We revisit the classic signal-to-symbol barrier in light of the remarkable
ability of deep neural networks to generate realistic synthetic data. DeepFakes
and spoofing highlight the feebleness of the link between physical reality and
its abstract representation, whether learned by a digital computer or a
biological agent. Starting from a widely applicable definition of abstract
concept, we show that standard feed-forward architectures cannot capture but
trivial concepts, regardless of the number of weights and the amount of
training data, despite being extremely effective classifiers. On the other
hand, architectures that incorporate recursion can represent a significantly
larger class of concepts, but may still be unable to learn them from a finite
dataset. We qualitatively describe the class of concepts that can be
"understood" by modern architectures trained with variants of stochastic
gradient descent, using a (free energy) Lagrangian to measure information
complexity. Even if a concept has been understood, however, a network has no
means of communicating its understanding to an external agent, except through
continuous interaction and validation. We then characterize physical objects as
abstract concepts and use the previous analysis to show that physical objects
can be encoded by finite architectures. However, to understand physical
concepts, sensors must provide persistently exciting observations, for which
the ability to control the data acquisition process is essential (active
perception). The importance of control depends on the modality, benefiting
visual more than acoustic or chemical perception. Finally, we conclude that
binding physical entities to digital identities is possible in finite time with
finite resources, solving in principle the signal-to-symbol barrier problem,
but we highlight the need for continuous validation.

在现代架构上，使用随机梯度下降算法训练模型可以更好地了解抽象概念，但仍然需要不断交互和验证以理解物理概念，并通过控制数据采集过程获得积极的观察结果。然而，使用有限的资源和时间将物理实体绑定到数字身份是解决信号到符号障碍问题的一个潜在方法，但需要不断的验证和咨询。

物理概念的可学习性：神经网络能否理解真实世界？

On the Learnability of Physical Concepts: Can a Neural Network Understand What's Real?

Backdoor attacks embed hidden malicious behaviors into deep learning models,
which only activate and cause misclassifications on model inputs containing a
specific trigger. Existing works on backdoor attacks and defenses, however,
mostly focus on digital attacks that use digitally generated patterns as
triggers. A critical question remains unanswered: can backdoor attacks succeed
using physical objects as triggers, thus making them a credible threat against
deep learning systems in the real world? We conduct a detailed empirical study
to explore this question for facial recognition, a critical deep learning task.
Using seven physical objects as triggers, we collect a custom dataset of 3205
images of ten volunteers and use it to study the feasibility of physical
backdoor attacks under a variety of real-world conditions. Our study reveals
two key findings. First, physical backdoor attacks can be highly successful if
they are carefully configured to overcome the constraints imposed by physical
objects. In particular, the placement of successful triggers is largely
constrained by the target model's dependence on key facial features. Second,
four of today's state-of-the-art defenses against (digital) backdoors are
ineffective against physical backdoors, because the use of physical objects
breaks core assumptions used to construct these defenses. Our study confirms
that (physical) backdoor attacks are not a hypothetical phenomenon but rather
pose a serious real-world threat to critical classification tasks. We need new
and more robust defenses against backdoors in the physical world.

本文针对实体物体作为触发器所形成的后门攻击，进行一个详细的实证研究，并探究了针对这种攻击的防御方法。研究发现，实体后门攻击非常成功，尤其是对于关键面部特征高度依赖的系统，而且这些攻击可以规避现有的数字攻击防御去针对后门攻击的核心假设。因此，我们需要针对物理世界中的后门攻击，建立更为强大的防御方法。

物理世界中针对深度学习系统的后门攻击

Backdoor Attacks Against Deep Learning Systems in the Physical World

The ability to map descriptions of scenes to 3D geometric representations has
many applications in areas such as art, education, and robotics. However, prior
work on the text to 3D scene generation task has used manually specified object
categories and language that identifies them. We introduce a dataset of 3D
scenes annotated with natural language descriptions and learn from this data
how to ground textual descriptions to physical objects. Our method successfully
grounds a variety of lexical terms to concrete referents, and we show
quantitatively that our method improves 3D scene generation over previous work
using purely rule-based methods. We evaluate the fidelity and plausibility of
3D scenes generated with our grounding approach through human judgments. To
ease evaluation on this task, we also introduce an automated metric that
strongly correlates with human judgments.

该论文介绍了如何使用自然语言描述来学习将文本场景映射到三维几何表示中的方法，并且使用基于规则的方法来生成三维场景的方法得到了改进。他们还引入了一种自动化评估度量来评估生成的 3D 场景。