2023 AAAI Tutorial: Advances in Neuro Symbolic Reasoning
The greatest promise here is analogous to experimental particle physics, where large particle accelerators are built to crash atoms together and monitor their behaviors. In natural language processing, researchers have built large models with massive amounts of data using deep neural networks that cost millions of dollars to train. The next step lies in studying the networks to see how this can improve the construction of symbolic representations required for higher order language tasks. Neuro-Symbolic AI represents an interdisciplinary field that harmoniously integrates neural networks, a fundamental component of deep learning, with symbolic reasoning techniques. Its overarching objective is to establish a synergistic connection between symbolic reasoning and statistical learning, harnessing the strengths of each approach. By adopting this hybrid methodology, machines can perform symbolic reasoning alongside exploiting the robust pattern recognition capabilities inherent in neural networks.
As these networks encounter data, the strength (or weight) of connections between neurons is adjusted, facilitating learning. This mimics the plasticity of the brain, allowing the model to adapt and evolve. The deep learning subset utilizes multi-layered networks, enabling nuanced pattern recognition, and making it effective for tasks like image processing.
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Forward chaining inference engines are the most common, and are seen in CLIPS and OPS5. Backward chaining occurs in Prolog, where a more limited logical representation is used, Horn Clauses. Programs were themselves data structures that other programs could operate on, allowing the easy definition of higher-level languages. The key AI programming language in the US during the last symbolic AI boom period was LISP.
What is symbolic AI and connectionist AI?
While symbolic AI posits the use of knowledge in reasoning and learning as critical to pro- ducing intelligent behavior, connectionist AI postulates that learning of associations from data (with little or no prior knowledge) is crucial for understanding behavior.
In 2014, Daniel Katz and his team at Illinois Tech trained a machine learning model to predict the decisions of Supreme Court Justices. In summary, symbolic AI excels at human-understandable reasoning, while Neural Networks are better suited for handling large and complex data sets. Integrating both approaches, known as neuro-symbolic AI, can provide the best of both worlds, combining the strengths of symbolic AI and Neural Networks to form a hybrid architecture capable of performing a wider range of tasks. We see Neuro-symbolic AI as a pathway to achieve artificial general intelligence.
The current state of symbolic AI
They have created a revolution in computer vision applications such as facial recognition and cancer detection. The neuro-symbolic model, NSCL, excels in this task, outperforming traditional models, emphasizing the potential of Neuro-Symbolic AI in understanding and reasoning about visual data. Notably, models trained on the CLEVRER dataset, which encompasses 10,000 videos, have outperformed their traditional counterparts in VQA tasks, indicating a bright future for Neuro-Symbolic approaches in visual reasoning. The two biggest flaws of deep learning are its lack of model interpretability (i.e. why did my model make that prediction?) and the amount of data that deep neural networks require in order to learn. As a consequence, the botmaster’s job is completely different when using symbolic AI technology than with machine learning-based technology, as the botmaster focuses on writing new content for the knowledge base rather than utterances of existing content.
Analog to the human concept learning, given the parsed program, the perception module learns visual concepts based on the language description of the object being referred to. Meanwhile, the learned visual concepts facilitate learning new words and parsing new sentences. We use curriculum learning to guide searching over the large compositional space of images and language. Extensive experiments demonstrate the accuracy and efficiency of our model on learning visual concepts, word representations, and semantic parsing of sentences. Further, our method allows easy generalization to new object attributes, compositions, language concepts, scenes and questions, and even new program domains. It also empowers applications including visual question answering and bidirectional image-text retrieval.
Neural AI focuses on learning patterns from data and making predictions or decisions based on the learned knowledge. It excels at tasks such as image and speech recognition, natural language processing, and sequential data analysis. Neural AI is more data-driven and relies on statistical learning rather than explicit rules. Complex problem solving through coupling of deep learning and symbolic components. Coupled neuro-symbolic systems are increasingly used to solve complex problems such as game playing or scene, word, sentence interpretation. In a different line of work, logic tensor networks in particular have been designed to capture logical background knowledge to improve image interpretation, and neural theorem provers can provide natural language reasoning by also taking knowledge bases into account.
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What is symbolic reasoning in artificial intelligence?
In symbolic reasoning, the rules are created through human intervention. That is, to build a symbolic reasoning system, first humans must learn the rules by which two phenomena relate, and then hard-code those relationships into a static program.