ML beyond metrics: challenges of using ML systems in the real world [Discussion]
Over the past 2 decades, machine learning has made tremendous breakthroughs in all directions. As a result, Google translate, autopilot, and Face ID have become an integral part of our lives.
At the same time, there are many areas of human life where the implementation of machine learning takes place with much greater effort, and the reason for this is often not the inefficiency of existing ML algorithms.
The legal and moral-ethical issues that arise during the transfer of responsibility for decisions from person to machine become much larger obstacles, which will be discussed in this discussion.
The discussion will be in Russian.
Moderator:
Vladislav Mats (Head of Research)
Experts:
- Alex Lazarev (Head of Research)
- Alexander Onbysh (Head of ML Pipelines)
Alex Lazarev
Head of Research
- Head of Research
- Specialized in: CV, Deep Learning/Machine Learning (objects classification, motion detection, segmentation, GANs etc.)
- Sphere of interests: AI, Video Games, Music, Yoga, Table Tennis
Alexander Onbysh
Head of ML Pipelines
- Head of ML Pipelines
- Main field of interest is high-load distributed systems. Has decent experience in production CV pipelines with real-time video stream processing. Was working on developing motion detection, object detection, face recognition pipelines.
- Specialized in CV/DL, system architecture, video streaming, performance optimizations.
- GitHub, LinkedIn
Vladislav Mats
Head of Research
- Head of Research
- ACM ICPC 2019 Champion of Ukraine and World Finalist, Google Hash Code 2017, 2020 World Finalist
- Graduated from Yandex School of Data Analisys, Big Data department
- Main interests: computer science, deep learning, economy, algorithmic trading, medicine