Informatics is a natural science
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I argue why I think that Computer Science (or better: Informatics) is a "natural science", in the same sense that physics, astronomy, biology, psychology and sociology are a natural science: they study a part of the world around us. In that same sense, I think Informatics studies a part of the world around us. For a similar talk (including script), but more aimed at a Semantic Web audience in particular, see http://www.cs.vu.nl/~frankh/spool/ISWC2011Keynote/ (or http://videolectures.net/iswc2011_van_harmelen_universal/ for a video registration)
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Informatics is a natural science
- 1. Informatics is a natural science Frank van Harmelen Dept. of “Computer Science” VU University A’dam Creative Commons License: allowed to share & remix, but must attribute & non-commercial
- 2. Health Warning: This is going to be a pretentious talk
- 4. Philosophical confession coming up: What I believe about scientific knowledge
- 6. Computer “Science” ? "Computer science is no more about computers than astronomy is about telescopes” -- Edsger W. Dijkstra
- 7. just like laws about the physical universe?
- 9. alchemy
- 10. Some examples of “laws” from the information universe (and sometimes: from the SIKS part of that universe)
- 11. Zipf’s law
- 13. Some proposed laws from the SIKS part of the Information Universe
- 14. Factual knowledge is a graph
- 15. Terminological knowledge is a hierarchy
- 17. Dataset Schema closure Full closure Ratio Linked Life Data 332sec 1h5min 10 FactForge 89sec 2h45min 100
- 18. The role of the human observer?
- 19. Many more laws, about: • Abstraction, Information Hiding, Layering • Simulation, Universality, Virtualisation • Tractability, Computability Are these the only examples?
- 20. Is this a weird position? "Informatics is the study of the structure, behaviour, and interactions of natural and engineered computational systems." Three of the truly fundamental questions of Science are: "What is matter?", "What is life?" and "What is mind?".
- 21. Is this even controversial? "Underlying our approach to this subject is our conviction that computer science is not a science” Mathematics provides a framework for dealing precisely with notions of "what is." Computation provides a framework for dealing precisely with notions of "how to"
- 22. Is this new? Bill Rapaport’s page with a map of 45(!) years of debate
- 23. Is this even important? It changes our “ontology” of CS! • A computer is a result • A programming language is a result • An algorithm is a result • A computer is a result A computer is an experimental instrument • A programming language is a result A programming language is an experiment • An algorithm is a result An algorithms is an observation
- 24. Is this even important? • It changes how PC’s and editorial boards think • It changes how you teach your courses • It changes how you train your PhD’s • It changes how you judge a PhD thesis • It changes how other fields perceive “CS” • It changes how the general public perceive “CS”
Editor's Notes
- Move this after the Naughton slide
- - look back at 10 years of Semantic Web no point in arguing what engineering feats we have achieved (just look around you, quotes from SemWeb GoodNews Quiz), but rather: did we _learn_ any permanent, generic, scientific knowledge. - after 10 years of Semantc Web research, which stable patterns did we find? - stable pattern = if you did the whole thing again, which patterns would you find again (and again, and again), vs. incidental patterns. - science = finding the stable patterns (examples from physics?) "laws", "principle of recurrent discovery"
- Move this after the Naughton slide
- - I believe that information has inherent structure & properties, and that there are laws that govern these structures & properties. - I believe we can discover these laws (just like we can discover physics laws). - thus: just like the physical universe "exists out there" (and is not just a mental or social or cultural construction, so is the information universe "out there" (and is not just a mental or social or cultural construction. - Of course, many of the actual objects in the physical universe are our own construction (billiard balls, space ships, people), but the _laws_ that govern these objects are not just mental/social constructs, these laws are "objective", "real", they are "out there to be discovered". In the same way, the actual objects in the informational universe are - our own constructs (programs, databases, languages), but the _laws_ that govern these objects are not just mental/social constructs, these laws are "objective", "real", they are "out there to be discovered". - Compare with "mathematical realism": humans do not invent mathematics, but rather discover it, and any other intelligent beings in the universe would presumably do the same. - In the same, I'm a "informational realist": humans do not invent the structures and properties of information, but rather discover it, and any other intelligent beings in the universe would presumably do the same.
- - Compare to physics laws: gravity F = G m_1 m_2 / r^2 conservation of energy (dE/dt = 0), increase of entropy (dS/dt \geq 0), we cannot yet hope for such beautifully mathematised laws, in such a concise language that fits on a very compact space computer science is like alchemy, a "protoscience"
- explain more about alchemy, it was not just a failure to turn lead into gold, it was a protoscience, searching for proper goals, proper ocnceptual framework (think of some useful contributions that still stand, developed lots of experimental apparatus)
- Some known information laws already apply: Zipf law / long tail distributions are everywhere = vast majority of occurrences are caused by a vast minority of items this phenomen is sometimes a blessing, sometimes a curse nice for compression awful for load balancing and knowing the law helps us deal with the phenomenon that’s why it’s worth trying to discover these laws.
- Another known information also applys: Use vs reuse: use = 1 - re-use (of course don’t take linear form literally) lesson from ontologies Law of conservation of mysery, you can’t have it both ways
- How does the universality of this compare to DB and logic Law: some forms of information come in graphs. weaker form: graph knowledge is one of the dominant forms (comporable to logic & DB’s). significant classes of problems & data that apparently come with their dominant/preferred form. Universal question: what factors determine the shape.
- here many fewer competitor forms, more universal agreement, much more repeated invention, makes this a much stronger law
- this only works because terminologies are in general only simple hierarchies. (it’s easy to build examples where this doesn’t hold, but in practice it turns out to hold). So, this law depends on the previous law as an aside: the graph is now big enough to do statistics on it.
- use complexity” as a measure, not just “size”. spell out LLD, don’t break FactForge
- Health Warning: in general hard to distinguish the “real” laws about the external universe from cognitive artifacts and historical bias (but that doesn’t imply that all laws are only fictions of our culturally biased imaginations).
- My hope for this talk: you might agree with some of my observations, and disagree with some other, or even disagree with all of them, but at I hope that at least I will have prompted you to start thinking about these patterns: what are the patters that we see, are they real laws? This is an invitation, and also a challenge to future programme committees and editorial board. and also a challenge to you: of course we won’t redo the 10 year experiment, but think in this way when you write your papers: try to separate the incidental choices from the fundamental choices.