The intersection of Artificial Intelligence (AI) and neuroscience in
Explainable AI (XAI) is pivotal for enhancing transparency and interpretability
in complex decision-making processes. This paper explores the evolution of XAI
methodologies, ranging from feature-based to human-centric approaches, and
delves into their applications in diverse domains, including healthcare and
finance. The challenges in achieving explainability in generative models,
ensuring responsible AI practices, and addressing ethical implications are
discussed. The paper further investigates the potential convergence of XAI with
cognitive sciences, the development of emotionally intelligent AI, and the
quest for Human-Like Intelligence (HLI) in AI systems. As AI progresses towards
Artificial General Intelligence (AGI), considerations of consciousness, ethics,
and societal impact become paramount. The ongoing pursuit of deciphering the
mysteries of the brain with AI and the quest for HLI represent transformative
endeavors, bridging technical advancements with multidisciplinary explorations
of human cognition.

人工智能与神经科学在可解释的人工智能中的交叉是增强复杂决策过程中透明度和可解释性的关键，本文探讨了从基于特征到以人为中心的方法的可解释的人工智能方法学的演变，并深入研究了它们在包括医疗保健和金融在内的不同领域的应用。同时讨论了在生成模型的可解释性、确保负责任的人工智能实践以及应对伦理影响方面的挑战。本文进一步研究了可解释的人工智能与认知科学的潜在融合、情感智能人工智能的发展以及人工智能系统中人类智能的追求。随着人工智能向人工通用智能的发展，对意识、伦理和社会影响的考虑变得至关重要。致力于通过人工智能解密大脑奥秘和实现人类智能的追求代表了结合技术进步与多学科人类认知探索的变革性努力。

推动可解释人工智能迈向人类智能：铸造人工大脑之路

Advancing Explainable AI Toward Human-Like Intelligence: Forging the  Path to Artificial Brain

Recognizing elementary underlying concepts from observations
(disentanglement) and generating novel combinations of these concepts
(compositional generalization) are fundamental abilities for humans to support
rapid knowledge learning and generalize to new tasks, with which the deep
learning models struggle. Towards human-like intelligence, various works on
disentangled representation learning have been proposed, and recently some
studies on compositional generalization have been presented. However, few works
study the relationship between disentanglement and compositional
generalization, and the observed results are inconsistent. In this paper, we
study several typical disentangled representation learning works in terms of
both disentanglement and compositional generalization abilities, and we provide
an important insight: vector-based representation (using a vector instead of a
scalar to represent a concept) is the key to empower both good disentanglement
and strong compositional generalization. This insight also resonates the
neuroscience research that the brain encodes information in neuron population
activity rather than individual neurons. Motivated by this observation, we
further propose a method to reform the scalar-based disentanglement works
($\beta$-TCVAE and FactorVAE) to be vector-based to increase both capabilities.
We investigate the impact of the dimensions of vector-based representation and
one important question: whether better disentanglement indicates higher
compositional generalization. In summary, our study demonstrates that it is
possible to achieve both good concept recognition and novel concept
composition, contributing an important step towards human-like intelligence.

在人工智能领域，学习并拥有类人类的智能需要处理认知的本质概念和生成这些概念的新的组合的能力，本文研究了这两种能力之间的关系，并发现基于向量的表示方法可以提高这两种能力，此观察结果与神经科学研究一致，为实现类人智能迈出了重要一步。

基于向量的表示是关键：有关解缠和组合泛化的研究

Vector-based Representation is the Key: A Study on Disentanglement and  Compositional Generalization

We discuss how over the last 30 to 50 years, Artificial Intelligence (AI)
systems that focused only on data have been handicapped, and how knowledge has
been critical in developing smarter, intelligent, and more effective systems.
In fact, the vast progress in AI can be viewed in terms of the three waves of
AI as identified by DARPA. During the first wave, handcrafted knowledge has
been at the center-piece, while during the second wave, the data-driven
approaches supplanted knowledge. Now we see a strong role and resurgence of
knowledge fueling major breakthroughs in the third wave of AI underpinning
future intelligent systems as they attempt human-like decision making, and seek
to become trusted assistants and companions for humans. We find a wider
availability of knowledge created from diverse sources, using manual to
automated means both by repurposing as well as by extraction. Using knowledge
with statistical learning is becoming increasingly indispensable to help make
AI systems more transparent and auditable. We will draw a parallel with the
role of knowledge and experience in human intelligence based on cognitive
science, and discuss emerging neuro-symbolic or hybrid AI systems in which
knowledge is the critical enabler for combining capabilities of the
data-intensive statistical AI systems with those of symbolic AI systems,
resulting in more capable AI systems that support more human-like intelligence.

在人工智能发展的三个阶段中，手工知识和数据驱动成为前两个阶段的关键，但现在的第三个阶段中，知识得到了全面的应用，使得 AI 系统更加具有人类智能化的思维，包括结合数据和符号 AI 系统的能力，以获得更高层次的人工智能。