What is Neuro Symbolic Artificial Intelligence and Why Does it Make AI Explainable?

Symbolic AI works by using symbols to represent objects and concepts, and rules to represent relationships between them. These rules can be used to make inferences, solve problems, and understand complex concepts. Equally cutting-edge, France’s AnotherBrain is a fast-growing symbolic AI startup whose vision is to perfect “Industry 4.0” by using their own image recognition technology for quality control in factories.

Neuro Symbolic Artificial Intelligence, also known as neurosymbolic AI, is an advanced version of artificial intelligence (AI) that improves how a neural network arrives at a decision by adding classical rules-based (symbolic) AI to the process. This hybrid approach requires less training data and makes it possible for humans to track how AI programming made a decision. The origins of symbolic AI can be traced back to the early days of AI research, particularly in the 1950s and 1960s, when pioneers such as John McCarthy and Allen Newell laid the foundations for this approach. The concept gained prominence with the development of expert systems, knowledge-based reasoning, and early symbolic language processing techniques. Over the years, the evolution of symbolic AI has contributed to the advancement of cognitive science, natural language understanding, and knowledge engineering, establishing itself as an enduring pillar of AI methodology. Other ways of handling more open-ended domains included probabilistic reasoning systems and machine learning to learn new concepts and rules.

A different way to create AI was to build machines that have a mind of its own. Symbolic AI algorithms are able to solve problems that are too difficult for traditional AI algorithms. Also, some tasks can’t be translated to direct rules, including speech recognition and natural language processing. Being able to communicate in symbols is one of the main things that make us intelligent. Therefore, symbols have also played a crucial role in the creation of artificial intelligence.

Currently, Python, a multi-paradigm programming language, is the most popular programming language, partly due to its extensive package library that supports data science, natural language processing, and deep learning. Python includes a read-eval-print loop, functional elements such as higher-order functions, and object-oriented programming that includes metaclasses. And unlike symbolic AI, neural networks have no notion of symbols and hierarchical representation of knowledge. This limitation makes it very hard to apply neural networks to tasks that require logic and reasoning, such as science and high-school math. Symbolic AI involves the explicit embedding of human knowledge and behavior rules into computer programs.

The role of symbols in artificial intelligence

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. There are now several efforts to combine neural networks and symbolic AI. One such project is the Neuro-Symbolic Concept Learner (NSCL), a hybrid AI system developed by the MIT-IBM Watson AI Lab. NSCL uses both rule-based programs and neural networks to solve visual question-answering problems.

Symbolic AI algorithms are based on the manipulation of symbols and their relationships to each other. Using symbolic AI, everything is visible, understandable and explainable, leading to what is called a ‘transparent box’ as opposed to the ‘black box’ created by machine learning. As you can easily imagine, this is a very heavy and time-consuming job as there are many many ways of asking or formulating the same question. And if you take into account that a knowledge base usually holds on average 300 intents, you now see how repetitive maintaining a knowledge base can be when using machine learning. Natural language processing focuses on treating language as data to perform tasks such as identifying topics without necessarily understanding the intended meaning.

This way, a Neuro Symbolic AI system is not only able to identify an object, for example, an apple, but also to explain why it detects an apple, by offering a list of the apple’s unique characteristics and properties as an explanation. We see Neuro-symbolic AI as a pathway to achieve artificial general intelligence. By augmenting and combining the strengths of statistical AI, like machine learning, with the capabilities of human-like symbolic knowledge and reasoning, we’re aiming to create a revolution in AI, rather than an evolution. The difficulties encountered by symbolic AI have, however, been deep, possibly unresolvable ones. One difficult problem encountered by symbolic AI pioneers came to be known as the common sense knowledge problem.

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. Symbolic AI algorithms are used in a variety of AI applications, including knowledge representation, planning, and natural language processing. The advantage of neural networks is that they can deal with messy and unstructured data.

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Latent semantic analysis (LSA) and explicit semantic analysis also provided vector representations of documents. In the latter case, vector components are interpretable as concepts named by Wikipedia articles. Symbolic artificial intelligence is very convenient for settings where the rules are very clear cut,  and you can easily obtain input and transform it into symbols. In fact, rule-based systems still account for most computer programs today, including those used to create deep learning applications.

