Abstract: 

Artificial intelligence has been expanding across several areas of our daily lives by getting embedded into billions of smart objects or IoT devices. Given the resource constraints and limited connectivity of IoT devices, so far, we have only managed to integrate a small set of pre-defined functions into these objects.
Edge Processing not only optimizes large-scale data mining and aggregation, (by moving the data processing portion of an application to a single unit, known as the Gateway) but also facilitates Machine Learning in modern IoT applications. Most IoT gateways however, are only capable of running basic data aggregation functions due to their limited parallel processing capabilities of their CPUs. In order to enable IoT networks to mimic human perception of events and their environment, gateways need to be equipped with Artificial Neural Networks or Deep Learning.

In comparison with traditional CPU systems a GPU is capable of far higher peak performance for parallel data streams, which is a requirement of deep learning in artificial neural networks. GPUs have evolved from a fixed pipeline graphics processing hardware into powerful programmable co-processing units capable of performing general purpose computing. In this talk, we'll describe how Machine Learning and Deep Learning are changing the landscape of IoT and how GPU-enabled edge gateways can transform our perception of IoT networks.

Bio: 

Dr Mo Haghighi is the head of Developer Ecosystems Group at IBM, former Research Scientist at Intel and former Java and Open Source developer at Sun Microsystems. Mo's expertise primarily lies in the areas of distributed computing, blockchain, embedded systems and AI, with several publications and patents in those areas. He has also been a regular speaker at various events including JavaOne, TNW, Index and IEEE conferences. Mo obtained his PhD in one of the largest multidisciplinary research projects involving the UK government and major industrial players around complexity science and large scale distributed systems. Mo is very passionate about integrating blockchain and AI into IoT edge processing and he spends his free time developing a new AI platform for intelligent and scalable sensor and actuator networks.