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informatics_future.pdf
- 1. The Future of Informatics How can Informatics be a new fundamental science? What’s the pedigree of this at Edinburgh? What good has this done lately? What’s carrying us forward? I’m talking from a personal viewpoint, although I believe these personal views should be widely held. Dave Robertson
- 2. Traditional Computer Science scientists observations observations predictions predictions theories theories Hypothesis formation and testing Analysis definitions definitions computer system
- 3. scientists specific system observations observations predictions predictions theories theories definitions definitions scientists specific system observations observations predictions predictions theories theories definitions definitions Broader View of Informatics scientists universal computation system observations observations predictions predictions theories theories Hypothesis formation and testing Analysis definitions definitions engineers application system observations observations predictions predictions theories theories definitions definitions
- 4. Example (1) scientists distributed processes process states process states valid states valid states process properties process properties Hypothesis formation and testing Analysis ambient calculus ambient calculus proteomics scientists protein structure structure predictions structure predictions service reliability service reliability data quality data quality service orchestrations service orchestrations
- 5. Example (2) scientists distributed processes process states process states valid states valid states process properties process properties Hypothesis formation and testing Analysis ambient calculus ambient calculus clinicians clinical decision support protocol behaviours protocol behaviours clinical advice clinical advice clinical protocols clinical protocols protocol enactment protocol enactment
- 6. Example (3) scientists distributed processes process states process states valid states valid states process properties process properties Hypothesis formation and testing Analysis ambient calculus ambient calculus cellular biologists cell signalling cell signals cell signals cell responses cell responses cell behaviours cell behaviours signalling pathways signalling pathways
- 7. Classical Period (1965-1984) Growing roots: • State • Logic
- 8. Eclectic Period (1985-1999) • Many sub-fields • Many institutes Vigorous growth:
- 9. Modern Period (2000-2007) Going large: • Internet scale • Ubiquitous
- 10. Post-Modern Period (2008+) Open minimalism: • Common core • Computational scholarship • Symbiosis with other sciences
- 11. Examples of Recent Impact Transformation Adaptation Design Concurrency Parsing Synthesis Learning Planning Control Perception Data Types XML databases Java generic types Iterative compilation On-demand multipath routing for ad hoc networks Design of high-performance low-power micro-architectures Algorithmic skeletons for parallel computation Combinatory Categorical Grammar Festival modular speech synthesis toolkit Gaussian processes Proof planning Humanoid robotics 3D imaging Complexity Game theory
- 12. Scale • Numbers of nodes • Numbers of people • Data at the edge • Bandwidth • Computing resource • Service complexity • Device connectivity ( ) Time Level where centralising becomes impractical Complexity factors in the global network:
- 13. Semantics of single knowledge base Hybrid architecture Semantics across architecture Open system of reasoners Semantics via interactions 1990 2010 An Effect of Scale on Theory
- 14. An Effect of Scale on Applications • Automation is necessary to get most things to work. • Lack of background knowledge for reasoners isn’t a showstopper. • New internet environments bridge physical and virtual worlds. • Many traditional algorithms don’t scale. • Traditional boundedness assumptions no longer apply. • No single sub-field matches these new environments. Opportunities Problems
- 16. Examples of Responses to Scale Using scale as a fulcrum Statistical machine translation; Web as a corpus Peer to peer Agent systems; Web service choreography Guarantees in open systems Proof carrying code Provenance of data Digital curation Statistical complexity Random structures and algorithms
- 17. Scale-driven Application Challenges • Healthcare: We know that centralised architectures can’t scale to the demands of current healthcare. Can decentralised architectures? • Emergency response: Every device that can communicate could potentially be used in coordinating emergency responses. How? • Data analysis: The Large Synoptic Survey Telescope will produce 30 TB of data per night. What is the architecture of the system(s) to analyse this data set?
- 18. The Future of Informatics Informatics is a fundamental science because it makes sense to study the information (and information processing characteristics) of every natural system. It is a new science because the core methods for doing this aren’t in the domain of traditional sciences. We (including you as alumni) supplied a substantial part of the foundations for this; not only the technical stuff but the ethos that allowed the science to mature. In the process we’ve been useful, to our fellow scientists and to society. As we have gained confidence in our own science we have been better able to engage with others. We are now being carried forward by critical mass we’ve obtained (size does matter in this respect) and by the sheer scale of the problems for which an informatics approach is the only solution.