 |
|
 |
MARI News Press Releases Blogs&Lists About Us Newsletter Disclaimers |
Computers and Science
Course: OEAS 795 (one credit), CRN 24921
Course title: Computers and Science
Instructors: Dr. Hans-Peter Plag
Term: Spring 2017
Time: Wednesday, 1:00-1:50 PM
Location: OEAS Class room
Class Pages
- Class (01/18/2017)
- Class (01/25/2017)
- Class (02/01/2017)
- Class (02/08/2017)
- Class (02/15/2017)
- Class (02/22/2017)
- Class (03/01/2017)
- Class (03/15/2017)
- Class (03/22/2017)
- Class (03/29/2017)
- Class (04/05/2017)
- Class (04/12/2017)
- Class (04/19/2017)
A number of publications will be available as PDF for use in the class on the workspace.
- Class 1 (01/18/2017): Introduction and Time Line
Summary
There is a long history of computer science, which started more than 4,000 years ago. There was an interplay of computational alogrithms and mechanical means as well as human computers that impacted the speed and extent of possible compitations. However, a rapid development took place after the development of a general concept for a computer by Turing and the introduction of electronic computers. There is abundant literature on the history of computer science and the development of computers. However, there is relatively little consideration of the impact of computers on science, epistemology, and the creation of knowledge.
Reading List
Wikipedia, 2016. History of computer science. https://en.wikipedia.org/wiki/History_of_computer_science.
Zimmermann, K. A., 2015. History of Computers: A Brief Timeline. Live Science, September 8, 2015. http://www.livescience.com/20718-computer-history.html
Shallit, J., 1995. A Very Brief History of Computer Science. https://cs.uwaterloo.ca/~shallit/Courses/134/history.html
Wing, J., 2014. Impact of Computer Science Research on Science, Technology, and Society. https://www.microsoft.com/en-us/research/video/impact-of-computer-science-research-on-science-technology-and-society/?tduid=(d53164134a10168660d598627dbaaac0)(256380)(2459594)(TnL5HPStwNw-.Rbzer3V4tdQPfDIWxZmjw)()
Malina, R., 2010. The Impact of Computers on the Arts, Sciences and Humanities. http://malina.diatrope.com/2010/03/23/the-impact-of-computers-on-the-arts-sciences-and-humanities/
- Class 2 (01/25/2017): Philosophy of Computer Science and Epistemology of Information Science
Summary
Interestingly, there seems to be very little literature addressing the impacts of computers on epistemology, ontology, and methodology. There is an abundance of literature on the question of whether computers can exhibit intelligent behaviour and on the potential benefits and dangers of artificial intelligence. Turing's (1950) paper is an important milestone providing a test for intelligent behaviour of a machine. With respect to the second question, the book by Bostrom (2014) provides a valuable reference. It will be interesting to study how computers change the way scientific experiments are conducted and how research is carried out. Did computers change the epistemology as laid out in Kuhn
Reading List
Capurro, R., 1999. Epistemology and Information Science. http://www.capurro.de/trita.htm.
Vamos, T., 1991. Computer Epistemology - A Treatise on the Feasibility of the Unfeasible or Old Ideas Brewed New. World Scientific Series in Computer Science: Volume 25. http://www.worldscientific.com/worldscibooks/10.1142/1203
Turing, A., 1950, Computing Machinery and Intelligence, Mind, LIX (236): 433-460, doi:10.1093/mind/LIX.236.433, ISSN 0026-4423.
Bostrom, N., 2014. Superintelligence - Paths, Dangers, Strategies. Oxford University Press.
- Class 3 (02/01/2017): Computer Models and the real world
Summary
A main use of computers in science is in modeling. Computer models play an increasing role in exploring both theories and the real world, including possible futures. This poses the challenge of how such models can be verified or validated. What is the benefit of models?
Reading List
Oreskes, N., Shrader-Freshette, K., Belitz, K., 1994. Verification, validation and confirmation of numerical models in the Earth sciences, Science, 263, 641-646.
