JCU's Phoebe Arbon was presented with a Science and Innovation Award for Young People in Agriculture, Fisheries and Forestry last night in Canberra. "I'll use the grant to develop, train and validate an AI model to identify, count and measure abalone from an image. So, very basically, the AI model will learn to predict the weight and size of abalone from images," she said. Ms Arbon said the technology is already in existence but needs specific instructions and application to work within the abalone industry. "Currently, assessing abalone is done manually which can cause harm to the abalone, and costs each farm about $25,000 a year," she said.

The surface area for testing software has never been so broad. Applications today interact with other applications through APIs, they leverage legacy systems, and they grow in complexity from one day to the next in a nonlinear fashion. What does that mean for testers? The 2016-17 World Quality Report suggests that AI will help. "We believe that the most important solution to overcome increasing QA and Testing Challenges will be the emerging introduction of machine-based intelligence," the report states. How will we as testers leverage AI to verify these ever-growing code suites?

We are back with more interviewers. I have Tariq and Jason, nice to have you both. Tariq King: Nice to be here. Jason Arbon: It's very nice to be here. Thank you for inviting me here. So we get both you guys all at once. Jennifer Bonine: And, I heard that both of you like to talk about AI. Jennifer Bonine: Yeah, and you have a new organization that was started. Jennifer Bonine: So if people want to find it they just go to ... Jennifer Bonine: And, what is the intent behind it?