Emergence of Machine Language: Towards Symbolic Intelligence with Neural Networks

• URL: http://arxiv.org/abs/2201.05489v1
• Date: Fri, 14 Jan 2022 14:54:58 GMT
• Title: Emergence of Machine Language: Towards Symbolic Intelligence with Neural Networks
• Authors: Yuqi Wang, Xu-Yao Zhang, Cheng-Lin Liu, Zhaoxiang Zhang
• Abstract summary: We propose to combine symbolism and connectionism principles by using neural networks to derive a discrete representation. By designing an interactive environment and task, we demonstrated that machines could generate a spontaneous, flexible, and semantic language.
• Score: 73.94290462239061
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. Although this is reasonable from the statistical perspective, it has other major problems like lacking interpretability, poor generalization, and is easy to be attacked. Since both paradigms have strengths and weaknesses, a better choice is to seek reconciliation. In this paper, we make an initial attempt towards this direction. Specifically, we propose to combine symbolism and connectionism principles by using neural networks to derive a discrete representation. This process is highly similar to human language, which is a natural combination of discrete symbols and neural systems, where the brain processes continuous signals and represents intelligence via discrete language. To mimic this functionality, we denote our approach as machine language. By designing an interactive environment and task, we demonstrated that machines could generate a spontaneous, flexible, and semantic language through cooperation. Moreover, through experiments we show that discrete language representation has several advantages compared with continuous feature representation, from the aspects of interpretability, generalization, and robustness.

Related papers

• VisualPredicator: Learning Abstract World Models with Neuro-Symbolic Predicates for Robot Planning [86.59849798539312]
  We present Neuro-Symbolic Predicates, a first-order abstraction language that combines the strengths of symbolic and neural knowledge representations. We show that our approach offers better sample complexity, stronger out-of-distribution generalization, and improved interpretability.
  arXiv  Detail & Related papers  (2024-10-30T16:11:05Z)
• Sparse Autoencoders Find Highly Interpretable Features in Language Models [0.0]
  Polysemanticity prevents us from identifying concise, human-understandable explanations for what neural networks are doing internally. We use sparse autoencoders to reconstruct the internal activations of a language model. Our method may serve as a foundation for future mechanistic interpretability work.
  arXiv  Detail & Related papers  (2023-09-15T17:56:55Z)
• From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought [124.40905824051079]
  We propose rational meaning construction, a computational framework for language-informed thinking. We frame linguistic meaning as a context-sensitive mapping from natural language into a probabilistic language of thought. We show that LLMs can generate context-sensitive translations that capture pragmatically-appropriate linguistic meanings. We extend our framework to integrate cognitively-motivated symbolic modules.
  arXiv  Detail & Related papers  (2023-06-22T05:14:00Z)
• SNeL: A Structured Neuro-Symbolic Language for Entity-Based Multimodal Scene Understanding [0.0]
  We introduce SNeL (Structured Neuro-symbolic Language), a versatile query language designed to facilitate nuanced interactions with neural networks processing multimodal data. SNeL's expressive interface enables the construction of intricate queries, supporting logical and arithmetic operators, comparators, nesting, and more. Our evaluations demonstrate SNeL's potential to reshape the way we interact with complex neural networks.
  arXiv  Detail & Related papers  (2023-06-09T17:01:51Z)
• Minding Language Models' (Lack of) Theory of Mind: A Plug-and-Play Multi-Character Belief Tracker [72.09076317574238]
  ToM is a plug-and-play approach to investigate the belief states of characters in reading comprehension. We show that ToM enhances off-the-shelf neural network theory mind in a zero-order setting while showing robust out-of-distribution performance compared to supervised baselines.
  arXiv  Detail & Related papers  (2023-06-01T17:24:35Z)
• Learning an Artificial Language for Knowledge-Sharing in Multilingual Translation [15.32063273544696]
  We discretize the latent space of multilingual models by assigning encoder states to entries in a codebook. We validate our approach on large-scale experiments with realistic data volumes and domains. We also use the learned artificial language to analyze model behavior, and discover that using a similar bridge language increases knowledge-sharing among the remaining languages.
  arXiv  Detail & Related papers  (2022-11-02T17:14:42Z)
• Multitasking Models are Robust to Structural Failure: A Neural Model for Bilingual Cognitive Reserve [78.3500985535601]
  We find a surprising connection between multitask learning and robustness to neuron failures. Our experiments show that bilingual language models retain higher performance under various neuron perturbations. We provide a theoretical justification for this robustness by mathematically analyzing linear representation learning.
  arXiv  Detail & Related papers  (2022-10-20T22:23:27Z)
• Join-Chain Network: A Logical Reasoning View of the Multi-head Attention in Transformer [59.73454783958702]
  We propose a symbolic reasoning architecture that chains many join operators together to model output logical expressions. In particular, we demonstrate that such an ensemble of join-chains can express a broad subset of ''tree-structured'' first-order logical expressions, named FOET. We find that the widely used multi-head self-attention module in transformer can be understood as a special neural operator that implements the union bound of the join operator in probabilistic predicate space.
  arXiv  Detail & Related papers  (2022-10-06T07:39:58Z)
• Enhancing Interpretability and Interactivity in Robot Manipulation: A Neurosymbolic Approach [0.0]
  We present a neurosymbolic architecture for coupling language-guided visual reasoning with robot manipulation. A non-expert human user can prompt the robot using unconstrained natural language, providing a referring expression (REF), a question (VQA) or a grasp action instruction. We generate a 3D vision-and-language synthetic dataset of tabletop scenes in a simulation environment to train our approach and perform extensive evaluations in both synthetic and real-world scenes.
  arXiv  Detail & Related papers  (2022-10-03T12:21:45Z)
• Discrete and continuous representations and processing in deep learning: Looking forward [18.28761409764605]
  We argue that combining discrete and continuous representations and their processing will be essential to build systems that exhibit a general form of intelligence. We suggest and discuss several avenues that could improve current neural networks with the inclusion of discrete elements to combine the advantages of both types of representations.
  arXiv  Detail & Related papers  (2022-01-04T16:30:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.