Neuro-Symbolic AI: Bridging the Gap Between Traditional and Modern AI Approaches
The grandfather of AI, Thomas Hobbes said — Thinking is manipulation of symbols and Reasoning is computation. Similarly, Allen’s temporal interval algebra is a simplification of reasoning about time and Region Connection Calculus is a simplification of reasoning about spatial relationships. A more flexible kind of problem-solving occurs when reasoning about what to do next occurs, rather than simply choosing one of the available actions. This kind of meta-level reasoning is used in Soar and in the BB1 blackboard architecture. Japan championed Prolog for its Fifth Generation Project, intending to build special hardware for high performance.
This approach involves the fusion of deep learning neural network topologies with symbolic reasoning techniques, thereby elevating the sophistication of AI beyond its traditional counterparts. For example, neural networks have proven effective in shape or color. Nevertheless, Neuro-Symbolic AI takes it a step further, leveraging symbolic reasoning to unveil more intriguing facets of the item, such as its area, volume, and other pertinent attributes. And unlike symbolic AI, neural networks have no notion of symbols and hierarchical representation of knowledge.
Fuzzy and Annotated Logic for Neuro Symbolic Artificial Intelligence
When combined with the power of Symbolic Artificial Intelligence, these large language models hold a lot of potential in solving complex problems. Such a framework called SymbolicAI has been developed by Marius-Constantin Dinu, a current Ph.D. student and an ML researcher who used the strengths of LLMs to build software applications. A robot using a complex knowledge base like Cyc or First Order Logic would be able to reason about many different aspects of the world.
Neural networks are almost as old as symbolic AI, but they were largely dismissed because they were inefficient and required compute resources that weren’t available at the time. In the past decade, thanks to the large availability of data and processing power, deep learning has gained popularity and has pushed past symbolic AI systems. Knowledge-based systems have an explicit knowledge base, typically of rules, to enhance reusability across domains by separating procedural code and domain knowledge. A separate inference engine processes rules and adds, deletes, or modifies a knowledge store. Semantic networks, conceptual graphs, frames, and logic are all approaches to modeling knowledge such as domain knowledge, problem-solving knowledge, and the semantic meaning of language. DOLCE is an example of an upper ontology that can be used for any domain while WordNet is a lexical resource that can also be viewed as an ontology.
What are the benefits of symbolic AI?
The representational power of First Order Logic is very great and allows you to translate virtually any idea you can express in a sentence as a proposition. There are some problems with the ability to represent time-based changes, but there are often tricks one can perform to alleviate them. To test the Prolog legal reasoning model on R v Bentham, I manually translated the relevant sections of the Firearms Act 1968 into Prolog. In Germany in 2017, Bernhard Waltl and other researchers at the Technical University of Munich trained a machine learning classifier on 5990 tax law appeals to use 11 features to predict the outcome of a new tax appeal. To apply legal reasoning, a judge must identify the facts of a case, the question, the relevant legislation and any precedents (in common law jurisdictions).
Symbolic AI’s strength lies in its knowledge representation and reasoning through logic, making it more akin to Kahneman’s “System 2” mode of thinking, which is slow, takes work and demands attention. That is because it is based on relatively simple underlying logic that relies on things being true, and on rules providing a means of inferring new things from things already known to be true. Samuel’s Checker Program[1952] — Arthur Samuel’s goal was to explore to make a computer learn. The program improved as it played more and more games and ultimately defeated its own creator. In 1959, it defeated the best player, This created a fear of AI dominating AI. This lead towards the connectionist paradigm of AI, also called non-symbolic AI which gave rise to learning and neural network-based approaches to solve AI.
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What is symbolic reasoning?
In symbolic reasoning, the rules are created through human intervention and then hard-coded into a static program. If machine learning can appear as a revolutionary approach at first, its lack of transparency and a large amount of data that is required in order for the system to learn are its two main flaws.