Epstein, J., 2008. Why Models. JASSS, 11, 4, 12. See http://jasss.soc.surrey.ac.uk/11/4/12.html.
Gail, W. B., 2016. A new Dark Age Looms. The Opinion Page, New York Times, April 19, 2016. html.
Anderson, J., 2017. Availability Bias: The psychology of why 94 deaths from terrorism are scarier than 301,797 deaths from guns. Quarz, January 3, 2017. html.
- Class 4 (02/08/2017): Scientific Revolutions
Summary
The nature of scientific revolutions was addressed by Thomas Kuhn (1962). He found that the traditional view, in which science collects objective facts towards and improved veiw of thee world is not correct. Instead, what scientists find depends strongly on the questions they ask. Thus, if the availability of computers and the ever increasing computer power and data availability changed the questions science asked, then it has a significant impact on science.
Reading List:
Glass, G., 2013. How Science Works - an overview of the philosophy with particular emphasis on Karl Popper and Thomas Kuhn. Video available at https://www.youtube.com/watch?v=g04D24HfW18.
Rees, M. C., 2012. The Structure of Scientific Revolutions at Fifty, The New Atlantis: A Journal of Technology and Society, Fall 2012, see here. See here.
Wikipedia, 2017. The Structure of Scientific Revolutions. html.
Pajares, F., 20??. The Structure of Scientific Revolutions - outline and study guide. html.
Forster, M. R., 1998. Guide to Thomas Kuhn's The Structure of Scientific Revolutions. html.
Kuhn, T, 1962. The structure of scientific revolutions. Available as pdf. Classic history of science essay that defines normal science and how paradigm shifts redefine science so that what was before is now unscientific. Defines science problems and standards, and communication techniques within science.
- Class 5 (02/22/2017): The role of philosphy, epistemology in a time of information overflow
Summary
Computers provided a basis to interconnect information on a global scale through Internet and World Wide Web, which changed the relationship of humans to information fundamentally. The transition from local access to information with large time delays in access to information from distant locations to immediate access to almost all information globally made selection of information more important than more access to information. This is also true for science.
With the advent of social media and social communications, a new connectivity has been created where minds distributed globally can have immediate impact on other minds. This creates a new complex system with the emerging properties not even partially anticipated. What is the impact of this on science?
Reading List
Resilience, 2017. The world as representation. html.
Bergstrom, C. T., West, J., 2017. Calling Bullshit in the Age of Big Data - Syllabus. html.
- Class 6 (02/22/2017): From local knowledge creation to a global brain
Summary
If we considered humanity as a brain with 7,5 billion neurons tightly connected and interacting with each other, what could be the emerging properties of this global brain?
Reading List
Herculano-Houzel, S., 2017. Lessons from Making Brain Soup. Scientific American Mind, XXVIII(2), 36-41. https://www.scientificamerican.com/article/lessons-from-making-brain-soup/.
Class discussion:
- Class 7 (03/01/2017): A computer-caused shift in science to "virtual" empiricists?
Summary
So far, we have identified that philosophical questions have moved into the background to some extent. The philosophical crisis identified by J.M. Greer for all cultures, that leads to the epistemic question of what we can really know, seems to have been put to the side and "epistemic modesty" has been replaced by a high trust in numerical models. Understanding planetary dynamics and the past, current and possible future changes of the Earth system is approached by using models that have been made coherent with past data to explore the future. Different models provide very different answers about possible futures, and using a significant number of different models is consider a good approach to explore the future. Thus, scientists explore a "virtual" future as empiricists. Is there an epistemological basis for this approach? If so, what is the basis?
In many aspects, the actual trajectory of the planetary system deviates significantly from the "most probable" trajectory found in exploring virtual futures. What is the implication of this deviation of the "thing itself" from the virtual reflections provided by numerical models?
