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Artificial Intelligence To Solve Complex, Real-world Problems


And there is a lot of fascinating things taking place thanks to the power of AI, from conservationists preventing illegal logging in the Amazon rainforest with AI – and researchers partnering with astronomers hunting new planets with machine learning.

The potential of AI to solve complex, real-world problems is huge.

To help more people tackle challenges with AI, we’ve open-sourced machine learning tools like TensorFlow, we help others innovate with Cloud AI – and collaborate with researchers around the globe.


And here in Australia, researchers, developers and businesses are using these AI tools to solve difficult problems in the fields of health, conservation, linguistics and more. Today, we celebrated some of these stories with an event in our Sydney office, to show how AI is driving impact in extraordinary, unexpected and tangible ways––here and now.

A Snapshot of Aussie AI-powered products and projects:

Saving dugongs with AI

Google AI: Saving dugongs with AI
Google AI; Saving dugongs with AI Pic credit: Ahmed M Shawk

Dugongs are the gentle giants of the sea and despite their size, are hard to keep track of. This has presented a challenge for conservation researchers working to save this endangered species.

For decades, scientists had to spend days peering out of small planes to count populations, which was expensive, time consuming and often hazardous.

Researchers then analysed imagery manually, zooming in to count dugongs one-by-one.

Dr. Amanda Hodgson of Murdoch University and Dr. Frederic Maire of Queensland University of Technology knew there must be a better way.

In 2010, they began testing drones, which take aerial photography of the ocean––and in 2014, they applied the magic of machine learning in their quest to make the processing of images from drones faster and cheaper, and therefore make drone surveys a realistic option.

Using TensorFlow, Google’s free open source machine learning platform, the team built a detector that could learn to find dugongs in these photos automatically.
mapping Dugongs
Mapping Dugongs

So far, the team have processed 37k+ images––identifying 70% of the sea cows they’d found manually in images. This analysis took 18 hours to complete, compared to the 377 hours required for manual analysis. Hodgson and Frederic have now integrated this detection software with mapping software to plot all sightings, giving them richer data about the volume and locations of dugongs.

Preserving precious indigenous languages

Professor Janet Wiles talked about the challenges of preserving the indigenous languages in Australia of which there are many.

“We are in a race against time to support our indigenous languages,” said Professor Wiles.


While there are 6,000+ languages in the world, and more than 50% of web content is in English. AI can help us make this content accessible, break down language barriers and even preserve endangered languages. Since 2012, Google’s language technology teams have been using neural networks to make the world’s diverse language content universally accessible and useful.

Professor Janet Wiles and Ben Foley, researchers with the ARC Centre of Excellence for the Dynamics of Language (CoEDL) are working to transcribe and preserve endangered languages. There are over 300 Indigenous languages in Australia––which can be as distinct as Japanese is to German.

Indigenous languages are also inextricably connected to the land, imbued with history and sacred songlines––passed down through oral tradition.

Google AI: Preserving precious languages of indigenous communities
Google AI: Preserving precious languages including indigenous communities. CoEDL conducting traditional methods of fieldwork

With many indigenous languages endangered, research and transcription is both time sensitive and intensive. CoEDL has fieldworkers working with 130 languages, recording mountains of data (almost 50K hours of language audio in archives), which could take 1.9M hours to transcribe using traditional methods.

“The elephant in the room is a boring dull problem called transcription,” added Professor Foley. “We need speech detector systems.”

Recognising the importance and the sheer enormity of the work, Wiles and Foley realised AI could help provide a new solution to harness the contributions of community members and linguists, while protecting the integrity of this precious language data.

CoEDL and Google teams building language models at a recent workshop
CoEDL and Google teams building language models at a recent workshop

 In 2016, Wiles and Foley looked to Google’s open-source AI technology to build unique models for several Indigenous languages – allowing for faster transcription and a bespoke solution. While this project is still in its early stages, Google has partnered up with CoEDL to implement TensorFlow and Kaldi to transcribe indigenous languages.

So far, 12 models have been built for Indigenous languages including Bininj Kunwok, Kriol, Mangarayi, Nakkara, Pitjantjatjara, Warlpiri, Wubuy – as well as indigenous languages in regions surrounding Australia, such as Abui (spoken in Indonesia) and Cook Islands Maori.

 CoEDL aim to train more language workers to contribute to the models, and build an even simpler interface. Long-term, the team has a dream to integrate recognition and synthesis systems into their social robot Opie, designed with the Ngukurr Language Center to promote community engagement and the revitalisation of endangered languages.


Enhancing healthcare with Google AI

Another area for advancement thanks to AI is in healthcare and already Google is seeing hugely beneficial applications that could help billions of people. Working closely with clinicians and medical providers, Google is developing tools that will improve the availability and accuracy of medical services across a range of conditions, from diabetic eye disease, to cardiovascular health, and cancer.
In early 2017 Google partnered with Dr Elliot Smith, of Brisbane-based medical data specialist Maxwell Plus, to combine deep learning with medical imaging to diagnose prostate cancer in a faster, more affordable and accurate way.

Dr Smith, an expert in magnetic resonance imaging (MRI) systems with a Ph.D. in Electrical Engineering, was troubled that highly trained diagnostics are a scarce resource and unevenly distributed in Australia. Moreover, prostate cancer diagnostic methods can take up to seven days to process results. He felt compelled to find a scalable solution and make this clinical brainpower available to all doctors, to offer patients better care.

He leveraged Google Cloud AI to ‘train’ a system to analyse hundreds of thousands of prostate cancer images using AI. This delivered results to clinicians in 10-15 minutes rather than 2-7 days by traditional diagnostic methods.

Maxwell Plus has since expanded to cover breast and lung cancer diagnostics – and has a goal to process 150,000 cases by the end of 2018.

Dr Smith leveraged Google Cloud AI
Dr Smith leveraged Google Cloud AI. Maxwell Plus’ interface running cancer diagnostics, powered by Google Cloud Platform

With so many advancements being made to help the work for conservationists and medical teams taking place, the way is open for positive change on scale we have never seen before.

So when someone bemoans artificial intelligence taking away jobs, remind them it is thanks to machine learning that complex, real-life problems are starting be solved.


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