The reliable monitoring of the depth of anesthesia (DoA) is essential to control the anesthesia procedure. Electroencephalography (EEG) has been widely used to estimate DoA since EEG could reflect the effect of anesthetic drugs on the central nervous system (CNS). In this study, we propose that a deep learning model consisting mainly of a deep residual shrinkage network (DRSN) and a 1 × 1 convolution network could estimate DoA in terms of patient state index (PSI) values. First, we preprocessed the four raw channels of EEG signals to remove electrical noise and other physiological signals. The proposed model then takes the preprocessed EEG signals as inputs to predict PSI values. Then we extracted 14 features from the preprocessed EEG signals and implemented three conventional feature-based models as comparisons. A dataset of 18 patients was used to evaluate the models’ performances. The results of the five-fold cross-validation show that there is a relatively high similarity between the ground-truth PSI values and the predicted PSI values of our proposed model, which outperforms the conventional models, and further, that the Spearman’s rank correlation coefficient is 0.9344. In addition, an ablation experiment was conducted to demonstrate the effectiveness of the soft-thresholding module for EEG-signal processing, and a cross-subject validation was implemented to illustrate the robustness of the proposed method. In summary, the procedure is not merely feasible for estimating DoA by mimicking PSI values but also inspired us to develop a precise DoA-estimation system with more convincing assessments of anesthetization levels.

Large Language Models (LLMs) exhibit impressive linguistic competence but also produce inaccurate or fabricated outputs, often called “hallucinations”. Engineering approaches usually regard hallucination as a defect to be minimized, while formal analyses have argued for its theoretical inevitability. Yet both perspectives remain incomplete when considering the conditions required for artificial general intelligence (AGI). This paper reframes “hallucination” as a manifestation of the generalization problem. Under the Closed World assumption, where training and test distributions are consistent, hallucinations may be mitigated. Under the Open World assumption, however, where the environment is unbounded, hallucinations become inevitable. This paper further develops a classification of hallucination, distinguishing cases that may be corrected from those that appear unavoidable under open-world conditions. On this basis, it suggests that “hallucination” should be approached not merely as an engineering defect but as a structural feature to be tolerated and made compatible with human intelligence.

Sequence learning is a crucial aspect of intelligence research, with sequence prediction tasks commonly used to evaluate the performance of sequence learning models. This paper introduces and tests a novel sequence learning model that mimics the structure of neocortical mini-columns and is grounded in Non-Axiomatic Logic, offering interpretability. The model’s learning mechanism encompasses three steps: hypothesizing, revising, and recycling, enabling it to operate effectively under conditions of insufficient knowledge and resources. The model’s performance is assessed using synthetic datasets for sequence prediction. The results demonstrate that the model consistently achieves high accuracy across various levels of difficulty, reaching the theoretical maximum. Furthermore, the model’s concept-centered representation effectively avoids catastrophic forgetting, a finding supported by the experimental results.

This paper aims to establish a consensus on AGI's definition. General intelligence refers to the adaptation to open environments according to certain principles using limited resources. It emphasizes that adaptation or learning is an indispensable property of intelligence, and places the controversial part within the principles of intelligence, which can be described from different perspectives.