Transparent Government: The Stories we can Tell with Data

schedule May 7th 01:30 - 02:00 PM place Green Room people 50 Interested

There is an increasing and powerful global push to open up the trove of information governments generate, collect, and manage. There is a vast array of data ranging from; open data, big data from multiple sources, sensitive information about citizens, and complex information about businesses interactions with government, such as contracts and procurement, taxes and royalties.

This creates many opportunities to use this data to tell stories. To help citizens and the private sector understand what is happening across governments, means not just access to this data, but tools that allow people to dig through, analyse it, use diagrams and maps to make sense of it and to connect information in a way that is understandable, engaging and useful to the general public.

This talk shows some of the possibilities available today and looks at what may be possible as governments around the world open up. It shows some of the ground-breaking work done in Australia. The talk touches on issues around transparency, accountability, systems of protection, data ethics frameworks, and ultimately how to build trust.

Throughout, we'll see some of the work by Nook Studios building systems for government, including the ground-breaking Common Ground mining title information system, as well as tools that help link and connect information in a meaningful way using data pathways.

 
2 favorite thumb_down thumb_up 0 comments visibility_off  Remove from Watchlist visibility  Add to Watchlist
 

Target Audience

Data enthusiasts

schedule Submitted 2 months ago

  • Liked Mat Kelcey
    keyboard_arrow_down

    Mat Kelcey - Practical Learning To Learn

    30 Mins
    Talk
    Advanced

    Gradient descent continues to be our main work horse for training neural networks. One recurring problem though is the large amount of data required. Meta learning frames the problem not as learning from a single large dataset, but learning how to learn from multiple related smaller datasets. In this talk we'll first discuss some key concepts around gradient descent; fine-tuning, transfer learning, joint training and catastrophic forgetting and compare them to how simple meta learning techniques can make optimisation feasible for much smaller datasets.