Distance teaching has become popular these years because of the COVID-19
epidemic. However, both students and teachers face several challenges in
distance teaching, like being easy to distract. We proposed Focus+, a system
designed to detect learners' status with the latest AI technology from their
web camera to solve such challenges. By doing so, teachers can know students'
status, and students can regulate their learning experience. In this research,
we will discuss the expected model's design for training and evaluating the AI
detection model of Focus+.

为了解决远程教学中学生和教师容易分散注意力的问题，本研究设计了一种名为 Focus+ 的系统，采用最新的人工智能技术从学生的网络摄像头中检测学生的状态，以便为教师和学生调节学习体验。本文将讨论 Focus+ 的预期模型设计，以培训和评估 AI 检测模型。

Focus Plus：远程教学中通过网络摄像头检测学生的分心情况

Focus Plus: Detect Learner's Distraction by Web Camera in Distance Teaching

Although AI-based systems are increasingly being leveraged to provide value
to organizations, individuals, and society, significant attendant risks have
been identified. These risks have led to proposed regulations, litigation, and
general societal concerns.
As with any promising technology, organizations want to benefit from the
positive capabilities of AI technology while reducing the risks. The best way
to reduce risks is to implement comprehensive AI lifecycle governance where
policies and procedures are described and enforced during the design,
development, deployment, and monitoring of an AI system. While support for
comprehensive governance is beginning to emerge, organizations often need to
identify the risks of deploying an already-built model without knowledge of how
it was constructed or access to its original developers.
Such an assessment will quantitatively assess the risks of an existing model
in a manner analogous to how a home inspector might assess the energy
efficiency of an already-built home or a physician might assess overall patient
health based on a battery of tests. This paper explores the concept of a
quantitative AI Risk Assessment, exploring the opportunities, challenges, and
potential impacts of such an approach, and discussing how it might improve AI
regulations.

介绍了通过执行全面的 AI 生命周期治理来降低人工智能风险的最佳方法，定量评估现有模型的风险可以类比于如何评估已经建造的房屋的能源效率或医生根据一系列测试评估整体患者健康状况，本文探讨了这一思路，并讨论了这种方法如何改善 AI 的监管。

量化人工智能风险评估：机遇与挑战

Quantitative AI Risk Assessments: Opportunities and Challenges

As humans we are driven by a strong desire for seeking novelty in our world.
Also upon observing a novel pattern we are capable of refining our
understanding of the world based on the new information---humans can discover
their world. The outstanding ability of the human mind for discovery has led to
many breakthroughs in science, art and technology. Here we investigate the
possibility of building an agent capable of discovering its world using the
modern AI technology. In particular we introduce NDIGO, Neural Differential
Information Gain Optimisation, a self-supervised discovery model that aims at
seeking new information to construct a global view of its world from partial
and noisy observations. Our experiments on some controlled 2-D navigation tasks
show that NDIGO outperforms state-of-the-art information-seeking methods in
terms of the quality of the learned representation. The improvement in
performance is particularly significant in the presence of white or structured
noise where other information-seeking methods follow the noise instead of
discovering their world.

本文介绍了一种名为 NDIGO 的神经差分信息增益优化自我监督发现模型，旨在利用现代人工智能技术建立一个能够自主发现其世界的代理工具。通过对一些受控的二维导航任务的实验，发现 NDIGO 在学习表示质量方面的表现优于现有的信息寻求方法，尤其是在存在白色或结构噪声的情况下，其他信息寻找方法还停留在噪声中，并没有真正发现其世界。