The good our field can hope to do, the harm it should avoid

This paper tries to achieve a balanced view of the ethical issues raised by emotion-oriented technology as it is, rather than as it might be imagined. A high proportion of applications seem ethically neutral. Uses in entertainment and allied areas do no great harm or good. Empowering professions may do either, but regulatory systems already exist. Ethically positive aspirations involve mitigating problems that already exist by supporting humans in emotion-related judgments, by replacing technology that treats people in dehumanized and/or demeaning ways, and by improving access for groups who struggle with existing interfaces. Emotion-oriented computing may also contribute to revaluing human faculties other than pure intellect. Many potential negatives apply to technology as a whole. Concerns specifically related to emotion involve creating a lie, by simulate emotions that the systems do not have, or promoting mechanistic conceptions of emotion. Intermediate issues arise where more general problems could be exacerbated-helping systems to sway human choices or encouraging humans to choose virtual worlds rather than reality. "SIIF" systems (semi-intelligent information filters) are particularly problematic. These use simplified rules to make judgments about people that are complex, and have potentially serious consequences. The picture is one of balances to recognize and negotiate, not uniform good or evil. © 2010-2012 IEEE.

A Capabilities-Aware Framework for Using Computational Accelerators in Data-Intensive Computing

Multicore computational accelerators such as GPUs are now commodity components for highperformance computing at scale. While such accelerators have been studied in some detail as stand-alone computational engines, their integration in large-scale distributed systems raises new challenges and trade-offs. In this paper, we present an exploration of resource management alternatives for building asymmetric accelerator-based distributed systems. We present these alternatives in the context of a capabilities-aware framework for data-intensive computing, which uses an enhanced implementation of the MapReduce programming model for accelerator-based clusters, compared to the state of the art. The framework can transparently utilize heterogeneous accelerators for deriving high performance with low programming effort. Our work is the first to compare heterogeneous types of accelerators, GPUs and a Cell processors, in the same environment and the first to explore the trade-offs between compute-efficient and control-efficient accelerators on data-intensive systems. Our investigation shows that our framework scales well with the number of different compute nodes. Furthermore, it runs simultaneously on two different types of accelerators, successfully adapts to the resource capabilities, and performs 26.9% better on average than a static execution approach.