On Computing: The Fourth Great Scientific Domain

On Computing: The Fourth Great Scientific Domain

Paul S. Rosenbloom

Language: English

Pages: 332

ISBN: 0262018322

Format: PDF / Kindle (mobi) / ePub

Computing is not simply about hardware or software, or calculation or applications. Computing, writes Paul Rosenbloom, is an exciting and diverse, yet remarkably coherent, scientific enterprise that is highly multidisciplinary yet maintains a unique core of its own. In On Computing, Rosenbloom proposes that computing is a great scientific domain on a par with the physical, life, and social sciences.

Rosenbloom introduces a relational approach for understanding computing, conceptualizing it in terms of forms of interaction and implementation, to reveal the hidden structures and connections among its disciplines. He argues for the continuing vitality of computing, surveying the leading edge in computing's combination with other domains, from biocomputing and brain-computer interfaces to crowdsourcing and virtual humans to robots and the intermingling of the real and the virtual. He explores forms of higher order coherence, or macrostructures, over complex computing topics and organizations. Finally, he examines the very notion of a great scientific domain in philosophical terms, honing his argument that computing should be considered the fourth great scientific domain.

With On Computing, Rosenbloom, a key architect of the founding of University of Southern California's Institute for Creative Technologies and former Deputy Director of USC's Information Sciences Institute, offers a broader perspective on what computing is and what it can become.

















This subsumes the science and engineering of computing; basic and applied work with computers (including scientific computing and the many informatics disciplines); the academic disciplines of computer science and engineering; information science and technology; and the divide between academic and industrial computing. Understanding is The Computing Sciences 19 more central in those segments of the domain characterized by such terms as science, basic, and academic, whereas shaping takes the

nodes in a directed graph that visits each node in the graph exactly once while traveling along the links only in the directions indicated.49 Nodes and links were represented as strings of nucleotides, the molecules that combine to form DNA and RNA, with the link nucleotides complementary to the node nucleotides (figure 3.9). When mixed together in large quantities, random paths through the graph became encoded in nucleotide sequences. Filtering steps, involving amplification and purification,

in how it generates new candidates from combinations of existing ones rather than from individual ones. Within evolutionary computing, some of the work strives to follow the mechanisms of evolution found in the living world quite closely, whereas other work uses the idea only metaphorically. No significant computational paradigms that I am aware of have been inspired by the details of ontogenetic computing, but several have been loosely motivated by it. Cellular automata58 were invented by John

turn the palm into a virtual keyboard (Carnegie Mellon University). Image courtesy of Chris Harrison, Desney Tan, Dan Morris—Microsoft Research & Carnegie Mellon University. perception–action cycle on the human side of the interaction but an action–perception cycle on the computational side. The computer prompts the human via a communication action to set up a subsequent need on its part of perceiving, and understanding, the human response. In such circumstances, the computer is in charge, as it

such as facial expressions, gestures, and body positioning all become part of the language of interaction. Augmented cognition70 moves the human–computer interaction paradigm more strongly in the direction of polyadic computing, combining sensing of the state of the brain, usually through a noninvasive brain– computer interface, with a form of presentation that is adapted according to this perceived state: C↔S/L or L→C→S. For example, if measurements via EEG show that the brain is overloaded,

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