Book contents
- The Cambridge Handbook of Translation
- Cambridge Handbooks in Language and Linguistics
- The Cambridge Handbook of Translation
- Copyright page
- Dedication
- Contents
- Figures
- Tables
- Contributors
- Acknowledgements
- Introduction
- Part I The Nature of Translation
- Part II Translation in Society
- Part III Translation in Company
- Part IV Translation in Practice: Factual Genres
- Part V Translation in Practice: Arts
- Part VI Translation in History
- 26 Translation before the Christian Era
- 27 Translation in the First Millennium
- 28 Translation in the Second Millennium
- 29 Translation in the Third Millennium
- Index
- References
29 - Translation in the Third Millennium
from Part VI - Translation in History
Published online by Cambridge University Press: 10 March 2022
Edited by
Book contents
- The Cambridge Handbook of Translation
- Cambridge Handbooks in Language and Linguistics
- The Cambridge Handbook of Translation
- Copyright page
- Dedication
- Contents
- Figures
- Tables
- Contributors
- Acknowledgements
- Introduction
- Part I The Nature of Translation
- Part II Translation in Society
- Part III Translation in Company
- Part IV Translation in Practice: Factual Genres
- Part V Translation in Practice: Arts
- Part VI Translation in History
- 26 Translation before the Christian Era
- 27 Translation in the First Millennium
- 28 Translation in the Second Millennium
- 29 Translation in the Third Millennium
- Index
- References
Summary
Chapter 29 predicts that we will come to understand the brain better and that technology will become more integrated with humans, which will have a revolutionary influence on how translation is conceptualized, practised and used. The concept of the original will be turned on its head should it become possible to replicate an entire brain, and global connectivity will acquire a new meaning if brains become connected in the way we are currently connected via machines external to our bodies. In these circumstances, translation will be central in the endeavour to build an interface among individuals.
Keywords
- Type
- Chapter
- Information
- The Cambridge Handbook of Translation , pp. 576 - 587Publisher: Cambridge University PressPrint publication year: 2022
References
Carl, M., and Schaeffer, M. J. (2017). Why translation is difficult: A corpus-based study of non-literality in post-editing and from-scratch translation. Hermes – Journal of Language and Communication in Business, 56, 43–57.Google Scholar
Deadwyler, S. A., Berger, T. W., Sweatt, A. J., Song, D., Chan, R. H. M., Opris, I., Gerhardt, G. A., Marmarelis, V. Z., and Hampson, R. E. (2013). Donor/recipient enhancement of memory in rat hippocampus. Frontiers in Systems Neuroscience, 7. https://doi.org/10.3389/fnsys.2013.00120.CrossRefGoogle ScholarPubMed
Delgado, J. M. R. (1966). Aggressive behavior evoked by radio stimulation in monkey colonies. American Zoologist, 6, 669–81. https://doi.org/10.1093/icb/6.4.669.Google Scholar
Delgado, J. M. R., Mark, V., Sweet, W., Ervin, F., Weiss, G., Bach-y-Rita, G., and Hagiwara, R. (1968). Intracerebral radio stimulation and recording in completely free patients. Journal of Nervous and Mental Disease, 147, 329–40. https://doi.org/10.1097/00005053-196810000-00001.CrossRefGoogle ScholarPubMed
Huth, A. G., de Heer, W. A., Griffiths, T. L., Theunissen, F. E., and Gallant, J. L. (2016). Natural speech reveals the semantic maps that tile human cerebral cortex. Nature, 532, 453–8. https://doi.org/10.1038/nature17637.CrossRefGoogle ScholarPubMed
Jakobson, R. (1959). On linguistic aspects of translation. In Brower, R., ed., On Translation. Cambridge, MA: Harvard University Press, pp. 232–9.Google Scholar
