2025/04/06 by Matej Šprogar, Šprogar, Matej
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Computability, Logic, AI Algorithms #D.2.8 #FOS: Computer and information sciences #Ferroelectric and Negative Capacitance Devices #I.2 #I.2.6 #I.5 #Machine Learning in Bioinformatics
paper · pdf · doi:10.48550/arxiv.2504.04430
openalex publication_date 2025/04/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Grounded in the premise that high-level intelligence can emerge from low-level signal processing, we advance a hypothesis regarding low-level requirements necessary for artificial general intelligence. The proposed requirements characterise core properties of systems that learn through prediction over spatially and temporally structured signals with initially unknown semantic content. They include a selection of basic principles observed in cognitive neuroscience, from learning from an uninformed state to real-time liveness. To enable empirical testing and hypothesis rejection, we introduce an operational testbed composed of transparent and reusable tests, one per requirement. To date, no non-intelligent system has been identified or reported as successfully passing the testbed. Pending such a counterexample, the testbed serves as a candidate empirical milestone toward general intelligence. The reference implementation of the testbed is publicly available.