Events

The issues of food security and sustainable agriculture are vital concerns to society and key topics in assessments of climate variability and change on productivity and food supplies. However, accurate and advance knowledge of the associated risk in crop production systems can mitigate some of the impacts of such causative threats. It is anticipated that such approaches will increasingly become more valuable in decision-making and lead to better preparedness in coping with the impact of extreme climate events leading to a reduction in the downside risk and more resilient agricultural food systems.

Artificial intelligence and the data sciences are enabling ‘Agriculture 4.0’. In 2020, agrifood tech startups raised more than $30 billion in investment. Many of these innovations are deployed in agriculture research, supporting new sustainable solutions that keep us fed. Professor Scott Chapman will discuss some of these digital and predictive agriculture technologies.

Ever wondered how social media impacts on our society? Be part of this riveting Ask me anything session to get your answers.

Artificial Intelligence (AI) is receiving widespread attention around the world, and this workshop brought academics and research from around UQ together to share knowledge, explore synergies and develop collaborations around AI.

Musculoskeletal pain conditions, including low back pain, are the leading cause of disability internationally. Recent work has begun to expose features that have the potential to guide personalisation of treatment, but the challenge to disentangle the complexity of the condition is immense. Artificial intelligence will need to play a role.

What is your question about Face Recognition? We'll have the answer at this Ask me anything session.

Sophia Ananiadou will describe NLP methods for the extraction of structured representations from text for downstream applications while addressing challenges of information extraction tasks.

The concept of "biased data" is often too generic to be useful.  Through a series of cases studies, we will explore what algorithmic bias is, different types (with different causes), and debunk some common misconceptions.  We will cover why algorithmic bias is a problem worth addressing and some steps towards solutions.