The explosive growth in cloud computing systems has permitted the design and realization of novel digital services. This growth has ripple effects, such as significant data growth: by 2020, about 1.7 megabytes of new information will be created every second for every person on the planet and the accumulated digital universe of data will grow from today’s 4.4 zettabytes today to around 44 zettabytes or 44 trillion gigabytes. Meanwhile, internet-connected things are growing rapidly in number as well. Within this context, new opportunities arise from the wealth of data generated, processed, and stored. In a sense, artificial intelligence (AI), Machine Learning (ML) and distributed Deep-Learning (DL) provide a response to these challenges, requiring minimal investment in human resources to exploit a large amount of data in order to extract the relevant information from it with the promise of finding optimizations to unleash unknown market potential. Specifically, intelligent applications and services based on AI, ML, DL and extreme scale analytics constitute the next wave of technology and together with the burst of connected devices (Internet of Things – IoT) and the important advancements in sensor technology push current ICT infrastructures to their limits, with the latter failing quite often to host, provide and/or manage such applications efficiently or at all.

This CAS-sponsored 3-day school on “Innovation for Data Era: Power the Era of Artificial Intelligence”, named InnoAI School, offers a great opportunity to students, scientists, and professionals who desire to enhance their understanding and gain insight in basic and advanced issues on the design of the future machine learning based intelligent systems. The content of the school targets several the above-mentioned issues and areas where ML and AI are expected to play an essential role. In addition, to tutorial-like lectures and school includes one day with hands-on lab on ML applications with MATLAB.

innoai-program

innoai_poster

Από τη Γραμματεία του Τμήματος