We here analyse the question of developing artificial consciousness from an
evolutionary perspective, taking the evolution of the human brain and its
relation with consciousness as a reference model. This kind of analysis reveals
several structural and functional features of the human brain that appear to be
key for reaching human-like complex conscious experience and that current
research on Artificial Intelligence (AI) should take into account in its
attempt to develop systems capable of conscious processing. We argue that, even
if AI is limited in its ability to emulate human consciousness for both
intrinsic (structural and architectural) and extrinsic (related to the current
stage of scientific and technological knowledge) reasons, taking inspiration
from those characteristics of the brain that make conscious processing possible
and/or modulate it, is a potentially promising strategy towards developing
conscious AI. Also, it is theoretically possible that AI research can develop
partial or potentially alternative forms of consciousness that is qualitatively
different from the human, and that may be either more or less sophisticated
depending on the perspectives. Therefore, we recommend neuroscience-inspired
caution in talking about artificial consciousness: since the use of the same
word consciousness for humans and AI becomes ambiguous and potentially
misleading, we propose to clearly specify what is common and what differs in AI
conscious processing from full human conscious experience.

从进化的角度分析了开发人工意识的问题，以人类大脑的进化和与意识的关系作为参考模型，揭示了人脑的结构和功能特征，认为现有的人工智能研究应该考虑这些特征，同时提出了在开发具有意识处理能力的系统时应从脑部特征中汲取灵感的策略。

人工意识是否可行？从人类大脑中得出的教训

Is artificial consciousness achievable? Lessons from the human brain

Click-Through Rate (CTR) prediction is the most critical task in product and
content recommendation, and learning effective feature interaction is the key
challenge to exploiting user preferences for products. Some recent research
works focus on investigating more sophisticated feature interactions based on
soft attention or gate mechanism, while some redundant or contradictory feature
combinations are still introduced. According to Global Workspace Theory in
conscious processing, human clicks on advertisements ``consciously'': only a
specific subset of product features are considered, and the rest are not
involved in conscious processing. Therefore, we propose a CTR model that
\textbf{D}irectly \textbf{E}nhances the embeddings and \textbf{L}everages
\textbf{T}runcated Conscious \textbf{A}ttention during feature interaction,
termed DELTA, which contains two key components: (I) conscious truncation
module (CTM), which utilizes curriculum learning to apply adaptive truncation
on attention weights to select the most critical feature combinations; (II)
direct embedding enhancement module (DEM), which directly and independently
propagates gradient from the loss layer to the embedding layer to enhance the
crucial embeddings via linear feature crossing without introducing any extra
cost during inference. Extensive experiments on five challenging CTR datasets
demonstrate that DELTA achieves cutting-edge performance among current
state-of-the-art CTR methods.

提出 DELTA 算法用于提高 CTR 预测准确率，通过意识截断模块和直接嵌入增强模块实现对关键特征交叉的强化，实验证明其在 CTR 任务中表现优异。