Natural language understanding, in contrast, constructs a meaning representation and uses that for further processing, such as answering questions. The logic clauses that describe programs are directly interpreted to run the programs specified. No explicit series of actions is required, as is the case with imperative programming languages. During the first AI summer, many people thought that machine intelligence could be achieved in just a few years.

They can simplify sets of spatiotemporal constraints, such as those for RCC or Temporal Algebra, along with solving other kinds of puzzle problems, such as Wordle, Sudoku, cryptarithmetic problems, and so on. Constraint logic programming can be used to solve scheduling problems, for example with constraint handling rules (CHR). Marvin Minsky first proposed frames as a way of interpreting common visual situations, such as an office, and Roger Schank extended this idea to scripts for common routines, such as dining out. Cyc has attempted to capture useful common-sense knowledge and has “micro-theories” to handle particular kinds of domain-specific reasoning. Alain Colmerauer and Philippe Roussel are credited as the inventors of Prolog.

To think that we can simply abandon symbol-manipulation is to suspend disbelief. Similar axioms would be required for other domain actions to specify what did not change. 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. Time periods and titles are drawn from Henry Kautz’s 2020 AAAI Robert S. Engelmore Memorial Lecture[17] and the longer Wikipedia article on the History of AI, with dates and titles differing slightly for increased clarity.

Symbolic AI systems are based on high-level, human-readable representations of problems and logic. So to summarize, one of the main differences between machine learning and traditional symbolic reasoning is how the learning happens. Chat PG In machine learning, the algorithm learns rules as it establishes correlations between inputs and outputs. In symbolic reasoning, the rules are created through human intervention and then hard-coded into a static program.

• 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.
• Its coexistence with newer AI paradigms offers valuable insights for building robust, interdisciplinary AI systems.
• Production rules connect symbols in a relationship similar to an If-Then statement.
• Similarly, LISP machines were built to run LISP, but as the second AI boom turned to bust these companies could not compete with new workstations that could now run LISP or Prolog natively at comparable speeds.

Notably because unlike GAI, which consumes considerable amounts of energy during its training stage, symbolic AI doesn’t need to be trained. One solution is to take pictures of your cat from different angles and create new rules for your application to compare each input against all those images. Even if you take a million pictures of your cat, you still won’t account for every possible case. A change in the lighting conditions or the background of the image will change the pixel value and cause the program to fail.

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Research problems include how agents reach consensus, distributed problem solving, multi-agent learning, multi-agent planning, and distributed constraint optimization. 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. Multiple different approaches to represent knowledge and then reason with those representations have been investigated. Below is a quick overview of approaches to knowledge representation and automated reasoning.

We hope that by now you’re convinced that symbolic AI is a must when it comes to NLP applied to chatbots. Machine learning can be applied to lots of disciplines, and one of those is Natural Language Processing, which is used in AI-powered conversational chatbots. The General Problem Solver (GPS) cast planning as problem-solving used means-ends analysis to create plans. Graphplan takes a least-commitment approach to planning, rather than sequentially choosing actions from an initial state, working forwards, or a goal state if working backwards.

Artificial systems mimicking human expertise such as Expert Systems are emerging in a variety of fields that constitute narrow but deep knowledge domains. The primary distinction lies in their respective approaches to knowledge symbolic artificial intelligence representation and reasoning. While symbolic AI emphasizes explicit, rule-based manipulation of symbols, connectionist AI, also known as neural network-based AI, focuses on distributed, pattern-based computation and learning.

Instead of manually laboring through the rules of detecting cat pixels, you can train a deep learning algorithm on many pictures of cats. When you provide it with a new image, it will return the probability that it contains a cat. In the realm of artificial intelligence, symbolic AI stands as a pivotal concept that has significantly influenced the understanding and development of intelligent systems. This guide aims to provide a comprehensive overview of symbolic AI, covering its definition, historical significance, working principles, real-world applications, pros and cons, related terms, and frequently asked questions. By the end of this exploration, readers will gain a profound understanding of the importance and impact of symbolic AI in the domain of artificial intelligence. In image recognition, for example, Neuro Symbolic AI can use deep learning to identify a stand-alone object and then add a layer of information about the object’s properties and distinct parts by applying symbolic reasoning.

