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Maximally Autonomous AI Assistant/Kaski S.

Project Details

Description

Most current Artificial Intelligence principles have been designed for automating actions which require intelligence, even though they are being used by people. Instead, we develop artificial intelligence methods which have been designed to maximally help their user. They automate as much as possible but not more; when they do not know what their user's goal is or they have not understood it, they know to ask. The methods are applied in drug design and in designing maximally autonomous cars.
AcronymMAMAA /Kaski S.
StatusFinished
Effective start/end date01/01/2022 → 31/12/2024

UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):

  1. SDG 4 - Quality Education
    SDG 4 Quality Education
  2. SDG 8 - Decent Work and Economic Growth
    SDG 8 Decent Work and Economic Growth

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  • 16 Conference article in proceedings
  • 7 Article

  • Amortized Bayesian Experimental Design for Decision-Making

    Huang, D., Guo, Y., Acerbi, L. & Kaski, S., 2025, Advances in Neural Information Processing Systems 37 (NeurIPS 2024). Globerson, A., Mackey, L., Belgrave, D., Fan, A., Paquet, U., Tomczak, J. & Zhang, C. (eds.). Curran Associates Inc., 20 p. (Advances in Neural Information Processing Systems; vol. 37).

    Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

    Open Access

  • Chartist: Task-driven Eye Movement Control for Chart Reading

    Shi, D., Wang, Y., Bai, Y., Bulling, A. & Oulasvirta, A., 26 Apr 2025, CHI 2025 - Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems. ACM, 1167

    Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

    Open Access
    File
    4 Citations (Scopus)
    70 Downloads (Pure)

  • DxHF: Providing High-Quality Human Feedback for LLM Alignment via Interactive Decomposition

    Shi, D., Cheng, F., Weinkauf, T., Oulasvirta, A. & Mennatallah, E.-A., 27 Sept 2025, UIST 2025 - Proceedings of the 38th Annual ACM Symposium on User Interface Software and Technology. Bianchi, A., Glassman, E. L., Mackay, W. E., Zhao, S., Oakley, I. & Kim, J. (eds.). ACM, 14 p. 123

    Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

    Open Access
    File
    2 Downloads (Pure)
View all 23 research outputs