Jiang, L., Stocco, A., Losey, D. M., Abernethy, J. A., Prat, C. S., and Rao, R. P. N. (2019). BrainNet: A multi-person brain-to-brain interface for direct collaboration between brains. Scientific Reports, 9, 6115. https://doi.org/10.1038/s41598-019-41895-7.CrossRefGoogle ScholarPubMed
Kosinski, M., Stillwell, D., and Graepel, T. (2013). Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences, 110, 5802–5. https://doi.org/10.1073/pnas.1218772110.CrossRefGoogle ScholarPubMed
Lebedev, M. A., and Nicolelis, M. A. L. (2006). Brain–machine interfaces: Past, present and future. Trends in Neurosciences, 29, 536–46. https://doi.org/10.1016/j.tins.2006.07.004.Google Scholar
Malmkjær, K. (2011). Meaning and translation. In Malmkjær, K. and Windle, K., eds., The Oxford Handbook of Translation Studies. Oxford: Oxford University Press, pp. 108–22. https://doi.org/10.1093/oxfordhb/9780199239306.013.0009.Google Scholar
Mecacci, G., and Haselager, P. (2019). Identifying criteria for the evaluation of the implications of brain reading for mental privacy. Science and Engineering Ethics, 25, 443–61. https://doi.org/10.1007/s11948-017-0003-3.Google Scholar
Moses, D. A., Leonard, M. K., Makin, J. G., and Chang, E. F. (2019). Real-time decoding of question-and-answer speech dialogue using human cortical activity. Nature Communications, 10, 3096. https://doi.org/10.1038/s41467-019-10994-4.Google Scholar
Musk, E., and Neuralink. (2019). An integrated brain-machine interface platform with thousands of channels (preprint). Neuroscience. https://doi.org/10.1101/703801.Google Scholar
Pais-Vieira, M., Chiuffa, G., Lebedev, M., Yadav, A., and Nicolelis, M. A. L. (2015). Building an organic computing device with multiple interconnected brains. Scientific Reports, 5, 11869. https://doi.org/10.1038/srep11869.Google Scholar
Pais-Vieira, M., Lebedev, M., Kunicki, C., Wang, J., and Nicolelis, M. A. L. (2013). A brain-to-brain interface for real-time sharing of sensorimotor information. Scientific Reports, 3, 1319. https://doi.org/10.1038/srep01319.Google Scholar
Schaeffer, M. J., and Carl, M. (2014). Measuring the cognitive effort of literal translation processes. In Germann, U., Carl, M., Koehn, P., Sanchis-Trilles, G., Casacuberta, F., Hill, R. and O’Brien, S., eds., Proceedings of the Workshop on Humans and Computer-Assisted Translation (HaCaT). Stroudsburg, PA: Association for Computational Linguistics, pp. 29–37.Google Scholar
Schaeffer, M. J., Dragsted, B., Hvelplund, K. T., Winther Balling, L., and Carl, M. (2016). Word translation entropy: Evidence of early target language activation during reading for translation. In Carl, M., Bangalore, S. and Schaeffer, M., eds., New Directions in Empirical Translation Process Research: Exploring the CRITT TPR-DB. Cham: Springer, pp. 183–210. https://doi.org/10.1007/978-3-319-20358-4.Google Scholar
Shannon, C. E., and Weaver, W. (1949). The Mathematical Theory of Communication. Urbana: University of Illinois Press.Google Scholar
Song, D., Chan, R. H. M., Marmarelis, V. Z., Hampson, R. E., Deadwyler, S. A., and Berger, T. W. (2009). Nonlinear modeling of neural population dynamics for hippocampal prostheses. Neural Networks, 22, 1340–51. https://doi.org/10.1016/j.neunet.2009.05.004.Google Scholar
Taylor, J. R., Williams, N., Cusack, R., Auer, T., Shafto, M. A., Dixon, M., Tyler, L. K., Cam-CAN and Henson, R. N. (2017). The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data repository: Structural and functional MRI, MEG, and cognitive data from a cross-sectional adult lifespan sample. NeuroImage, 144, 262–9. https://doi.org/10.1016/j.neuroimage.2015.09.018.Google Scholar