，也能稳健地捕捉复杂的系统非线性。最后，我们表明，当测量稀疏、不完整且带有噪声，甚至控制方程变得不准确时，我们的网络仍然可以产生相当准确的预测，从而为实际湍流系统的无模型预测铺平了道路。
Artificial Intelligence (AI) has the potential to significantly benefit or harm humanity. At present, a few for-profit companies largely control the development and use of this technology, and therefore determine its outcomes. In an effort to diversify and democratize work on AI, various groups are building open AI systems, investigating their risks, and discussing their ethics. In this paper, we demonstrate how blockchain technology can facilitate and formalize these efforts. Concretely, we analyze multiple use-cases for blockchain in AI research and development, including decentralized governance, the creation of immutable audit trails, and access to more diverse and representative datasets. We argue that decentralizing AI can help mitigate AI risks and ethical concerns, while also introducing new issues that should be considered in future work.
