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Working provisional copy - Pls do not circulate 32 MICHAEL J. KURTZ analysis of huge data sets. The ATLAS experiment at CERN has a raw data rate of 60TB/s (Klous 2010), about fifty trillion times the information handling capacity of humans (Fitts 1954). The underlying technologies are being developed to satisfy the needs of huge systems, like the LHC experiments, but ever larger systems of sensor networks are being created to take advantage of the new capabilities. For these systems to combine their impact, they need to be able to communicate with each other; they need to share a language (Kurtz 1989; 1992). The shared language need not be native to any particular system or experiment (Hanisch 2001), but needs to be universally understood, like medieval Latin, or English today. This standardization is taking place across all scientific and technical fields, examples are the International Virtual Observatory Alliance’s Simple Application Messaging Protocol (SAMP; Taylor, et al 2009) or the Open Access Initiative’s Object Re-use and Exchange (OAI- ORE) protocol (Van de Sompel, et al 2009). Automated memory and automated perception are combining to form automated ideas. Standardized data descriptors along with semantic tagging of text (Accomazzi 2009) produce a new environment, in which inference engines, similar to the stochastic/syntactic procedures used to analyze bubble chamber tracts (Fu and Bhargava 1973) are able to discover new, important associations and patterns. These systems will be able to model and predict the behavior of humans (Barabasi 2010), indeed Ossorio (1967, 1977) spent a good fraction of his career using his methods to model the behavior of specific humans. The role of these systems will not be to model individual human behaviors, but will be to act as the functional core for our collective intelligence. This collection of machines will not provide the totality of the brain for the emergent super-organism which is human society; the higher functions, the cognitive layer, will be provided by the sum of all people. The machines will provide the core functionality, the lizard brain. Analogous with our own evolution there will be innate capabilities, like our instant reaction to heat, and anomalies, like our fear of snakes. Individual scholarly disciplines have long functioned as semi-autonomous super-organisms. The memory function (on paper) being journal articles in university libraries. With the advent of the internet scholars are rapidly transforming their work habits to take advantage of the possibilities (Borgman 2007). New technologies effecting the speed of information transfer (Ginsparg 1994), and the ability to directly and effectively access huge datasets (Szalay and Gray 2001) have already been created, more are coming daily. The scholarly literature has been fully digital for more than a decade. The idea that a discipline, such as astronomy, already functions as a coherent superorganism, with electronic memory and perception systems is not at all far
The Emerging Scholarly Brain 33 fetched. Millions of years before he imagined it would occur we are beginning to implement Stapledon’s(1930) racial mind, beginning first not with the human race, but with the sub races of astronomers, bio-chemists, economists, particle physicists, etc. With this scholarly brain we are achieving “insight into many unsuspected and previously inconceivable features of the familiar world of men and things.” 6. Acknowledgments This essay is dedicated to the memory of two extraordinary scientists who showed me great kindness. Peter Ossorio’s work continues to inspire me in several ways. Jim Gray’s (2007) 4 th Paradigm is the clearest exposition of how the new science will actually function. Also Andre Heck has, through the years, provided me, and many others, with venues to discuss the deeper meaning of some current trends. The references to this paper alone list several. The ADS is funded by NASA Grant NNX09AB39G. References Working provisional copy - Pls do not circulate 1. Accomazzi, A. (2010) Astronomy 3.0 Style. ArXiv e-prints arXiv:1006.0670. 2. Alexander, C., Ishikawa, S., and Silverstein, M. (1977) A Pattern Language, New York: Oxford University Press. 3. Baldwin, J. A., Phillips, M. M., Terlevich, R. (1981) Classification parameters for the emission-line spectra of extragalactic objects. Publications of the Astronomical Society of the Pacific 93, 5-19. 4. Barabasi, A-L (2003) Linked, New York: Plume. 5. Barabasi, A.-L. (2010) Bursts: the hidden pattern behind everything we do, New York: Dutton. 6. Bollen, J., Van de Sompel, H., Hagberg, A., Bettencourt, L., Chute, R., Rodriguez, M.A.,Balakireva, L. (2009a) Clickstream Data Yields High-Resolution Maps of Science, PLoS ONE, 4, e4803 7. Bollen, J., Van de Sompel, H., Hagberg, A., Chute, R., Mailund, T. (2009b). A Principal Component Analysis of 39 Scientific Impact Measures. PLoS ONE 4, e6022. 8. Bonacich, P. (1972) Factoring and weighting approaches to status scores and clique identification, Journal of Mathematical Sociology 2, 113-120. 9. Borgman, C.L. (2007) Scholarship in the Digital Age: Information, Infrastructure, and the Internet, Cambridge: MIT Press. 10. Brin, S. & Page, L. (1998),The Anatomy of a Large-Scale Hypertextual Web Search Engine, Computer Networks and ISDN Systems, 30, 107. 11. Connolly, A. J., Szalay, A. S., Bershady, M. A., Kinney, A. L., Calzetti, D. (1995) Spectral Classification of Galaxies: an Orthogonal Approach. The Astronomical Journal 110, 1071. 12. Davis, P.M. (2008) Eigenfactor: Does the Principle of Repeated Improvement Result in Better Estimates than Raw Citation Counts?, Journal of the American Society for Information Science and Technology, 59, 2186. 13. Deerwester, S., Dumais, S., Furnas, G., Landauer, T., & Harshman, R. (1990). Indexing by latent semantic analysis. Journal of the American Society for Information
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