SAMURAI - System support for AI

Artificial Intelligence (AI) has moved from research labs to production. Realizing a promise of complex machine data analysis, it does, however, also raise daunting challenges. In particular, we need AI systems that make timely and safe decisions in unpredictable environments, are robust against sophisticated adversaries, and process the ever increasing amount of data across organizations and individuals without compromising privacy, confidentiality or security.
Master

As Bushido, or "the way of the samurai", is providing the guiding principles and philosophy of the samurai - in this master project, we looking for students that are interested in the fundamental part of systems and architectures that can address the challenges
and provide "the way of the AI" to unlock the true potential of AI.
The main goal of this project is therefore to fill the knowledge gaps of individual AI components (data, algorithms, models) and provide an integrated complete end-to-end pipeline for future developers of ubiquitous AI-based applications that automatically handle data transfers between the stages and optimize for system resource consumption.

Goal

The goal is to utilize existing hardware and design AI systems and APIs that allow the composition of models and actions in a modular and flexible manner, and develop rich libraries of models and options using these APIs to dramatically simplify the development of AI applications. Of particular interest is systems that require real-time feedback, low resource consumption, and scalability to support millions of users, e.g., the sports and medical scenarios described above. Some possible subtopics are:

Learning outcome

  • Insight into advanced techniques of machine learning and systems
  • Working on a challenging but important topic
  • Collaboration with researchers
  • Possibility to implement and research a novel approach
  • Advanced Samurai skills (Shogunate possible)

Qualifications

  • Programming
  • Motivation
  • Basic Samurai training

Supervisors

  • Håkon Kvale Stensland
  • Pål Halvorsen
  • Michael Riegler
  • Hugo Lewi Hammer, OsloMet, hugoh@oslomet.no

Collaboration partners

OsloMet