Partnerships and consulting
Work with us
Did you know that at AIML we also apply our machine learning research to solve problems and deliver benefits to the community?
Our innovative capabilities enable us to engage with domestic and international businesses in:
|Defence and security||Agriculture and food||Manufacturing|
|Social and policy||Transport||Infrastructure|
|Health and medical||Environment||Sports science|
|Mining and resources||ICT||Energy|
|Entertainment||Finance and commerce||Space|
Why work with us?
At AIML, we are committed to producing outstanding research that is engaging, innovative and importantly can be applied to real-life challenges.
With a proven track record collaborating with local, state and federal government, universities, multi-national organisations and SMEs, our talented researchers can work with you to provide practical solutions including:
- developing deeper insights into customer behaviour
- identifying patterns in large, complex data sources
- predicting future behaviour of people and systems
- coaching staff or students on how to improve their performance
- optimising complex systems
- automating the interpretation of video imagery
- producing computer vision and robotics applications
- uncovering deep learning and pattern recognition from big data
- creating natural language processing
- developing video question and answer programs
- generating AI innovation strategies.
Want to connect and collaborate with our world-class machine learning experts? Contact us for more details.
Collaborative research and development (R&D)
At AIML we have an impressive track record of successful research and development projects that have been executed under various grants schemes, including ARC Linkage Projects, CRC-P Projects and Defence Innovation Partnerships.
By working with us, we may be able to provide financial support through co-contribution of grant funding and, if eligible, by accessing R&D tax benefits.
AIML conducts fundamental and applied research, and our specialised staff are experienced in developing and tailoring R&D proposals to suit individual requirements.
AIML’s services have seen multiple patents commercialised and Intellectual Property licenced, forming the foundation of a number of sophisticated products.
To find out how your organisation can benefit, please contact us.
Whether it is a short-term contract or a major, cross-disciplinary venture, AIML has the expertise and capability to work with industry and government on product commercialisation and development.
We work collaboratively to develop exclusive AI-based products and ensure that our clients are ready to compete in an AI-enabled economy.
To find out how your organisation can benefit, please contact us.
Speeding up disease detection
In partnership with Australia-based (LBT), we created the world’s first AI microbiology screening technology for use in pathologylaboratories.
The Automated Plate Assessment System (APAS) aims to speed up the rate of patient diagnosis and treatment by automating the functions involved in screening culture plates after incubation. APAS takes high-quality images of the plates, analyses and interprets any microbial growth, matches this against key patient data, presents a diagnosis, and continually updates its own knowledge base.
APAS will provide doctors with quicker access to more information, and one day may even allow country hospitals to run their own tests without having to ship samples to a central lab, potentially saving a significant amount of time, and importantly, lives.
Automated medical imaging analysis to detect breast tumours
Our researchers have developed an autonomous medical image program to detect breast tumours.
The system is used in conjunction with an MRI scan, and using artificial intelligence employs the traversal movement and style of a retro video game to examine the breast area.
Just as vintage game Tetris manipulated geometric shapes to fit a space, this program uses a green square to navigate and search over the breast image to locate lesions. The square changes to red in colour if a lesion is detected.
This unique approach is 1.78 times faster in finding a lesion than existing methods and just as accurate.
The program was developed using deep reinforcement learning methods, a form of AI that enables computers and machines to learn how to do complex tasks without being programed by humans. As a result the program can independently analyse breast tissue.
Our ultimate aim is for this detection method to be used by radiologists to complement, support and assist their important work in making a precise and quick prognosis.
Sharp and snappy surveillance
Several years of research carried out on large-scale video surveillance led to the formation of Adelaide based company, .
The core technology used by Snap focuses on synchronising surveillance camera networks as a whole, rather than individual cameras in isolation. As a result, the network can automatically learn the relationships with cameras across the network and then apply this knowledge to generate a straightforward and intuitive surveillance tool for security operators.
The technology is able to scale to networks that have thousands of cameras, and can be integrated with sensors.
Measuring biomass status with photography
Quick and efficient measurement to assess vegetation in natural ecosystems around Australia is being made possible by an innovative research project combining computer vision and environmental science.
University of Adelaide and TERN researchers developed a method to automatically determine the biomass (total amount of plant material) by analysis of a set of panoramic photographs taken at wooded sites.
Images were taken at 300 sites around the country in a variety of ecosystems to establish a baseline measure of the condition of Australia's natural vegetation. Computer vision and machine learning techniques were employed to estimate the cross-section of each tree photographed at a standard height of 1.3 metres (known as the basal area measurement).
The system analyses a set of three 360 degree panoramas from the centre of a site and then processes the images to produce a 3D reconstruction. From that, the system automatically detects individual trees, estimates the diameter at 1.3 metre up the tree trunk and puts them all together for an overall estimate of the biomass of the site.
This is a much more efficient method of biomass estimation than the standard method of direct measurement which is very labour intensive, using laser readings which is expensive, or other rapid measures which only provide crude estimates. The field method is quick accurate, and requires minimal training.
We are investing in South Australian business
As part of the investment made by the South Australian Government in AIML, funds are available to support local businesses in their development of new AI-based products. To find out how to access these funds, contact us.