Embedding Human and Articulated Object Interaction (HAOI) in 3D is an
important direction for a deeper human activity understanding. Different from
previous works that use parametric and CAD models to represent humans and
objects, in this work, we propose a novel 3D geometric primitive-based language
to encode both humans and objects. Given our new paradigm, humans and objects
are all compositions of primitives instead of heterogeneous entities. Thus,
mutual information learning may be achieved between the limited 3D data of
humans and different object categories. Moreover, considering the simplicity of
the expression and the richness of the information it contains, we choose the
superquadric as the primitive representation. To explore an effective embedding
of HAOI for the machine, we build a new benchmark on 3D HAOI consisting of
primitives together with their images and propose a task requiring machines to
recover 3D HAOI using primitives from images. Moreover, we propose a baseline
of single-view 3D reconstruction on HAOI. We believe this primitive-based 3D
HAOI representation would pave the way for 3D HAOI studies. Our code and data
are available at this https URL

提出了一种基于几何原语的三维人体和关节物体交互嵌入方法，使用超椭球作为基本表示，并构建了一个基于原语的三维人体和关节物体交互数据集，实现了机器对三维人体和关节物体交互的重建和识别任务。

基于基元的 3D 人物 - 物体交互建模与编程

Primitive-based 3D Human-Object Interaction Modelling and Programming

We envision a machine capable of solving mathematical problems. Dividing the
quantitative reasoning system into two parts: thought processes and cognitive
processes, we provide probabilistic descriptions of the architecture.

我们构想了一种能够解决数学问题的机器，将定量推理系统分为思维过程和认知过程，并提供了对该体系结构的概率描述。

数学推理的概率结果与认知交替

Probabilistic Results on the Architecture of Mathematical Reasoning  Aligned by Cognitive Alternation

We report on a series of eight workshops held in the summer of 2017 on the
topic "technology and consciousness." The workshops covered many subjects but
the overall goal was to assess the possibility of machine consciousness, and
its potential implications. In the body of the report, we summarize most of the
basic themes that were discussed: the structure and function of the brain,
theories of consciousness, explicit attempts to construct conscious machines,
detection and measurement of consciousness, possible emergence of a conscious
technology, methods for control of such a technology and ethical considerations
that might be owed to it. An appendix outlines the topics of each workshop and
provides abstracts of the talks delivered.
Update: Although this report was published in 2018 and the workshops it is
based on were held in 2017, recent events suggest that it is worth bringing
forward. In particular, in the Spring of 2022, a Google engineer claimed that
LaMDA, one of their "large language models" is sentient or even conscious. This
provoked a flurry of commentary in both the scientific and popular press, some
of it interesting and insightful, but almost all of it ignorant of the prior
consideration given to these topics and the history of research into machine
consciousness. Thus, we are making a lightly refreshed version of this report
available in the hope that it will provide useful background to the current
debate and will enable more informed commentary. Although this material is five
years old, its technical points remain valid and up to date, but we have
"refreshed" it by adding a few footnotes highlighting recent developments.

2017 年夏天，举办了 8 场关于 “技术与意识” 主题的研讨会，目的是评估机器意识的可能性以及其潜在影响。报告总结了讨论的主要主题，包括大脑的结构和功能、意识的理论、意识机器的构建、意识的检测和测量、意识技术的潜在出现、对技术的控制方法以及可能应对的伦理考虑。

技术与意识

Technology and Consciousness

Idioms are an important language phenomenon in Chinese, but idiom translation
is notoriously hard. Current machine translation models perform poorly on idiom
translation, while idioms are sparse in many translation datasets. We present
PETCI, a parallel English translation dataset of Chinese idioms, aiming to
improve idiom translation by both human and machine. The dataset is built by
leveraging human and machine effort. Baseline generation models show
unsatisfactory abilities to improve translation, but structure-aware
classification models show good performance on distinguishing good
translations. Furthermore, the size of PETCI can be easily increased without
expertise. Overall, PETCI can be helpful to language learners and machine
translation systems.

提出了 PETCI 数据集，该数据集是一组英汉对照的成语翻译，通过分类模型，在改善机器翻译的同时也可以提供给语言学习者参考。