HIIT Quantum Software Day in Helsinki on 3.2.2023

We are arranging the 1st HIIT Quantum Software Day in Helsinki on 3.2.2023 to get together people interested in quantum software, algorithms, and applications. The program is aimed to cover both basics from a CS perspective, examples of ongoing research, and discussion/networking of future opportunities.
Further information and registration here: https://www.hiit.fi/event/hiit-quantum-software-day/
We would appreciate it if you forwarded this message to other interested persons in your neighborhoods and networks.

Testing of AI or Testing with AI – Poster Session of IVVES ITEA project Sep 13th at 9-11 at the University of Helsinki Main building

IVVES https://ivves.eu/ is one of the major machine learning engineering projects we are running. Come and see the key results of the project. No registration, just walk into the University of Helsinki main building (Unioninkatu 34), grab a cup of coffee, walk around, and chat with the experts presenting the posters.

Further details here: IVVES_Poster_Session_Invitation

Quantum software development is growing

Quantum computing is a hot topic. The big question is how do we map real-life problems to quantum computers. A new kind of thinking is needed for the creation of quantum software. A successful approach by one of our doctoral researchers Valter Uotila

We would be welcome new industrial use cases or challenges. 

IML4E and VesselAI project meetings

Last week we had project meetings on two of our European machine learning engineering projects IML4E and VesselAI. It was amazingly useful to meet the partners face to face after all the video conferences we have had during covid times. Lots of new ideas, clarity of collaboration, and fun being together. Now there is even more potential for useful work on bringing AI into real use. 

New MOOC on AI in Society

Yesterday we launched a MOOC on AI in Society. It is a result of interdisciplinary work involving computer scientists, social scientists, philosophers, law researchers, and cognitive scientists from three universities: the University of Helsinki, Edinburgh, and Paris 1.

The MOOC aims to be an introduction to AI and its impact with a strong emphasis on the intersection between AI and other fields, such as ethics, healthcare, politics, and law. There is a need to understand how AI is changing the landscape of different sectors and the effects it has across societies. With this knowledge, it is possible to understand the potential that AI has.

The MOOC has a modular structure and additional modules e.g. on AI and democracy, robotics, and health are planned to be released later this year.

Students can study at their own pace and schedule, as well as select the optional modules based on their interests. The MOOC is available at https://ai-in-society.mooc.fi/

IVVES project on the testing of machine learning systems starting

Last week Business Finland decided to fund our three-year IVVES project (Industrial-grade Verification and Validation of Evolving Systems) https://ivves.weebly.com/. We can now significantly extend our research efforts on testing, continuous development, and maintenance of machine learning systems together with our partners in Finland, The Netherlands, Sweden, and Canada. We are also planning to set up an interest group for Finnish companies interested in the project. The University of Helsinki’s work in the project is jointly headed by Prof Tommi Mikkonen and Prof. Jukka K. Nurminen.

Open Source Software Framework for Data Fault Injection to Test Machine Learning Systems

dpEmu is our Python library for emulating data problems in the use and training of machine learning systems. It provides tools for injecting errors into data, running machine learning models with different error parameters and visualizing the results.
Data-intensive systems are sensitive to the quality of data. Data often has problems due to faulty sensors or network problems, for instance. dpEmu framework can emulate faults in data and use it to study how machine learning (ML) systems work when the data has problems. The Python framework aims for flexibility: users can use predefined or their own dedicated fault models. Likewise, different kinds of data (e.g. text, time series, video) can be used and the system under test can vary from a single ML model to a complicated software system.
The software and a set of Jupyter notebooks illustrating different use cases are available at https://github.com/dpEmu/dpEmu
We just presented the work at ISSRE conference: Jukka K. Nurminen, Tuomas Halvari, Juha Harviainen, Juha Mylläri, Antti Röyskö, Juuso Silvennoinen, and Tommi Mikkonen. “Software Framework for Data Fault Injection to Test Machine Learning Systems”. 4th IEEE International Workshop on Reliability and Security Data Analysis (RSDA 2019) at 30th Annual IEEE International Symposium on Software Reliability Engineering (ISSRE 2019), Berlin, Germany, October 2019.