Intelligent systems based on first-order logic on the one hand, and on artificial neural networks (also called connectionist systems) on the other, differ substantially. It would be very desirable to combine the robust neural networking machinery with symbolic knowledge representation and reasoning paradigms like logic programming in such a way that the strengths of either paradigm will be retained. Current state-of-the-art research, however, fails by far to achieve this ultimate goal. As one of the main obstacles to be overcome we perceive the question how symbolic knowledge can be encoded by means of connectionist systems: Satisfactory answers to this will naturally lead the way to knowledge extraction algorithms and to integrated neural-symbolic systems.
Shri Mata Vaishno Devi University（什里马塔瓦伊什诺德维大学）；Orthogonal Research and Education Lab（正交研究与教育实验室）；OpenWorm Foundation（OpenWorm基金会）
While Artificial Neural Networks (ANNs) have yielded impressive results in the realm of simulated intelligent behavior, it is important to remember that they are but sparse approximations of Biological Neural Networks (BNNs). We go beyond comparison of ANNs and BNNs to introduce principles from BNNs that might guide the further development of ANNs as embodied neural models. These principles include representational complexity, complex network structure/energetics, and robust function. We then consider these principles in ways that might be implemented in the future development of ANNs. In conc…
KU Leuven, Department of Computer Science（比利时鲁汶大学）
Neuro-symbolic and statistical relational artificial intelligence both integrate frameworks for learning with logical reasoning. This survey identifies several parallels across seven different dimensions between these two fields. These cannot only be used to characterize and position neuro-symbolic artificial intelligence approaches but also to identify a number of directions for further research.
Knowledge-based or Artificial Intelligence techniques are used increasingly as alternatives to more classical techniques to model ENVIRONMENTAL SYSTEMS. Use of Artificial Intelligence (AI) in environmental modelling has increased with recognition of its potential. In this paper we examine the DIFFERENT TECHNIQUES of Artificial intelligence with profound examples of human perception, learning and reasoning to solve complex problems. However with the increase of complexity better methods are required. Keeping in view of the above some researchers introduced the idea of hybrid mechanism in which …
Neuro-Symbolic Artificial Intelligence -- the combination of symbolic methods with methods that are based on artificial neural networks -- has a long-standing history. In this article, we provide a structured overview of current trends, by means of categorizing recent publications from key conferences. The article is meant to serve as a convenient starting point for research on the general topic.
Institute of Deep Perception Technology, JITRI（江苏产业技术研究院深度感知技术研究所）
The symbolism, connectionism and behaviorism approaches of artificial intelligence have achieved a lot of successes in various tasks, while we still do not have a clear definition of "intelligence" with enough consensus in the community (although there are over 70 different "versions" of definitions). The nature of intelligence is still in darkness. In this work we do not take any of these three traditional approaches, instead we try to identify certain fundamental aspects of the nature of intelligence, and construct a mathematical model to represent and potentially reproduce these fundamental…
University of Colorado, Denver（科罗拉多大学丹佛分校）；Dell Technologies Inc（戴尔科技公司）
The field of artificial intelligence has seen explosive growth and exponential success. The last phase of development showcased deep learnings ability to solve a variety of difficult problems across a multitude of domains. Many of these networks met and exceeded human benchmarks by becoming experts in the domains in which they are trained. Though the successes of artificial intelligence have begun to overshadow its failures, there is still much that separates current artificial intelligence tools from becoming the exceptional general learners that humans are. In this paper, we identify the ten…
The algorithm of brain learning and memory is still undetermined. The backpropagation algorithm of artificial neural networks was thought not suitable for brain cortex, and there is a lack of algorithm for memory engram. We designed a brain version of backpropagation algorithm, which are biologically plausible and could be implemented with virtual neurons to complete image classification task. An encoding algorithm that can automatically allocate engram cells is proposed, which is an algorithm implementation for memory engram theory, and could simulate how hippocampus achieve fast associative …
The field of machine learning has focused, primarily, on discretized sub-problems (i.e. vision, speech, natural language) of intelligence. While neuroscience tends to be observation heavy, providing few guiding theories. It is unlikely that artificial intelligence will emerge through only one of these disciplines. Instead, it is likely to be some amalgamation of their algorithmic and observational findings. As a result, there are a number of problems that should be addressed in order to select the beneficial aspects of both fields. In this article, we propose leading questions to guide the fut…
Complex systems fail. I argue that failures can be a blueprint characterizing living organisms and biological intelligence, a control mechanism to increase complexity in evolutionary simulations, and an alternative to classical fitness optimization. Imitating biological successes in Artificial Life and Artificial Intelligence can be misleading; imitating failures offers a path towards understanding and emulating life it in artificial systems.
