Neuro-symbolic approaches in artificial intelligence National Science Review

The researchers broke the problem into smaller chunks familiar from symbolic AI. In essence, they had to first look at an image and characterize the 3-D shapes and their properties, and generate a knowledge base. Then they had to turn an English-language question into a symbolic program that could operate on the knowledge base and produce an answer. We see Neuro-symbolic AI as a pathway to achieve artificial general intelligence.

In the field of artificial intelligence, the term symbolic artificial intelligence refers to the collection of all methods in artificial intelligence research that are based on high-level symbolic (human-readable) representations of problems, logic, and search. Humans interact with the environment using a combination of perception transforming sensory inputs from their environment into symbols and cognition, mapping symbols to knowledge about the environment for supporting abstraction, reasoning by analogy, and long-term planning. Human perception-inspired machine perception, in the context of artificial intelligence (AI), refers to large-scale pattern recognition from raw data using neural networks trained using self-supervised learning objectives such as next-word prediction or object recognition. On the other hand, machine cognition encompasses more complex computations, such as using knowledge of the environment to guide reasoning, analogy, and long-term planning. This seems to require the retention of symbolic mappings from perception outputs to knowledge about their environment. For example, humans can follow and explain the guidelines and safety constraints driving their decision-making in safety-critical applications such as health care, criminal justice, and autonomous driving.

• However, contemporary DRL systems inherit a number of shortcomings from the current generation of deep learning techniques.
• What the ducklings do so effortlessly turns out to be very hard for artificial intelligence.
• The symbolic representations are manipulated using rules to make inferences, solve problems, and understand complex concepts.
• However, they possess the added ability to fully govern the learning of all pipeline components through end-to-end differential compositions of functions that correspond to each component.

Crucially, these hybrids need far less training data then standard deep nets and use logic that’s easier to understand, making it possible for humans to track how the AI makes its decisions. According to Wikipedia, machine learning is an application of artificial intelligence where “algorithms and statistical models are used by computer systems to perform a specific task without using explicit instructions, relying on patterns and inference instead. (…) 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”.

In its simplest form, metadata can consist just of keywords, but they can also take the form of sizeable logical background theories. Neuro-symbolic lines of work include the use of knowledge graphs to improve zero-shot learning. Background knowledge can also be used to improve out-of-sample generalizability, or to ensure safety guarantees in neural control systems. Other work utilizes structured background knowledge for improving coherence and consistency in neural sequence models. Symbolic AI, a branch of artificial intelligence, specializes in symbol manipulation to perform tasks such as natural language processing (NLP), knowledge representation, and planning. These algorithms enable machines to parse and understand human language, manage complex data in knowledge bases, and devise strategies to achieve specific goals.

Automated planning

In a nutshell, symbolic AI involves the explicit embedding of human knowledge and behavior rules into computer programs. 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. In contrast to the US, in Europe the key AI programming language during that same period was Prolog.

But it is undesirable to have inference errors corrupting results in socially impactful applications of AI, such as automated decision-making, and especially in fairness analysis. It is one form of assumption, and a strong one, while deep neural architectures contain other assumptions, usually about how they should learn, rather than what conclusion they should reach. The ideal, obviously, is to choose assumptions that allow a system to learn flexibly and produce accurate decisions about their inputs.

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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. 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. Error from approximate probabilistic inference is tolerable in many AI applications.

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. Satplan is an approach to planning where a planning problem is reduced to a Boolean satisfiability problem. 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.

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. Programs were themselves data structures that other programs could operate on, allowing the easy definition of higher-level languages. Despite its early successes, Symbolic AI has limitations, particularly when dealing with ambiguous, uncertain knowledge, or when it requires learning from data. It is often criticized for not being able to handle the messiness of the real world effectively, as it relies on pre-defined knowledge and hand-coded rules. 2) The two problems may overlap, and solving one could lead to solving the other, since a concept that helps explain a model will also help it recognize certain patterns in data using fewer examples.

Symbolic AI is also known as Good Old-Fashioned Artificial Intelligence (GOFAI), as it was influenced by the work of Alan Turing and others in the 1950s and 60s. 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.

