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AIHappyHour @ AUT

Welcome. Every month the AIHappyHour gets bigger and better.

This month you'll hear three talks from two professors and one entrepreneur about image processing and recognition, autonomous vehicle technology and how machine and deep learning are being used in big-data projects in New Zealand. 

No matter what your job is or what level of understanding you have on Artificial Intelligence come along and learn about this emerging technology, how it's being used in every day life and how you could apply it in your business.

The goal is to help marketers, product managers, business leaders, and other professional services understand what AI is, how to identify business opportunities to build AI technology, connect with suppliers and build internal capability. 


Justin Flitter is the founder of, host of the AIHappyHour events and co-producer of TEDxAuckland.

Justin is an experienced marketer helping growing businesses leverage technology and consumer experience with modern lead generation and communication methodologies.



Guests will be welcomed by a wonderful AUT student, Amritpal Kaur whom is currently designing a start-up idea involving AI to help small businesses grow and gain a competitive advantage. She is currently studying a Bachelors of Engineering (honours), majoring in Mechatronics Engineering and minoring in Creative Entrepreneurship.


AI: From Rule-based Systems to Deep Learning Data Analytics and Brain-like Machines

The talk presents briefly the main principles of AI, from rule-based systems and evolutionary computation, to deep learning neural networks and the current  state-of-the-art in AI – brain like machines, exemplified by the NeuCube architecture, developed in the Knowledge Engineering and Discovery Research Institute (KEDRI) of AUT. We'll look at examples of how these Machine Learning techniques are being used in New Zealand and what future applications can follow.

Kasabov is a Fellow of IEEE, Fellow of the Royal Society of New Zealand, Fellow of the NZ IITP NZ and Distinguished Fellow the RAE and the SICSA UK. He is the Director of the Knowledge Engineering and Discovery Research Institute (KEDRI), at Auckland University of Technology. He holds a Chair of Knowledge Engineering at the School of Engineering, Computing and Mathematical Sciences at AUT. Kasabov is a Past President and Governor Board member of the International Neural Network Society (INNS) and also of the Asia Pacific Neural Network Society (APNNS). 

His main research interests are in the areas of neural networks, computational intelligence, soft computing, bioinformatics, neuroinformatics.



From Vision-based Driver Assistant Systems to Autonomous Driving

Machines are currently “learning to see”. 

The talk informs about general developments as well as about current work in the .enpeda.. project at Auckland University of Technology (AUT), Centre for Robotics & Vision (CeRV), directed at adaptive and intelligent solutions for vision-based driver assistance or autonomous driving. The talk also reports about the development of an extensive testbed for modern vehicles currently established near Whangarei.

The last 10 years have seen rapid developments in the automotive industry. Internal combustion engines are increasingly replaced by electric motors, control modules for driver assistance quickly generalised towards autonomous driving, and there will be communication between vehicles, or vehicles and infrastructure. The talk focusses on the use of cameras for driver assistance or autonomous driving, with the corresponding development in computer vision, a subdiscipline of AI. Modern cars are increasingly equipped with multiple sensors (such as GPS, IMU, radar, ultrasound, LiDAR, single cameras, or stereo-vision systems), and single cameras or stereo-vision systems provide a very valuable input for understanding processes in traffic scenes. Car companies started around 1995 to add vision solutions into their top-end models. 

Questions to be answered are as follows: What may happen next in front of the car, at the location where the ego-vehicle (i.e. the car where the system is operating in) is expected to be in the next few seconds? How far can we understand a complex environment, as defined by traffic scenarios, by stereo vision or by using just a single camera? — Various computer-vision modules have reached the state of being robust under various conditions (e.g. for lane analysis, driver monitoring, or distance calculation). A new quality of tasks is now defined by creating combined solutions for the better understanding of traffic related events. Learning concepts in AI are contributing to the rapid developments. 



Imagr is an Auckland based AI company that specialises in image processing and recognition. This means through our software we are able to teach a computer to act like a human eye. Our keystone product is Smartcart, a trolley-mounted camera that removes the need for checkouts in supermarkets. I started the business in 2015, in Sydney, but started operating here last July. We have 10 employees.

William Chomley got his first taste of business at age 16 when he started his first business, before selling it two years later to travel the world. William obtained a Bachelor of Commerce from the University of Notre Dame, and entered the private funds market in Sydney. However, William’s job in finance didn’t provide him with the challenges and day to day problem solving that come hand and hand with starting and running a business.

By February 2015 he was fixated on creating a frictionless and unique experience for the retail industry. For the last two and a half years he has leveraged every skill, contact and resource available to him to build out what is known as SMARTCART as well as the IMAGR brand.

Earlier Event: June 15
AIHappyHour @ IBM
Later Event: August 23
AIHappyHour @ Microsoft