OIA’s CTO Dr. Jürgen Riedel and Senior AI Researcher Dr. Santiago Hernández-Orozco delivered an invited technical talk at the international conference AUTOMATA 2020, the 26th International Workshop on Cellular Automata and Discrete Complex Systems, one of the most reputable conferences in the field.
Jürgen and Santiago explained some of the most fundamental technical aspects behind OIA’s A.I. and Machine Learning approaches to transforming medicine from simple statistical pattern-matching to causal diagnostics, and how OIA’s methods and technology give it an edge when it comes to robustness (e.g. to perturbations and interventions), generalisation (over-fitting avoidance) and explainability, features of what we call a responsible A.I. approach to applications in healthcare.
According to a survey conducted by London venture capital firm MMC, 40% of European startups that are classified as A.I. companies don’t actually use artificial intelligence in a way that is “material” to their businesses. Another percentage are startups or companies that adopt AI, but very few innovate in AI as much as in its domain of application, as OIA with its group of AI leaders and pioneers does.
Here are some of OIA’s most recent papers published in some of the top journals in the fields of AI, automata and molecular biology research:
- Rule Primality, Minimal Generating Sets and Turing-Universality in the Causal Decomposition of Elementary Cellular Automata
Journal of Cellular Automata, vol. 13, pp. 479–497, 2018.
- Causal Deconvolution by Algorithmic Generative Models
Nature Machine Intelligence, vol 1, pages 58–66, 2019.
- Training-free Measures Based on Algorithmic Probability Identify High Nucleosome Occupancy in DNA Sequences
Nucleic Acids Research, gkz750, 2019.
- An Algorithmic Information Calculus for Causal Discovery and Reprogramming Systems
iScience, S2589-0042(19)30270-6, 2019.
- Algorithmic Probability-guided Machine Learning On Non-differentiable Spaces
arXiv:1910.02758v2 Machine Learning (cs.LG), 2020.