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Some questions are simple (“Are there fewer cubes than red things?”), but others are much more complicated (“There is a large brown block in front of the tiny rubber cylinder that is behind the cyan block; are there any big cyan metallic cubes that are to the left of it?”). But the benefits of deep learning and neural networks are not without tradeoffs. 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.

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. Coupling may be through different methods, including the calling of deep learning systems within a symbolic algorithm, or the acquisition of symbolic rules during training. The Symbolic AI paradigm led to seminal ideas in search, symbolic programming languages, agents, multi-agent systems, the semantic web, and the strengths and limitations of formal knowledge and reasoning systems. In the field of artificial intelligence, the term « symbolic artificial intelligence » refers to the collection of all methods in artificial intelligence research that are based on high-level symbolic (human-readable) representations of issues, logic, and search.

You can foun additiona information about ai customer service and artificial intelligence and NLP. (Speech is sequential information, for example, and speech recognition programs like Apple’s Siri use a recurrent network.) In this case, the network takes a question and transforms it into a query in the form of a symbolic program. The output of the recurrent network is also used to decide on which convolutional networks are tasked to look over the image and in what order. This entire process is akin to generating a knowledge base on demand, and having an inference engine run the query on the knowledge base to reason and answer the question.

The similar reasoning was presented in the Lighthill study, which was the impetus for the beginning of the AI Winter in the middle of the 1970s. A physical symbol system has the essential and enough means for widespread intelligent action. A similar problem, called the Qualification Problem, occurs in trying to enumerate the preconditions for an action to succeed. An infinite number of pathological conditions can be imagined, e.g., a banana in a tailpipe could prevent a car from operating correctly. 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. Knowable Magazine is from Annual Reviews,

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We began to add to their knowledge, inventing knowledge of engineering as we went along. Machine learning can be applied to lots of disciplines, and one of those is NLP, which is used in AI-powered conversational chatbots. One of the key advantages of this approach is its ability to provide clear and detailed explanations of how a particular conclusion is reached.

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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. Symbolic AI, a subfield of AI focused on symbol manipulation, has its limitations. Its primary challenge is handling complex real-world scenarios due to the finite number of symbols and their interrelations it can process. For instance, while it can solve straightforward mathematical problems, it struggles with more intricate issues like predicting stock market trends. In this world, almost everything can be well understood by humans using symbols.

This approach is based on the creation of symbolic structures that encode domain-specific knowledge. These structures may include rules in “if-then” format, ontologies that describe the relationships between concepts and hierarchies, and other symbolic elements. In 1955 and 1956, Allen Newell, Herbert Simon, and Cliff Shaw developed the Logic theorist, which is considered to be the first ever symbolic artificial intelligence program. Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of symbolic artificial intelligence.

To bridge the learning of two modules, we use a neuro-symbolic reasoning module that executes these programs on the latent scene representation. 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.

This will only work as you provide an exact copy of the original image to your program. For instance, if you take a picture of your cat from a somewhat different angle, the program will fail. We use symbols all the time to define things (cat, car, airplane, etc.) and people (teacher, police, salesperson). Symbols can represent abstract concepts (bank transaction) or things that don’t physically exist (web page, blog post, etc.).

The combination of AllegroGraph’s capabilities with Neuro-Symbolic AI has the potential to transform numerous industries. In healthcare, it can integrate and interpret vast datasets, from patient records to medical research, to support diagnosis and treatment decisions. In finance, it can analyze transactions within the context of evolving regulations to detect fraud and ensure compliance. 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.

Artificial Intelligence (AI) has undergone a remarkable evolution, but its roots can be traced back to Symbolic AI and Expert Systems, which laid the groundwork for the field. In this article, we delve into the concepts of Symbolic AI and Expert Systems, exploring their significance and contributions to early AI research. Understanding these foundational ideas is crucial in comprehending the advancements that have led to the powerful AI technologies we have today. In recent years, several research groups have focused on developing new approaches and techniques for Neuro-Symbolic AI.

This knowledge revolution resulted in the creation and implementation of expert systems, the first really effective kind of artificial intelligence software. The knowledge base, which holds facts and rules that show artificial intelligence, is an essential element of the system architecture for all expert systems. The connection between two symbols in a production rule is very much like that of an If-Then expression. The rules are processed by the expert system, which then uses symbols that are understandable by humans to decide what deductions to make and what extra information it need, also known as what questions to ask. Because symbolic AI operates according to predetermined rules and has access to ever-increasing processing power, it is able to handle more difficult tasks.

