Machine Learning is used not only to detect new malware, but also to create more efficient, harder to detect threats. Will the pros of machine learning outweigh the cons or will the cybersecurity equilibrium deteriorate? And how to explain what the AI does when it makes decisions? This time, we will look at outsmarting cyber-criminals as well as understanding the AI itself.
Juraj joined ESET in 2008 as a Malware Analyst, he holds a bachelor’s degree in Applied informatics and a master’s degree in Robotics, both from the Slovak University of Technology. Currently, he is the leader of ESET’s Automated Threat Detection and Machine Learning section at ESET’s Core Research and Development. He was a member of several international working groups focusing on botnet eradication (e.g. Dorkbot, Gamarue, 3ve, Emotet). He presented at several international private and public conferences including RSA, MWC and CARO.
Martin Tamajka is a researcher and lead engineer at the Kempelen Institute of Intelligent Technologies. He focuses on research of novel methods of deep learning, as well as on increasing the transparency of neural networks through methods of explainability and interpretability. His past research also includes analysis of multidimensional medical images and images in general. He will talk about finding the right explainability algorithm that provides good explanations for a given model, task and data.
Ever wanted to start utilizing federated learning? Professor Peter Richtarik from King Abdullah University of Science and Technology will give us a detailed lecture on the role of local training in federated learning and will answer any questions you may have.
Federated Learning refers to machine learning over private data stored across a large number of heterogeneous devices, such as mobile phones or hospitals. In an October 2020 Forbes article, and alongside self-supervised learning and transformers, Federated Learning was described as one of three emerging areas that will shape the next generation of AI technologies. In this talk I will explain how we recently resolved one of the key open algorithmic and mathematical problems in this field.
What happens when research, its practical applications and human factor intersect? How to train the machine learning models to expect the unpredictable events that are inevitable in real life? This time we’ll look at the challenges the AI and ML technologies face when put to practical use.
This time, we want to give you insight into how AI and machine learning methods are put to practical use at Innovatrics for training new biometric algorithms and in Swiss Re, one of the largest re-insurance companies in the world, to develop new, innovative products.
Machine learning (ML) helped us to solve such complex tasks as face recognition, however when it is done on a large scale it relies on many high-effort data management tasks. We will describe our winding road from images in folders and labels stored in CSV files to structured versioned datasets with reproducible data processing pipelines. Moreover we mention how we deal with data versioning / validation / quality evaluation, reproducible ML trainings and experiment management.
The opening presentation is centered on the most recent success of AI research in Slovakia – the SlovakBERT model. Creating a neural language model for languages with limited available corpus can be challenging. We will explain what are the main obstacles when creating such model and what its practical applications are. The second presentation will focus on why AI is becoming mainstream and what it means for the future of the technology.