In this view, deep learning best models the first kind of thinking while symbolic reasoning best models the second kind and both are needed. There have been several efforts to create complicated symbolic AI systems that encompass the multitudes of rules of certain domains. Called expert systems, these symbolic AI models use hardcoded knowledge and rules to tackle complicated tasks such as medical diagnosis. But they require a huge amount of effort by domain experts and software engineers and only work in very narrow use cases. As soon as you generalize the problem, there will be an explosion of new rules to add (remember the cat detection problem?), which will require more human labor.

By 2015, his hostility toward all things symbols had fully crystallized. He gave a talk at an AI workshop at Stanford comparing symbols to aether, one of science’s greatest mistakes. 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. Expert systems can operate in either a forward chaining – from evidence to conclusions – or backward chaining – from goals to needed data and prerequisites – manner.

Deep learning has several deep challenges and disadvantages in comparison to symbolic AI. Notably, deep learning algorithms are opaque, and figuring out how they work perplexes even their creators. And it’s very hard to communicate and troubleshoot their inner-workings. Deep learning and neural networks excel at exactly the tasks that symbolic AI struggles with. They have created a revolution in computer vision applications such as facial recognition and cancer detection. https://chat.openai.com/, also known as symbolic AI or classical AI, refers to a type of AI that represents knowledge as symbols and uses rules to manipulate these symbols.

By the mid-1960s neither useful natural language translation systems nor autonomous tanks had been created, and a dramatic backlash set in. Some companies have chosen to ‘boost’ symbolic AI by combining it with other kinds of artificial intelligence. Inbenta works in the initially-symbolic field of Natural Language Processing, but adds a layer of ML to increase the efficiency of this processing. The ML layer processes hundreds of thousands of lexical functions, featured in dictionaries, that allow the system to better ‘understand’ relationships between words.

Together, they built the General Problem Solver, which uses formal operators via state-space search using means-ends analysis (the principle which aims to reduce the distance between a project’s current state and its goal state). A certain set of structural rules are innate to humans, independent of sensory experience. With more linguistic stimuli received in the course of psychological development, children then adopt specific syntactic rules that conform to Universal grammar. Hobbes was influenced by Galileo, just as Galileo thought that geometry could represent motion, Furthermore, as per Descartes, geometry can be expressed as algebra, which is the study of mathematical symbols and the rules for manipulating these symbols.

Symbolic AI, also known as good old-fashioned AI (GOFAI), refers to the use of symbols and abstract reasoning in artificial intelligence. It involves the manipulation of symbols, often in the form of linguistic or logical expressions, to represent knowledge and facilitate problem-solving within intelligent systems. In the AI context, symbolic AI focuses on symbolic reasoning, knowledge representation, and algorithmic problem-solving based on rule-based logic and inference. (…) Machine learning algorithms build a mathematical model based on sample data, known as ‘training data’, in order to make predictions or decisions without being explicitly programmed to perform the task”. New deep learning approaches based on Transformer models have now eclipsed these earlier symbolic AI approaches and attained state-of-the-art performance in natural language processing.

In contrast to the US, in Europe the key AI programming language during that same period was Prolog. Prolog provided a built-in store of facts and clauses that could be queried by a read-eval-print loop. The store could act as a knowledge base and the clauses could act as rules or a restricted form of logic. Maybe in the future, we’ll invent AI technologies that can both reason and learn. But for the moment, symbolic AI is the leading method to deal with problems that require logical thinking and knowledge representation. In fact, rule-based AI systems are still very important in today’s applications.

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As opposed to pure neural network–based models, the hybrid AI can learn new tasks with less data and is explainable. And unlike symbolic-only models, NSCL doesn’t struggle to analyze the content of images. But the benefits of deep learning and neural networks are not without tradeoffs.

The Disease Ontology is an example of a medical ontology currently being used. At the height of the AI boom, companies such as Symbolics, LMI, and Texas Instruments were selling LISP machines specifically targeted to accelerate the development of AI applications and research. In addition, several artificial intelligence companies, such as Teknowledge and Inference Corporation, were selling expert system shells, training, and consulting to corporations. Despite these limitations, symbolic AI has been successful in a number of domains, such as expert systems, natural language processing, and computer vision. OOP languages allow you to define classes, specify their properties, and organize them in hierarchies. You can create instances of these classes (called objects) and manipulate their properties.

