Day 1 Hall 2

Sustainable Development Goals (SDG have been setup by the United Nations in 2015 and intended to be achieved by 2030. In total, 17 SDGs have been identified some notable ones being No Poverty, Zero Hunger, Quality Education, Good health, Clean Water, Sustainable Energy, Affordable and Clean energy. The current pace of incremental development in those focus areas makes it almost impossible to attain the goals by 2030.

This talk focusses on how some of the important SDGs could be achieved by the use of Artificial Intelligence.

Airlines have always been at the cutting edge of leveraging data and data science in business operations.
This talk will provide an exposure to the spectrum of data science problems that are addressed and solved on a daily basis to provide you the safe, reliable and comfortable flying experience that you have come to expect from your favorite airline.

Adopting a data driven decision making culture involves many aspects – from upgrading the data management, analytics, governance to and probably most importantly, upgrading the decision making paradigm.

The speaker talks about experiences in transforming organization from traditional analytics eco-systems, to the modern, machine learning, fast paced and nimble organization winning in the market.

In this talk she discusses the current state of applications, the way to progress and five principles of cloud-native architecture that will help customers design systems to take full advantage of the cloud while avoiding the pitfalls of shoe-horning old approaches into a new platform.

The talk is about implementing research in the area of Deep Learning and successfully applying them in various use cases. A few things that would be covered include using a custom loss function and leveraging state of the art “Attention” concept to tabular data. The talk would cover practical aspects and pre-requisites when applying some of the recent findings in everyday projects.

We are living in the information era where every second we are producing plethora of data. Now, businesses have started unlocking the power of user data collected so far and how to leverage it to provide enhanced experience to their customers. However, It is imperative to preserve privacy of users in the process of data sharing while maintaining the utility of data. Differential privacy is helping us to tune privacy guarantee and utility of the data based on goal of data sharing.

Today, Industry 4.0 is the most refined technology for allowing intelligent and automated business applications. IoT, Big Data, Data Science, and artificial intelligence (AI) are growing technologies that are frequently used in industry. When developing a large-scale, real-time AI application system, data scientists must work on many technologies in Machine Learning and Deep Learning algorithms to push their applications; the AI application is resource (Compute and data) hungry. Modern Python software enables for the rapid implementation of AI & Data science algorithms such as recommendation engines, fraud detection, voice assistance, and so on. The Machine Learning algorithm is the foundation of every AI system. Machine learning algorithms learn from data and use hidden patterns in data to accomplish a certain objective.

The Intel oneAPI software creates an ecosystem of software tools that are optimized for AI training and inferencing. In this workshop, we will look at how to work on optimized frameworks like Tensorflow and Pytorch for accelerating inferencing workloads for deep vision algorithms like CNN, GAN, and Transformers on images and videos. The workshop will provide technical knowledge on oneAPI AI toolkit optimisation frameworks and how these libraries aid in boosting current deep vision models, followed by a hands-on session for participants on the Intel Dev cloud platform.  

Participants Prerequisites of the workshop

INTEL WORKSHOP CONTEST