Machine Learning in Lung Cancer Radiomics

Long term cost-effectiveness of resilient foods for global catastrophes compared to artificial general intelligence safety

“…Method 2 is estimating the reduction in long-term value of humanity from AGI directly (mean of 8%) (increased from the original model) and also described by a beta distribution, parameters (𝛂𝛂 = 1.2, 𝛃𝛃 = 15) [51] (Table 2). Two thirds weight 12 Discussion of limitations, and assumptions of the Machine Intelligence Research Institute cost effectiveness model available at Oxford Prioritisation Project website [57]. 13 AGI Catastrophe is used to describe scenarios in which AGI permanently and drastically curtails the potential of humanity.…”

“…full-scale nuclear war and 10% agricultural loss. Parameter sensitivity within the Artificial Intelligence Submodel was not investigated as the submodel was adapted from previous work by the Oxford Prioritisation Project, which considered uncertainties within the AGI safety cost effectiveness submodel [57].…”

“…AI's, post humans, etc 6. The Oxford Prioritisation Project -Machine Intelligence Research Institute Cost-effectiveness Model, which the AGI submodel is based on, attempts to quantify expected value of far future, in human-equivalent well-being-adjusted life-years (HEWALYs)[57,58]…”

Fully Data-Driven Pseudo Label Estimation for Pointly-Supervised Panoptic Segmentation

“…Panoptic segmentation is a computer vision task that partitions an image into non-overlapping masks for both thing objects and stuff categories (Kirillov et al 2019b). With the development of neural network technology (Zhiqiang Chen 2022; Mengya Jianing Han 2023;Jiaqi Li 2023;Qi Zheng 2023;Guyue Hu 2023;Cheng-Cheng Ma 2023), deep learning-based panoptic segmentation models have shown promising performance, but their effectiveness relies heavily on pixel-wise training labels, and annotating these labels is time-consuming. The high annotation cost hinders the widespread use of these methods in practical applications.…”