AIML currently has 140 members and has funding to recruit another 30 in the next 12 months (22 researchers, eight PhD students and three professional staff).
So, where does South Australia sit when it comes to real world application and commercialisation of its world-leading machine learning research?
It is no accident AIML was one of the first tenants at Lot Fourteen. With a nod to Silicon Valley’s mix of research, tech giants and venture capital in a hyper-competitive but collaborative ecosystem, innovation is sure to follow. AIML has since been joined by Google Cloud Services, Amazon Web Services, the MIT bigdata Living Lab and the Australian Space Agency.
And the Institute is already contributing to collaborative discovery at Lot Fourteen, working closely with companies and startups on commercially sensitive partnerships.
To enhance the application of machine learning for commercial development, AIML has a dedicated machine learning engineering team working with companies across South Australia.
The team works with companies to apply machine learning capabilities to build businesses of the future that compete on the global stage.
In the last year, AIML has worked with companies to apply AI and machine learning for diverse purposes, including matching celebrities with commercial opportunities, mapping satellite imagery to predict a city’s architecture, and monitoring innovation in financial technology. It’s work that future-proofs South Australia and prepares today’s startups and companies for Industry 4.0 and the jobs of tomorrow.
Here we deep dive into two examples where AIML has worked with companies with rich sources of data to incorporate machine learning and help drive innovation in the state.
AIML worked with automation company, and Lot Fourteen neighbour, Neo-Analytics to create smarter software for regulatory compliance monitoring in banks and financial institutions.
Adherence to strict regulations and standards is vital for banks to keep financial risks low and ensure safety of clients’ money. However small financial institutions with few staff and limited technical capability can feel overwhelmed by this burden of compliance.
Machine learning offers a solution.
“One of the requirements of operating a financial services institution is to follow local and national government laws and regulations – this is called regulatory compliance,” said Rick Rofe, founder at Neo-Analytics.
“This is a huge job involving lots of data, and so we worked with AIML to develop automated processing capability for regulatory compliance.”
Neo-Analytics now applies algorithms that deliver reliable and accurate results, providing smarter more intelligent software for their client base.
“We are working with three banks, one of which has our products in production,” Rofe said.
“We hope these improvements will soon allow us to employ our contractors permanently as adoption of our machine learning products increases.”
AIML worked with startup Pickstar to apply AI to help match celebrities and sports stars with paid opportunities.
The engineering team at AIML applied machine learning and predictive data analytics within the technology platform to match customers with 2000+ sports stars, personalities and celebrities with commercial opportunities.
Pickstar is a South Australian business that has developed a 3-sided online marketplace which allows brands, corporates, advertising/PR agencies and fans to connect directly with celebrity and high profile sporting talent with commercial opportunities through a self-serve platform at www.pickstar.pro.
CEO and founder of Pickstar James Begley said AIML has been helpful in providing a service that will be able to effectively pair up customers with the right stars within the client’s budget.
“The question that we answer as a business is, who is the best person I get for my budget?” Begley said.
“The work that AIML does allows us to serve up and recommend the best available talent for someone’s brief and budget, and that can only happen with heavy investment into machine learning and data analytics to underpin the recommendation engine.”
Begley said machine learning will help Pickstar to achieve faster and better results for their customers.
“AIML have provided us with a greatly enhanced recommendation engine and a functional prototype, an actual tangible early-stage product that we are able to use and commercialise – this is taking university smarts and bringing it into the real world for commercial application,” he said.
“For us the investment into machine learning and data analytics is only going to increase, so if we can maintain that relationship with the Institute we will be very pleased to follow on.”
Pickstar’s Chief Growth Officer, Loren Renton said “Since engaging AIML not only have we been able to mature the sophistication of our recommendation engine to benefit clients in matchmaking, but we’ve added a whole new dimension of insights which helps us identify where the gaps are and the areas we need to double down on internally.”
To find out how your organisation can work with AIML’s talented researchers and engineers to provide you with practical and innovative solutions for your business here.
"*" indicates required fields