Archive for the ‘data science’ Category

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Caitie on Einstein A Go Go

October 27, 2019

Caitie and Wray at 3RRR

Caitie Doogan, through her Twitter activies, got invited to talk on a science radio programme on 3RRR:  Einstein A Go Go – 27 October 2019 (our section starts at 25:15 in). Dr Krystal, Dr Ray & Dr Shane were the three scientists asking really relevant questions.  Caitie’s work is at the interface of applications and machine learning, so is far more accessible to the science public.  I came along for the ride, and discussed my work with Turning Point.

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Turning Point and Monash at Google

July 31, 2019

Turning Point colleague, Sam Campbell, and I went to the Google AI Impact Challenge Accelerator event in London on 29th-31st July (see the video here, I get a brief shots at 0:14 and 0:20 and Sam gets an interview at 0:49).  It was an in depth technology review so we could help design our system.  We had a number of very clued-in Google experts supporting us in designing our architecture.  My systems and internet applications experience is over 20 years old, so I get the general ideas but don’t know the modern specifics!

Lots the of AI Impact folks from Google attended and we all agreed the committment and support from Google was fabulous.  We’re in the top right, with Sam holding the “Turning Point” sign, me below him.

AI Impact Challenge Accelerator Tech Sprint

All the teams at the London tech event, 31/07/19.

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AI suicide surveillance with Turning Point

June 23, 2019
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Wray, Dan and Debbie at Google Launchpad, 15/05/2019

Along with Prof. Dan Lubman’s team at Turning Point, and funded through Google’s innovative AI for Social Good programme, I’ll be developing an AI system to accelerate “coding” (a form of content analysis) of ambulance records so we can understand the nature of suicide in Australia.   The local press has it so:

Google taps local addiction service to build AI suicide surveillance system

As the Google blurb says:

By using AI tools to analyze these records, Turning Point, a national center within Eastern Health, will uncover critical suicide trends and potential points of intervention to better inform policy and public health responses.

For us researchers, it means unifying a bunch technologies that I’ve been working in for a while like active learning, multi-label classification, multi-task learning and crowd-sourcing.  But most importantly, all these need to be placed in the context of​ doing accurate and properly monitored coding while at the same time trying to minimise costly expert (human) effort.  This is important stuff for NGOs and health organisations so we’re really excited by the application opportunities this can give us all.

In mid May Dan Lubman, Debbie Scott and I flew off to Google’s Lauchpad Space in San Francisco to spend a week with other members of the programme for a bootcamp, to brainstorm about our project and get coaching from Google’s experts.  Google has a lot of other plans for us to, in terms of supporting the development, which we are very grateful for!

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Fabulous data science tag cloud

June 2, 2018

This comes from PhD student Caitlin Doogan.

Tag Cloud on Data

Tag Cloud on Data by Caitlin Doogan

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Graduating MDS students

May 25, 2018

Our first larger batch of MDS students graduating.   Here are some who attended the ceremony.  Really great students!

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MDS Graduation May 2018

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The Big Tech Healthcare Invasion

April 25, 2018
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The Big Tech Healthcare Invasion Infographic

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Facebook and Data Science

April 6, 2018

My favorite topics in teaching, other than Bayesian statistics (“of course”), are about interesting applications, ethics and impact to society.  One of the things I always do is point out that many of the big technology companies are fundamentally “data” companies selling their consumer data to advertisers.  Lots of gnarly ethical issues here.  But the huge sleeper issue in all this is medical informatics where medical research really needs consumer lifestyle data if it wants to make major breakthroughs in the lifestyle diseases that are gradually strangling the Western economies.  Even gathering lifestyle data is difficult (think diet, for instance), let alone dealing with the ethics and privacy involved.

Anyway, great piece by Jennifer Duke “You’re worth $2.54 to Facebook: Care to pay more?” in the Australian press today (SMH, The Age).  We had an insightful 15 minute discussion on the phone on Friday and I managed to get a worthy quote in her article.  Impressed by her broad knowledge of the topics.  Good to see our journalists know their stuff.

Reuters has an extensive piece outlining details and the election influence, Cambridge Analytica CEO claims influence on U.S. election, Facebook questioned.

The Conversation seems to be surfing the media hub-bub with a dozen or more articles from the academic community in the last week or so.  Here are some that caught my eye.

Some other background articles are:

  • An older article in the Huffington Post, Didn’t Read Facebook’s Fine Print? Here’s Exactly What It Says, and commenting on an older terms of service, but a lot still applies.

  • The Australian Government has fairly strong privacy laws under the Privacy Act and its amendments.  This is described at Guide to securing personal information, which has a broad definition of personal information that probably covers most of what Facebook keeps.  Though a special class of information, sensitive information, which includes medical and financial details, silent phone numbers, etc., and requires a higher level of protection.
  • In 2014 Cambridge researchers Kosinski, Stillwell and Graepel published an article on PNAS (Proc. National Academy of Sciences of the USA) showing Private traits and attributes are predictable from digital records of human behavior.  If that sounds too technical, the short version is:
    If your a frequent user, Facebook probably knows your religion, sexual preferences and any serious diseases you might have, and major personality traits, even if you take great care not to expose them.
    Keep in mind this was the best known of a long series of research.  When this information is inferred (i.e., predicted using a statistical algorithm) it is called implicit information.
  • Note Facebook has been relaxing their privacy default settings over the years, The Evolution of Privacy on Facebook, according to Matt McKeon.  This makes their job of monetising their users easier.
  • Note it is not clear what the Privacy Act says about implicit information.  Note implicit information can be very hard to extract, and require access to a fuller database to make inferences.

Personally, I believe online data privacy will evolve in fits and bursts, but there are a lot of technical hurdles.  Online advertising, for instance, needs to turn around impressions at great speed and doesn’t have time to work through complex APIs so I suspect they will need the personal data in some form on their own servers.  Sounds like a perfect application for cryptosystems to me, if it can be made fast enough.  As for data harvesting, well, I expect that will go on forever.