【MLSys Weekly】A Berkeley View of Systems Challenges for AI

论文原文:A Berkeley View of Systems Challenges for AI

这又是篇不涉及过多技术的一篇论文,不过其中提到的Challenges确是目前AI Systems一直在努力解决的。

文中提到的几点关于AI System的Challenge:

  • Design AI systems that learn continually by interacting with a dynamic environment, while making decisions that are timely, robust, and secure.
  • Design AI systems that enable personalized applica- tions and services yet do not compromise users’ privacy and security.
  • Design AI systems that can train on datasets owned by different organizations without compromising their confidentiality, and in the process provide AI capabilities that span the boundaries of potentially competing organization.
  • Develop domain-specific architectures and soſtware systems to address the performance needs offuture AI applications in the post-Moore’s Law era, including custom chips for AI work-loads, edge-cloud systems to efficiently process data at the edge, and techniques for abstracting and sampling data.