发展智力理论的八大挑战

IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Frontiers in Computational Neuroscience Pub Date : 2024-07-24 DOI:10.3389/fncom.2024.1388166
Haiping Huang
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引用次数: 0

摘要

一个好的数学美学理论比当前的任何观测都更实用,因为关于物理现实的新预测可以自洽地得到验证。这一信念适用于理解深度神经网络(包括大型语言模型)甚至生物智能的现状。玩具模型为物理现实提供了一种隐喻,可以用数学方法表述现实(即所谓的理论),并随着更多猜想的证实或反驳而更新。我们不需要在模型中呈现所有细节,而是要构建更抽象的模型,因为大脑或深层网络等复杂系统有许多马虎的维度,但对宏观观测指标有强烈影响的僵硬维度却少得多。在理解自然或人工智能的现代,这种自下而上的机理建模仍大有可为。在此,我们将阐明按照这种理论范式发展智能理论所面临的八大挑战。这些挑战包括表征学习、泛化、对抗鲁棒性、持续学习、因果学习、大脑内部模型、下一个标记预测以及主观体验的机制。
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Eight challenges in developing theory of intelligence
A good theory of mathematical beauty is more practical than any current observation, as new predictions about physical reality can be self-consistently verified. This belief applies to the current status of understanding deep neural networks including large language models and even the biological intelligence. Toy models provide a metaphor of physical reality, allowing mathematically formulating the reality (i.e., the so-called theory), which can be updated as more conjectures are justified or refuted. One does not need to present all details in a model, but rather, more abstract models are constructed, as complex systems such as the brains or deep networks have many sloppy dimensions but much less stiff dimensions that strongly impact macroscopic observables. This type of bottom-up mechanistic modeling is still promising in the modern era of understanding the natural or artificial intelligence. Here, we shed light on eight challenges in developing theory of intelligence following this theoretical paradigm. Theses challenges are representation learning, generalization, adversarial robustness, continual learning, causal learning, internal model of the brain, next-token prediction, and the mechanics of subjective experience.
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来源期刊
Frontiers in Computational Neuroscience
Frontiers in Computational Neuroscience MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
5.30
自引率
3.10%
发文量
166
审稿时长
6-12 weeks
期刊介绍: Frontiers in Computational Neuroscience is a first-tier electronic journal devoted to promoting theoretical modeling of brain function and fostering interdisciplinary interactions between theoretical and experimental neuroscience. Progress in understanding the amazing capabilities of the brain is still limited, and we believe that it will only come with deep theoretical thinking and mutually stimulating cooperation between different disciplines and approaches. We therefore invite original contributions on a wide range of topics that present the fruits of such cooperation, or provide stimuli for future alliances. We aim to provide an interactive forum for cutting-edge theoretical studies of the nervous system, and for promulgating the best theoretical research to the broader neuroscience community. Models of all styles and at all levels are welcome, from biophysically motivated realistic simulations of neurons and synapses to high-level abstract models of inference and decision making. While the journal is primarily focused on theoretically based and driven research, we welcome experimental studies that validate and test theoretical conclusions. Also: comp neuro
期刊最新文献
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