In addition, areas that rely on procedural or implicit knowledge such as sensory/motor processes, are much more difficult to handle within the Symbolic AI framework. In these fields, Symbolic AI has had limited success and by and large has left the field to neural network architectures (discussed in a later chapter) which are more suitable for such tasks. In sections to follow we will elaborate on important sub-areas of Symbolic AI as well as difficulties encountered by this approach. One of the most common applications of symbolic AI is natural language processing (NLP). NLP is used in a variety of applications, including machine translation, question answering, and information retrieval. In the realm of mathematics and theoretical reasoning, symbolic AI techniques have been applied to automate the process of proving mathematical theorems and logical propositions.

One of the primary challenges is the need for comprehensive knowledge engineering, which entails capturing and formalizing extensive domain-specific expertise. Additionally, ensuring the adaptability of symbolic AI in dynamic, uncertain environments poses a significant implementation hurdle. When deep learning reemerged in 2012, it was with a kind of take-no-prisoners attitude that has characterized most of the last decade.

Many of the concepts and tools you find in computer science are the results of these efforts. Symbolic AI programs are based on creating explicit structures and behavior rules. Symbolic AI was the dominant approach in AI research from the 1950s to the 1980s, and it underlies many traditional AI systems, such as expert systems and logic-based AI.

Class instances can also perform actions, also known as functions, methods, or procedures. Each method executes a series of rule-based instructions that might read and change the properties of the current and other objects. Since its foundation as an academic discipline in 1955, Artificial Intelligence (AI) research field has been divided into different camps, of which symbolic AI and machine learning.

Prolog is a form of logic programming, which was invented by Robert Kowalski. You can foun additiona information about ai customer service and artificial intelligence and NLP. Its history was also influenced by Carl Hewitt’s PLANNER, an assertional database with pattern-directed invocation of methods. For more detail see the section on the origins of Prolog in the PLANNER article.

Neuro Symbolic AI is expected to help reduce machine bias by making the decision-making process a learning model goes through more transparent and explainable. Combining learning with rules-based logic is also expected to help data scientists and machine learning engineers train algorithms with less data by using neural networks to create the knowledge base that an expert system and symbolic AI requires. While deep learning and neural networks have garnered substantial attention, symbolic AI maintains relevance, particularly in domains that require transparent reasoning, rule-based decision-making, and structured knowledge representation. Its coexistence with newer AI paradigms offers valuable insights for building robust, interdisciplinary AI systems.

The practice showed a lot of promise in the early decades of AI research. But in recent years, as neural networks, also known as connectionist AI, gained traction, symbolic AI has fallen by the wayside. Symbolic AI has greatly influenced natural language processing by offering formal methods for representing linguistic structures, grammatical rules, and semantic relationships. These symbolic representations have paved the way for the development of language understanding and generation systems. The enduring relevance and impact of symbolic AI in the realm of artificial intelligence are evident in its foundational role in knowledge representation, reasoning, and intelligent system design. As AI continues to evolve and diversify, the principles and insights offered by symbolic AI provide essential perspectives for understanding human cognition and developing robust, explainable AI solutions.

Many leading scientists believe that symbolic reasoning will continue to remain a very important component of artificial intelligence. 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. YAGO incorporates WordNet as part of its ontology, to align facts extracted from Wikipedia with WordNet synsets.

You create a rule-based program that takes new images as inputs, compares the pixels to the original cat image, and responds by saying whether your cat is in those images. Symbolic artificial intelligence showed early progress at the dawn of AI and computing. You can easily visualize the logic of rule-based programs, communicate them, and troubleshoot them. Using OOP, you can create extensive and complex symbolic AI programs that perform various tasks.

The main limitation of symbolic AI is its inability to deal with complex real-world problems. Symbolic AI is limited by the number of symbols that it can manipulate and the number of relationships between those symbols. For example, a symbolic AI system might be able to solve a simple mathematical problem, but it would be unable to solve a complex problem such as the stock market.

Satplan is an approach to planning where a planning problem is reduced to a Boolean satisfiability problem. Qualitative simulation, such as Benjamin Kuipers’s QSIM,[88] approximates human reasoning about naive physics, such as what happens when we heat a liquid in a pot on the stove. We expect it to heat and possibly boil over, even though we may not know its temperature, its boiling point, or other details, such as atmospheric pressure. Cognitive architectures such as ACT-R may have additional capabilities, such as the ability to compile frequently used knowledge into higher-level chunks. Japan championed Prolog for its Fifth Generation Project, intending to build special hardware for high performance.

