Presentation given at Datasalon #6 in Brussels (2011). It presents a review on the article by R.D. King "Rise of the Robo Scientists" and some afterthoughts on the nature of data.
The article by R.D. King appeared in Scientific American: Vol. 304 (2011) pp. 72-77. DOI: 10.1038/scientificamerican0111-72
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Datasalon6 2011 - "Rise of the robo scientists": where is data coming from?
1. Rise of the RoboScientists:where is data coming from?Pieter PauwelsDatasalon #621th January2011BOZAR, Brussel
4. The (boring) detailsWhat: The Robot Scientist ProjectWhen: 1999 ongoingWhere: AberystwythUniversity Wales & Cambridge UniversityEnglandWho: Adam (5m x 3m x 3m) & EveWhy: instead of merelycreating a deluge of data for the scientist, Adam aims at activelyhelping in the experimental research of microbiologiststhrough hypothesis generation and testing
5. Science 3 April 2009: Vol. 324 no. 5923 pp. 85-89 DOI: 10.1126/science.1165620 Scientific American 17 January 2011: Vol. 304 pp. 72-77 DOI: 10.1038/scientificamerican0111-72
8. The Robot Scientist makes use of an iterative approach to experimentation, where knowledge acquired from a previous iteration is used to guide the next experimentation step. This is a process known as Active Learning, where the learner can plan its own agenda, i.e. decide how best to improve its knowledge base and how to go about acquiring this information. The Robot Scientist uses the laboratory robot to execute the experiment(s) selected as most informative; has a plate reader to analyse the experiments, generating data corresponding to the scientific observations; uses abductive logic programming to generate valid hypotheses that explain the observations; and uses these hypotheses to determine the next most informative experiment. At the beginning of any investigation, the Robot Scientist has not discovered any information, therefore all possible hypotheses are equally valid. As the directed discovery process continues, each new observation (or experiment/interpretation cycle) will invalidate some of the hypotheses, thereby excluding incorrect discoveries. The experiment selection process aims to choose the experiment most likely to refute the most hypotheses. This iterative process allows irrelevant experiments to be avoided, potentially saving both laboratory time and the cost of using unnecessary reagents and biological materials.Quote from: http://www.aber.ac.uk/en/cs/research/cb/projects/robotscientist/
13. the process of scientificenquiry(cfr. C.S. Peirce)Image from: Flach and Kakas. Abductive and InductiveReasoning: Background and Issues. In: Abduction and Induction: Essays ontheirRelation and Integration. KluwerAcademicPress, pp. 1-27, 2000.
14. the process of scientificenquiryLOGICabduction (1)ObservationConstruct / revise set of hypothesesinduction (3)deduction (2)Analyse experimental resultsMake predictions / Devise experimentsDo experiment(s)
16. Example 1Abduction:Result: grass is wetRule: it has rained -> grass is wetCase: it has rainedDeduction:Rule: it has rained -> grass is wetCase: it has rainedResult: grass is wetInduction:Case: it has rainedResult: grass is wetRule: it has rained -> grass is wet
17. Example 2Abduction:Result: grass is wetRule: it has rained -> grass is wet sprinklers are on -> grass is wet=> Hypothesis: it has rainedDeduction:Rule: it has rained -> pluviometer is fullCase: it has rained=> Prediction: pluviometer is fullExperimentInduction:Case: it has rainedit has rainedResult: grass is wet pluviometer is full=> Rule: it has rained -> grass is wet it has rained -> pluviometer is full