Understanding how humans use physical contact to interact with the world is
key to enabling human-centric artificial intelligence. While inferring 3D
contact is crucial for modeling realistic and physically-plausible human-object
interactions, existing methods either focus on 2D, consider body joints rather
than the surface, use coarse 3D body regions, or do not generalize to
in-the-wild images. In contrast, we focus on inferring dense, 3D contact
between the full body surface and objects in arbitrary images. To achieve this,
we first collect DAMON, a new dataset containing dense vertex-level contact
annotations paired with RGB images containing complex human-object and
human-scene contact. Second, we train DECO, a novel 3D contact detector that
uses both body-part-driven and scene-context-driven attention to estimate
vertex-level contact on the SMPL body. DECO builds on the insight that human
observers recognize contact by reasoning about the contacting body parts, their
proximity to scene objects, and the surrounding scene context. We perform
extensive evaluations of our detector on DAMON as well as on the RICH and
BEHAVE datasets. We significantly outperform existing SOTA methods across all
benchmarks. We also show qualitatively that DECO generalizes well to diverse
and challenging real-world human interactions in natural images. The code,
data, and models are available at this https URL

理解人类如何通过身体接触与世界互动对于实现以人为中心的人工智能至关重要。我们关注于推断全身表面与任意图像中的物体之间的密集 3D 接触，通过构建新的数据集 DAMON 以及训练新的 3D 接触检测器 DECO，在各种基准测试中显著优于现有方法，同时展示 DECO 在自然图像中广泛适用于多样且具有挑战性的真实人类互动。

DECO: 野外环境下 3D 人体场景接触密集估计

DECO: Dense Estimation of 3D Human-Scene Contact In The Wild

In the recent shift towards human-centric AI, the need for machines to
accurately use natural language has become increasingly important. While a
common approach to achieve this is to train large language models, this method
presents a form of learning misalignment where the model may not capture the
underlying structure and reasoning humans employ in using natural language,
potentially leading to unexpected or unreliable behavior. Emergent
communication (Emecom) is a field of research that has seen a growing number of
publications in recent years, aiming to develop artificial agents capable of
using natural language in a way that goes beyond simple discriminative tasks
and can effectively communicate and learn new concepts. In this review, we
present Emecom under two aspects. Firstly, we delineate all the common
proprieties we find across the literature and how they relate to human
interactions. Secondly, we identify two subcategories and highlight their
characteristics and open challenges. We encourage researchers to work together
by demonstrating that different methods can be viewed as diverse solutions to a
common problem and emphasize the importance of including diverse perspectives
and expertise in the field. We believe a deeper understanding of human
communication is crucial to developing machines that can accurately use natural
language in human-machine interactions.

发展人机互动中准确使用自然语言的机器，重要性愈发突显。本文回顾了近年来涉及新兴交流（Emergent communication）的文献，分析了其公共特性、两个子类别和挑战，并强调了多元视角和专业知识在领域中的重要性，主张研究者们共同努力以寻找共同问题的多种解决方案，加深对人类交流的理解，从而发展出更能准确使用自然语言的机器。

走向更贴近人类的 AI 交流：紧急交流研究综述

Towards More Human-like AI Communication: A Review of Emergent  Communication Research

Recent advances in large language models (LLMs) have led to the development
of powerful AI chatbots capable of engaging in natural and human-like
conversations. However, these chatbots can be potentially harmful, exhibiting
manipulative, gaslighting, and narcissistic behaviors. We define Healthy AI to
be safe, trustworthy and ethical. To create healthy AI systems, we present the
SafeguardGPT framework that uses psychotherapy to correct for these harmful
behaviors in AI chatbots. The framework involves four types of AI agents: a
Chatbot, a "User," a "Therapist," and a "Critic." We demonstrate the
effectiveness of SafeguardGPT through a working example of simulating a social
conversation. Our results show that the framework can improve the quality of
conversations between AI chatbots and humans. Although there are still several
challenges and directions to be addressed in the future, SafeguardGPT provides
a promising approach to improving the alignment between AI chatbots and human
values. By incorporating psychotherapy and reinforcement learning techniques,
the framework enables AI chatbots to learn and adapt to human preferences and
values in a safe and ethical way, contributing to the development of a more
human-centric and responsible AI.

本文提出 SafeguardGPT 框架，使用精神疗法纠正聊天机器人中具有潜在危害的行为，使得 AI chatbots 可以学习并适应人类喜好和价值观，以更安全、可靠和道德的方式，促进更加以人为中心和负责任的 AI 的发展。