However, Transformer models are opaque and do not yet produce human-interpretable semantic representations for sentences and documents. Instead, they produce task-specific vectors where the meaning of the vector components is opaque. The work in AI started by projects like the General Problem Solver and other rule-based reasoning systems like Logic Theorist became the foundation for almost 40 years of research. Symbolic AI (or Classical AI) is the branch of artificial intelligence research that concerns itself with attempting to explicitly represent human knowledge in a declarative form (i.e. facts and rules). If such an approach is to be successful in producing human-like intelligence then it is necessary to translate often implicit or procedural knowledge possessed by humans into an explicit form using symbols and rules for their manipulation.

Symbolic AI has been instrumental in the creation of expert systems designed to emulate human expertise and decision-making in specialized domains. By encoding domain-specific knowledge as symbolic rules and logical inferences, expert systems have been deployed in fields such as medicine, finance, and engineering to provide intelligent recommendations and problem-solving capabilities. Each approach—symbolic, connectionist, and behavior-based—has advantages, but has been criticized by the other approaches. Symbolic AI has been criticized as disembodied, liable to the qualification problem, and poor in handling the perceptual problems where deep learning excels.

Symbolic AI has been used in a wide range of applications, including expert systems, natural language processing, and game playing. It can be difficult to represent complex, ambiguous, or uncertain knowledge with symbolic AI. Furthermore, symbolic AI systems are typically hand-coded and do not learn from data, which can make them brittle and inflexible. In natural language processing, symbolic AI has been employed to develop systems capable of understanding, parsing, and generating human language. Through symbolic representations of grammar, syntax, and semantic rules, AI models can interpret and produce meaningful language constructs, laying the groundwork for language translation, sentiment analysis, and chatbot interfaces. For other AI programming languages see this list of programming languages for artificial intelligence.

Symbolic AI has its roots in logic and mathematics, and many of the early AI researchers were logicians or mathematicians. Symbolic AI algorithms are often based on formal systems such as first-order logic or propositional logic. But symbolic AI starts to break when you must deal with the messiness of the world. For instance, consider computer vision, the science of enabling computers to make sense of the content of images and video. Say you have a picture of your cat and want to create a program that can detect images that contain your cat.

René Descartes, a mathematician, and philosopher, regarded thoughts themselves as symbolic representations and Perception as an internal process. The grandfather of AI, Thomas Hobbes said — Thinking is manipulation of symbols and Reasoning is computation. As such, Golem.ai applies linguistics and neurolinguistics to a given problem, rather than statistics. Their algorithm includes almost every known language, enabling the company to analyze large amounts of text.

Our chemist was Carl Djerassi, inventor of the chemical behind the birth control pill, and also one of the world’s most respected mass spectrometrists. We began to add to their knowledge, inventing knowledge of engineering as we went along. These experiments amounted to titrating DENDRAL more and more knowledge. Like Inbenta’s, “our technology is frugal in energy and data, it learns autonomously, and can explain its decisions”, affirms AnotherBrain on its website. And given the startup’s founder, Bruno Maisonnier, previously founded Aldebaran Robotics (creators of the NAO and Pepper robots), AnotherBrain is unlikely to be a flash in the pan.

In turn, connectionist AI has been criticized as poorly suited for deliberative step-by-step problem solving, incorporating knowledge, and handling planning. Finally, Nouvelle AI excels in reactive and real-world robotics domains but has been criticized for difficulties in incorporating learning and knowledge. In ML, knowledge is often represented in a high-dimensional space, which requires a lot of computing power to process and manipulate. In contrast, symbolic AI uses more efficient algorithms and techniques, such as rule-based systems and logic programming, which require less computing power. Samuel’s Checker Program[1952] — Arthur Samuel’s goal was to explore to make a computer learn.

Similar to the problems in handling dynamic domains, common-sense reasoning is also difficult to capture in formal reasoning. Examples of common-sense reasoning include implicit reasoning about how people think or general knowledge of day-to-day events, objects, and living creatures. This kind of knowledge is taken for granted and not viewed as noteworthy. Production rules connect symbols in a relationship similar to an If-Then statement.