YEARS

2013-2014

AUTHORS

Kate Elizabeth Jones, Mark Girolami, Gabriel Julian Brostow

TITLE

ENGAGE : Interactive Machine Learning Accelerating Progress in Science, An Emerging Theme of ICT Research

ABSTRACT

Our vision is to establish and lead a new theme in ICT research based on Interactive Machine Learning (IML). Our expansion of IML will give scientists and non-ICT specialists unprecedented access to cutting-edge Machine Learning algorithms by providing a human-computer interface by which they can directly interact with large scale data and computing resources in an intuitive visual environment. In addition, the outcome of this particular project will have a direct transformative impact on the sciences by making it possible for non-programming individuals (scientists), to create systems that semi-automatically detect objects and events in vast quantities of A) audio and B) visual data. By working together across two parallel, highly interconnected streams of ICT research, we will develop the foundations of statistical methodology, algorithms and systems for IML. As an exemplar, this project partners with world leading scientists grappling with the challenge of analysing enormous quantities of heterogeneous data being generated in Biodiversity Science.

FUNDED PUBLICATIONS

  • articles:433c17817f9413dda305d53c45a14f34
  • Markov Chain Monte Carlo from Lagrangian Dynamics.
  • How to use: Click on a object to move its position. Double click to open its homepage. Right click to preview its contents.

    Download the RDF metadata as:   json-ld nt turtle xml License info


    26 TRIPLES      17 PREDICATES      27 URIs      10 LITERALS

    Subject Predicate Object
    1 grants:09fd18566cb1c6fbe8b79a29393a0540 sg:abstract Our vision is to establish and lead a new theme in ICT research based on Interactive Machine Learning (IML). Our expansion of IML will give scientists and non-ICT specialists unprecedented access to cutting-edge Machine Learning algorithms by providing a human-computer interface by which they can directly interact with large scale data and computing resources in an intuitive visual environment. In addition, the outcome of this particular project will have a direct transformative impact on the sciences by making it possible for non-programming individuals (scientists), to create systems that semi-automatically detect objects and events in vast quantities of A) audio and B) visual data. By working together across two parallel, highly interconnected streams of ICT research, we will develop the foundations of statistical methodology, algorithms and systems for IML. As an exemplar, this project partners with world leading scientists grappling with the challenge of analysing enormous quantities of heterogeneous data being generated in Biodiversity Science.
    2 sg:endYear 2014
    3 sg:fundingAmount 674580.0
    4 sg:fundingCurrency GBP
    5 sg:hasContribution contributions:0b867400de1fb29466cfde6723524846
    6 contributions:d4e0ec7b19b42adcdc3435976151ba52
    7 contributions:f94950a0d5c9e723f372c7ede7aa3e63
    8 sg:hasFieldOfResearchCode anzsrc-for:01
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    14 sg:hasFundedPublication articles:433c17817f9413dda305d53c45a14f34
    15 articles:fbe3949269e6b894efd33f4c3f5bc59d
    16 sg:hasFundingOrganization grid-institutes:grid.421091.f
    17 sg:hasRecipientOrganization grid-institutes:grid.83440.3b
    18 sg:language English
    19 sg:license http://scigraph.springernature.com/explorer/license/
    20 Contains UK public sector information licensed under the Open Government Licence v2.0 (http://www.nationalarchives.gov.uk/doc/open-government-licence/version/2/).
    21 sg:scigraphId 09fd18566cb1c6fbe8b79a29393a0540
    22 sg:startYear 2013
    23 sg:title ENGAGE : Interactive Machine Learning Accelerating Progress in Science, An Emerging Theme of ICT Research
    24 sg:webpage http://gtr.rcuk.ac.uk/project/FC2AB483-BAF1-4068-98A1-D34558490149
    25 rdf:type sg:Grant
    26 rdfs:label Grant: ENGAGE : Interactive Machine Learning Accelerating Progress in Science, An Emerging Theme of ICT Research
    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular JSON format for linked data.

    curl -H 'Accept: application/ld+json' 'http://scigraph.springernature.com/things/grants/09fd18566cb1c6fbe8b79a29393a0540'

    N-Triples is a line-based linked data format ideal for batch operations .

    curl -H 'Accept: application/n-triples' 'http://scigraph.springernature.com/things/grants/09fd18566cb1c6fbe8b79a29393a0540'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'http://scigraph.springernature.com/things/grants/09fd18566cb1c6fbe8b79a29393a0540'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'http://scigraph.springernature.com/things/grants/09fd18566cb1c6fbe8b79a29393a0540'






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