Is Neuro-Symbolic AI Meeting its Promise in Natural Language Processing? A Structured Review

System thinking is an approach that recognizes and analyzes the interconnections between all the components within a system, including relationships, feedback loops, and cause-and-effect chains. Applying system thinking in product design allows designers to consider the broader context in which their products will be used, leading to more effective and sustainable solutions. It’s not just about fixing problems, but also about really understanding and caring for the person you’re helping. When someone comes to us with a problem, they want to be heard and understood, not just get a quick fix. It gives tips and examples so that every chat with a customer feels helpful and kind. Finally, this chapter also covered how one might exploit a set of defined logical propositions to evaluate other expressions and generate conclusions.

We discussed the process and intuition behind formalizing these symbols into logical propositions by declaring relations and logical connectives. Moreover, Symbolic AI allows the intelligent assistant to make decisions regarding the speech duration and other features, such as intonation when reading the feedback to the user. Modern dialog systems (such as ChatGPT) rely on end-to-end deep learning frameworks and do not depend much on Symbolic AI. Similar logical processing is also utilized in search engines to structure the user’s prompt and the semantic web domain.

Robust similarity between hypergraphs based on valuations and mathematical morphology operators

As such, this chapter also examined the idea of intelligence and how one might represent knowledge through explicit symbols to enable intelligent systems. Thomas Hobbes, a British philosopher, famously said that thinking is nothing more than symbol manipulation, and our ability to reason is essentially our mind computing that symbol manipulation. René Descartes also compared our thought process to symbolic representations. Our thinking process essentially becomes a mathematical algebraic manipulation of symbols.

Finally, Nouvelle AI excels in reactive and real-world robotics domains but has been criticized for difficulties in incorporating learning and knowledge. In Symbolic AI, knowledge is explicitly encoded in the form of symbols, rules, and relationships. These symbols can represent objects, concepts, or situations, and the rules define how these symbols can be manipulated or combined to derive new knowledge or make inferences. The reasoning process is typically based on formal logic, allowing the AI system to make conclusions based on the given knowledge.

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Natural language understanding, in contrast, constructs a meaning representation and uses that for further processing, such as answering questions. Expert systems can operate in either a forward chaining – from evidence to conclusions – or backward chaining – from goals to needed data and prerequisites – manner. More advanced knowledge-based systems, such as Soar can also perform meta-level reasoning, that is reasoning about their own reasoning in terms of deciding how to solve problems and monitoring the success of problem-solving strategies. Nearly every machine learning model available today uses the same core learning algorithm. Be it Tesla Autopilot, Stable Diffusion, or ChatGPT, they all learn via gradient descent. As the author of this article, I invite you to interact with “AskMe,” a feature powered by the data in the knowledge graph integrated into this blog.

Production rules connect symbols in a relationship similar to an If-Then statement. The expert system processes the rules to make deductions and to determine what additional information it needs, i.e. what questions to ask, using human-readable symbols. For example, OPS5, CLIPS and their successors Jess and Drools operate in this fashion. John McCarthy held the opinion that, in contrast to Simon and Newell, machines did not require the ability to simulate human thought. Instead, he believed that machines should work toward discovering the essence of abstract reasoning and problem-solving, regardless of whether or not people used the same algorithms. His research group at Stanford, known as SAIL, concentrated on the use of formal logic to address a diverse range of issues, including as the representation of knowledge, the process of planning, and the acquisition of new information.

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However, LLMs can be used to extract and organize knowledge from unstructured data in a number of ways. While why a bot recommends a certain song over other on Spotify is a decision a user would hardly be bothered about, there are certain other situations where transparency in AI decisions becomes vital for users. For instance, if one’s job application gets rejected by an AI, or a loan application doesn’t go through. Neuro-symbolic AI can make the process transparent and interpretable by the artificial intelligence engineers, and explain why an AI program does what it does. When a human brain can learn with a few examples, AI engineers require to feed thousands into an AI algorithm. Neuro-symbolic AI systems can be trained with 1% of the data that other methods require.

A Symbolic AI system is said to be monotonic – once a piece of logic or rule is fed to the AI, it cannot be unlearned. Newly introduced rules are added to the existing knowledge, making Symbolic AI significantly lack adaptability and scalability. One power that the human mind has mastered over the years is adaptability. Humans can transfer knowledge from one domain to another, adjust our skills and methods with the times, and reason about and infer innovations.

This also extends the neighborhood semantics, usually considered on sets [49], to toposes. Syntax and semantics are defined, as well as a sound and complete proof system. Finally, in Section 6, we illustrate the proposed approach on typical examples in symbolic AI and knowledge representation, namely belief revision, merging, abduction, and spatial reasoning. Inevitably, this issue results in another critical limitation of Symbolic AI – common-sense knowledge. The human mind can generate automatic logical relations tied to the different symbolic representations that we have already learned. Humans learn logical rules through experience or intuition that become obvious or innate to us.

Around the year 1970, the availability of computers with huge memory prompted academics from all three schools of thought to begin applying their own bodies of knowledge to AI problems. The awareness that even relatively simple AI applications will need tremendous volumes of information was a driving force behind the knowledge revolution. Models developed by the QLattice have unparalleled accuracy, even with very little data, and are uniquely simple to understand. Abzu’s QLattice® is an explainable AI that rationally reasons and makes evidence-based decisions.

For a logical expression to be TRUE, its resultant value must be greater than or equal to 1. This chapter aims to understand the underlying mechanics of Symbolic AI, its key features, and its relevance to the next generation of AI systems. The summer school will include invited talks, panels, and tutorials in various areas of theory and the application of neuro-symbolic AI. We feel lucky to have two Turing award winners with us as distinguished invited speakers this year.

In Symbolic AI, we formalize everything we know about our problem as symbolic rules and feed it to the AI. Note that the more complex the domain, the larger and more complex the knowledge base becomes. Expert Systems, an application of Symbolic AI, emerged as a solution to the knowledge bottleneck. Developed in the 1970s and 1980s, Expert Systems aimed to capture the expertise of human specialists in specific domains. Instead of encoding explicit rules, Expert Systems utilized a knowledge base containing facts and heuristics to draw conclusions and make informed decisions.

While in Symbolic AI, we tend to rely heavily on Boolean logic computation, the world around us is far from Boolean. For example, a digital screen’s brightness is not just on or off, but it can also be any other value between 0% and 100% brightness. The concept of fuzziness adds a lot of extra complexities to designing Symbolic AI systems. Due to fuzziness, multiple concepts become deeply abstracted and complex for Boolean evaluation.

• A basic understanding of AI concepts and familiarity with Python programming are needed to make the most of this book.
• Whether we opt for fine-tuning, in-context feeding, or a blend of both, the true competitive advantage will not lie in the language model but in the data and its ontology (or shared vocabulary).
• Today, we are at a point where humans cannot understand the predictions and rationale behind AI.
• This approach is highly interpretable as the reasoning process can be traced back to the logical rules used.
• This paper relies on many terms and notations from the categorical theory of elementary toposes.

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