Centre for Augmented Reasoning

Centre for Augmented Reasoning

Making computers more capable of understanding humans, our instructions and needs, through more natural conversation.

Adelaide University’s Centre for Augmented Reasoning (CAR) is a $20 million investment from the Australian Government’s Department of Education to develop the expertise Australia needs to be an active participant in the machine learning-enabled global economy.

Building on Adelaide University’s existing research strengths at the Australian Institute for Machine Learning (AIML), this initiative has supported machine learning research through scholarship opportunities, strategic recruitment, supporting AI commercialisation initiatives, and advocating for AI in Australia.

Centre objectives

CAR aims to train a new generation of researchers to become experts in machine learning.

Our objectives are to:

  • build world-class capability in machine learning research
  • increase impact of machine learning research in Australia
  • establish Adelaide University as a key strategic research institute for machine learning in Australia
  • attract funding beyond the lifetime of Commonwealth funding
  • stimulate commercialisation of artificial intelligence in Australia
  • increase the application of artificial intelligence in Australia’s industries
  • increase gender equity in machine learning research
  • promote the value of the centre to Australia and the world

From research to impact

In this compilation of short interviews, we hear directly from CAR's members as they share their insights, experiences, and visions for the future of augmented reasoning.

What is augmented reasoning?

A new and emerging form of AI, augmented reasoning combines an advanced ability to learn patterns using traditional machine learning, with an ability to reason.

Rather than teaching humans to learn how to ‘talk’ in computer language, augmented reasoning can help us make computers better at understanding people and our needs, through more natural conversation and interaction. Augmented reasoning gives software an ability to solve some of the frustrations and problems that we all experience with current computers and technology.

Why augmented reasoning?

CAR will research and develop new augmented systems and improve the application of machine learning technology across a range of applications, these might include:

  • machines that work with data analysts in companies to optimise business processes 
  • machines that can question people in ways that are more natural and easier than filling in forms
  • robots that can understand and follow instructions from people
  • websites that interact with people to solve their problems and answer their questions
  • factories where people and machines work seamlessly together without the need for constant reprogramming of software
  • machines and staff working more effectively to deliver what customers really want.

The result will reduce the need for structured interfaces between humans and machines (like keyboards and command lines) and enable machines and humans to interact in a more natural way.

Examples of augmented reasoning

Examples include, but are not limited to:

  • visual question answering
  • visual language navigation
  • learning from priors
    (such as geometry, natural language processing, neural priors, implicit functions and deep priors)
  • machine learning and reasoning
  • causation and machine learning interface.

The intention is to build machines that can learn faster, with less information, through greater interaction with their environment and learn from prior knowledge.

Freed from keyboards and command lines, computers will be able to be more useful and less demanding to interact with. By being able to reason, computers will be able to link our language to its ability to process, operate and predict. We are looking particularly for examples whereby augmented reasoning can democratise technology, giving many more people the ability to program their own functions and operations, without needing to be experts in AI or coding.

Examples of augmented reasoning Augmented reasoning will help us build machines that can learn faster, with less information, through greater interaction with their environment.

Programs

CAR is being funded to develop the high-calibre machine learning expertise the nation needs to be an active participant in the coming machine learning-enabled global economy.

CAR will invest to push the capacities of machine learning. The centre’s vision and objectives will be delivered through six programs over four years.

Augmented reasoning programs

A research program for CAR staff to deliver a ‘first in class’ research outcomes in the focus areas of:

  • natural language processing
  • computer vision
  • deep learning and machine reasoning

Nine 3-year PhD scholarships with the option of 4-year scholarships that include a transition year specifically focused on attracting women from other disciplines.

To attract and retain high performance researchers and to support applications for fellowships such as DECRA, Future Fellow, Laureate Fellow.

A $3.5 million innovation fund for machine learning investment will support local collaboration opportunities, strategic development programs, and new business ventures.

Commenced in 2023, the fund aims to stimulate commercialisation of AI in Australia, with at least 10 new commercial opportunities arising from the centre’s research over the life of the program. These include:

  • seed funding to launch new start-ups involving the University’s researchers and graduates
  • collaborations with external companies to co-develop new AI-enabled products and capabilities
  • projects to extend the impact and reach of our world-class research

CAR will be a leading voice in the community to help improve artificial intelligence literacy in Australia and increase the visibility of Australia’s growing AI capability globally.

The centre will work with government, industry and the broader community to demonstrate the importance of AI and machine learning for Australia. We will work with journalists, writers, artists, and musicians, to explore what AI can mean for all Australians, and how it will impact and improve our lives.

Governance and operational support for the centre includes a Program Manager, administrative and communications staff, as well as funding for computer hardware and maintenance.

Themes

CAR will enable Adelaide University to grow its advanced machine learning capability with grants to support the delivery of a world-class research program focusing on natural language processing, computer vision, deep learning, and machine reasoning.

The centre’s ambitious research plan identifies four main research themes that will chart the course for the next generation of Australian AI leadership.

Theme 1: Next generation machine learning

We will improve the underlying tools to enable more functional AI to deliver better solutions by improving the efficiency of learning, enabling learning at scale, building next generation causality capability and applying quantum technologies to reasoning.

Next generation machine learning

Theme 2: Interactive machine learning

Our researchers will develop new models for reasoning and storing information that can be called upon by machines to solve new challenges. This includes developing advanced vision-and-language systems, and building machines that better understand humans and that can learn by interacting with us and the environment.

We will explore new areas of vision-and-language research, build novel algorithms, and collaborate with world-renowned musicians and artists to develop new ideas and creations at the frontiers of music, art, and AI.

Theme 3: Knowledge, representation and generalisation

We will build the technology to enable machines that are more capable of learning on their own and can explain their reasoning. We will develop more adaptable learning systems that can be applied to new examples with minimal retraining. This includes developing novel semantic representations for natural languages and methods that can generalise across scenarios and support complex reasoning.

Knowledge, representation and generalisation

Theme 4: Machine learning-driven science discovery

Using machine learning to support discoveries in biological and chemical sciences, our researchers will convert our research capability into solutions for the intractable challenges faced by humans now and in the future. This includes predicting treatment outcomes for patients by testing and implementing machine learning algorithms, and leading AI-driven discovery for energy materials.

Contact us

Program Manager, Centre for Augmented Reasoning

Dr Nisha Schwarz

nisha.schwarz@adelaide.edu.au