Plymouth University（普利茅斯大学）；Bernstein Center for Computational Neuroscience Berlin（柏林伯恩斯坦计算神经科学中心）；Humboldt-Universität zu Berlin（柏林洪堡大学）；Inria Sophia-Antipolis Méditerranée（法国国家信息与自动化研究所索菲亚-安蒂波利斯地中海研究中心）
Computation is classically studied in terms of automata, formal languages and algorithms; yet, the relation between neural dynamics and symbolic representations and operations is still unclear in traditional eliminative connectionism. Therefore, we suggest a unique perspective on this central issue, to which we would like to refer as to transparent connectionism, by proposing accounts of how symbolic computation can be implemented in neural substrates. In this study we first introduce a new model of dynamics on a symbolic space, the versatile shift, showing that it supports the real-time simul…
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）
Representation is a core issue in artificial intelligence. Humans use discrete language to communicate and learn from each other, while machines use continuous features (like vector, matrix, or tensor in deep neural networks) to represent cognitive patterns. Discrete symbols are low-dimensional, decoupled, and have strong reasoning ability, while continuous features are high-dimensional, coupled, and have incredible abstracting capabilities. In recent years, deep learning has developed the idea of continuous representation to the extreme, using millions of parameters to achieve high accuracies…
Artificial Intelligence has been a growth catalyst to our society and is cosidered across all idustries as a fundamental technology. However, its development has been limited to the signal processing domain that relies on the generated and collected data from other sensors. In recent research, concepts of Digital Artificial Intelligence and Physicial Artifical Intelligence have emerged and this can be considered a big step in the theoretical development of Artifical Intelligence. In this paper we explore the concept of Physicial Artifical Intelligence and propose two subdomains: Integrated Phy…
In this paper, I put forward that in many instances, thinking mechanisms are equivalent to artificial intelligence modules programmed into the human mind.
This is the Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, which was held in Stockholm Sweden, July 30 - August 1, 1999
A short review of the literature on measurement and detection of artificial general intelligence is made. Proposed benchmarks and tests for artificial general intelligence are critically evaluated against multiple criteria. Based on the findings, the most promising approaches are identified and some useful directions for future work are proposed.
This is the Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence, which was held in San Francisco, CA, June 30 - July 3, 2000
This is the Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence, which was held in Edinburgh, Scotland July 26 - 29 2005.
This is the Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial Intelligence, which was held in Helsinki, Finland, July 9 - 12 2008.
This is the Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence, which was held in Barcelona, Spain, July 14 - 17 2011.
The TriRhenaTech alliance universities and their partners presented their competences in the field of artificial intelligence and their cross-border cooperations with the industry at the tri-national conference 'Artificial Intelligence : from Research to Application' on March 13th, 2019 in Offenburg. The TriRhenaTech alliance is a network of universities in the Upper Rhine Trinational Metropolitan Region comprising of the German universities of applied sciences in Furtwangen, Kaiserslautern, Karlsruhe, and Offenburg, the Baden-Wuerttemberg Cooperative State University Loerrach, the French univ…
This is the Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, which was held in Montreal, QC, Canada, June 18 - 21 2009.
This is the Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence, which was held on Catalina Island, CA, July 8 - 11 2010.
This is the Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, which was held on Catalina Island, CA August 14-18 2012.
University of Brasilia（巴西利亚大学）；University of Sao Paulo（圣保罗大学）
This article reviews the "Once learning" mechanism that was proposed 23 years ago and the subsequent successes of "One-shot learning" in image classification and "You Only Look Once - YOLO" in objective detection. Analyzing the current development of Artificial Intelligence (AI), the proposal is that AI should be clearly divided into the following categories: Artificial Human Intelligence (AHI), Artificial Machine Intelligence (AMI), and Artificial Biological Intelligence (ABI), which will also be the main directions of theory and application development for AI. As a watershed for the branches…
This is the Proceedings of the Twenty-Third Conference on Uncertainty in Artificial Intelligence, which was held in Vancouver, British Columbia, July 19 - 22 2007.
Narrative intelligence is the ability to craft, tell, understand, and respond affectively to stories. We argue that instilling artificial intelligences with computational narrative intelligence affords a number of applications beneficial to humans. We lay out some of the machine learning challenges necessary to solve to achieve computational narrative intelligence. Finally, we argue that computational narrative is a practical step towards machine enculturation, the teaching of sociocultural values to machines.
School of Economics and Management, University of Chinese Academy of Sciences（中国科学院大学经济与管理学院）
Although artificial intelligence is currently one of the most interesting areas in scientific research, the potential threats posed by emerging AI systems remain a source of persistent controversy. To address the issue of AI threat, this study proposes a standard intelligence model that unifies AI and human characteristics in terms of four aspects of knowledge, i.e., input, output, mastery, and creation. Using this model, we observe three challenges, namely, expanding of the von Neumann architecture; testing and ranking the intelligence quotient of naturally and artificially intelligent system…
Digital twin brain: a bridge between biological intelligence and artificial intelligence
Brainnetome Center, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所脑网络组研究中心）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Research Center for Augmented Intelligence, Zhejiang Lab（之江实验室增强智能研究中心）
Thanks to rapid development of artificial intelligence technology in recent years, the current artificial intelligence technology is contributing to many part of society. Education, environment, medical care, military, tourism, economy, politics, etc. are having a very large impact on society as a whole. For example, in the field of education, there is an artificial intelligence tutoring system that automatically assigns tutors based on student's level. In the field of economics, there are quantitative investment methods that automatically analyze large amounts of data to find investment laws …
Recent advances in the development of artificial intelligence, technological progress acceleration, long-term trends of macroeconomic dynamics increase the relevance of technological singularity hypothesis. In this paper, we build a model of finite-time technological singularity assuming that artificial intelligence will replace humans for artificial intelligence engineers after some point in time when it is developed enough. This model implies the following: let A be the level of development of artificial intelligence. Then, the moment of technological singularity n is defined as the point in…