Can we use mathematical models implemented in numerical schemes to explore the future? Today, more than ever before, studying the future and preparing for what may come is of existential importance. Before the Enlightenment, religion was the main source of humanity's knowledge of the future supplemented by a large dose of superstition. In the modern world, science plays an important role in exploring possible futures, and science introduced the concepts of mathematical extrapolation and prediction for this purpose. That approach was very valuable in times when the planet was in a rather stable homeostasis. But changes today are 100 to 1000 times faster than throughout the Holocene before about 1900, and simple extrapolation does not work for a highly dynamic system with a complex and increasingly novel future. How can we create knowledge about the future in such a case? Do we need to replace "prediction" by "foresight" and "we know" by more "epistemic modesty"?
A side comment, but an important one on education is that most educational efforts nevertheless are still equipping the students to explore and deal with the future in this century with the tools developed and valid during the last few centuries. It would be important for every student to have a chance to take courses on foresight and studies of futures with the goal to enable them to handle rapid change in the world that they will live in rather than predicting the tranquil path of the stable world that their parents and teachers lived in.
Reading List
Greer, J. M., 2017. A muddle of mind and matter. The Archdruid Report. http://thearchdruidreport.blogspot.com/.
- Class 8 (03/15/2017): Impacts of Data Overload on Reasoning and Science
Summary
Reading List
See the papers uploaded by Brett to the workspace.
- Class 9 (03/22/2017): Development of Computers
Summary
The development of computing is a parallel development of hardware and software. For hardware, competing developments wwer between large and powerful mainframes (like IBM and Grey computers) and smaller work stations like HP 1000. The Digital Equipment VAX was somewhere in between contributing to the proliferation of local computing power in the 1980ies and early 1990ies. The rapid development of personal computers into powerful work stations and networking of these work stations led to more and more distributed computational capacity. Cloud and grid computing gave even more access everywhere.
Languages and operating systems developed rapidly with operating systems often built around core software packages. Examples in the scientific domain are Mathlab, statistical analysis software, and agent-based modeling packages. While there are a few dominating packages, there is also a proliferation of packages with a number of compatibility issues. Interestingly, very often the most successful packages are not necessarily the best ones.
Reading List
The modern history of computers. https://plato.stanford.edu/entries/computing-history/.
10 Developments that changed the face of computing. html.
Wikipeda, html.
- Class 10 (03/29/2017): Development of Programming Languages
Note: This class has been moved 04/05/2017
- Class 11 (04/05/2017): ...
Summary:
Reading List
Tanenbaum, A. S., 2002. A History of Operating Systems. http://www.informit.com/articles/article.aspx?p=24972&ranMID=24808.
Unknown. Operating System Documentation Project. http://www.operating-system.org/index.html
Wikipedia: https://en.wikipedia.org/wiki/History_of_programming_languages and the sources therein.
Unknown. Computer History: Tracing the History of the Computer - History of Operating Systems. http://www.computernostalgia.net/articles/HistoryofOperatingSystems.htm.
Unknown. Computer History: Tracing the History of the Computer - History of Programming Languageshttp://www.computernostalgia.net/articles/HistoryofProgrammingLanguages.htm
Diakopoulos, N., and Cass, S., 2016. Interactive: The Top Programming Languages 2016. Find the programming languages that are most important to you. IEEE Spectrum. http://spectrum.ieee.org/static/interactive-the-top-programming-languages-2016, Posted 26 Jul 2016 | 16:00 GMT.
Cass, S., 2016. The 2016 Top Programming Languages - C is No. 1, but big data is still the big winner. IEEE Spectrum. http://spectrum.ieee.org/computing/software/the-2016-top-programming-languages?utm_source=TechAlert&utm_medium=Email&utm_campaign=TechAlert_07-28-16&bt_email=hpplag@tiwah.com&bt_ts=1469711217105, Posted 26 Jul 2016 | 16:00 GMT.
- Class 12 (04/12/2017): ...
Summary
Reading List
- Class 13 (04/19/2017): ...