Practical AI Methods for Anyone

The latest Large Language and Stable Diffusion Models are awesome in their power, but did you know their hallucinations make the execution of engineering and scientific technical computation difficult?  Similarly, the latest object detection and classification models, while accurate, require significant computational resources. This presentation series focusses on an alternate strategy of practical and applied AI, which uses Low Code methods to deliver an appropriate solution that balances compute power and accuracy.  Essentially, we stand on the shoulders of giants and reuse models in a technical and scientific context.

About the Presenter

Dr Emmanuel Blanchard is a Senior Application Engineer at MathWorks who first joined the company as a training engineer in 2014. He focuses on data analytics. Prior to joining MathWorks, he was a Lecturer in Mechatronic Engineering at the University of Wollongong. He holds a PhD in Mechanical Engineering from Virginia Tech. He also worked as a Systems / Controls Engineer at Cummins Engine Company and as a research assistant in several research institutions in California and Virginia.

Dr Peter Brady is a Principal Application Engineer at MathWorks where he leverages maths to accelerate our customers projects.  He works across the engineering spectrum with a focus on maths, statistics, optimisation, machine and deep learning as well as cloud scale out.  Prior to joining MathWorks Peter worked for several civil and defence contractors delivering projects in surface water, hydraulics and hydrology as well as fluid dynamics and submarine cavitation inception. Peter has a PhD in Mechanical Engineering and a Bachelors in Civil Engineering, both from UTS, and is a Chartered Practicing Engineer with Engineers Australia (CPEng NER) and a Certified Professional with the Australian Computer Society (MACS CP).