What is symbolic AI?

So not only has symbolic AI the most mature and frugal, it’s also the most transparent, and therefore accountable. As pressure mounts on GAI companies to explain where their apps’ answers come from, symbolic AI will never have that problem. 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. Notably because unlike GAI, which consumes considerable amounts of energy during its training stage, symbolic AI doesn’t need to be trained. Generative AI (GAI) has been the talk of the town since ChatGPT exploded late 2022.

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. Natural language processing focuses on treating language as data to perform tasks such as identifying topics without necessarily understanding the intended meaning. Natural language understanding, in contrast, constructs a meaning representation and uses that for further processing, such as answering questions. 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.

Such explanations are useful for developers but not easily understood by end-users. Additionally, neural networks can fail due to uncontrollable training-time factors like data artifacts, adversarial attacks, distribution shifts, and system failures. To ensure rigorous safety standards, it is necessary to incorporate appropriate background knowledge to set guardrails during training rather than as a post-hoc measure.

In planning, symbolic AI is crucial for robotics and automated systems, generating sequences of actions to meet objectives. The two biggest flaws of deep learning are its lack of model interpretability (i.e. why did my model make that prediction?) and the large amount of data that deep neural networks require in order to learn. So, while naysayers may decry the addition of symbolic modules to deep learning as unrepresentative of how our brains work, proponents of neurosymbolic AI see its modularity as a strength when it comes to solving practical problems. “When you have neurosymbolic systems, you have these symbolic choke points,” says Cox.

Symbolic AI programs are based on creating explicit structures and behavior rules. 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. Symbolic AI algorithms are able to solve problems that are too difficult for traditional AI algorithms. Symbolic AI has its roots in logic and mathematics, and many of the early AI researchers were logicians or mathematicians.

A few years ago, scientists learned something remarkable about mallard ducklings. If one of the first things the ducklings see after birth is two objects that are similar, the ducklings will later follow new pairs of objects that are similar, too. Hatchlings shown two red spheres at birth will later show a preference for two spheres of the same color, even if they are blue, over two spheres that are each a different color. Somehow, the ducklings pick up and imprint on the idea of similarity, in this case the color of the objects. Unlike ML, which requires energy-intensive GPUs, CPUs are enough for symbolic AI’s needs.

But adding a small amount of white noise to the image (indiscernible to humans) causes the deep net to confidently misidentify it as a gibbon. Thus contrary to pre-existing cartesian philosophy he maintained that we are born without innate ideas and knowledge is instead determined only by experience derived by a sensed perception. Children can be symbol manipulation and do addition/subtraction, but they don’t really understand what they are doing. 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. A different way to create AI was to build machines that have a mind of its own.

These early concepts laid the foundation for logical reasoning and problem-solving, and while they faced limitations, they provided valuable insights that contributed to the evolution of modern AI technologies. Today, AI has moved beyond Symbolic AI, incorporating machine learning and deep learning techniques that can handle vast amounts of data and solve complex problems with unprecedented accuracy. Nevertheless, understanding the origins of Symbolic AI and Expert Systems remains essential to appreciate the strides made in the world of AI and to inspire future innovations that will further transform our lives. First of all, every deep neural net trained by supervised learning combines deep learning and symbolic manipulation, at least in a rudimentary sense.

The first one comes from the field of cognitive science, a highly interdisciplinary field that studies the human mind. In that context, we can understand artificial neural networks as an abstraction of the physical workings of the brain, while we can understand formal logic as an abstraction of what we perceive, through introspection, when contemplating explicit cognitive reasoning. In order to advance the understanding of the human mind, it therefore appears to be a natural question to ask how these two abstractions can be related or even unified, or how symbol symbolic artificial intelligence manipulation can arise from a neural substrate [1]. We investigate an unconventional direction of research that aims at converting neural networks, a class of distributed, connectionist, sub-symbolic models into a symbolic level with the ultimate goal of achieving AI interpretability and safety. To that end, we propose Object-Oriented Deep Learning, a novel computational paradigm of deep learning that adopts interpretable “objects/symbols” as a basic representational atom instead of N-dimensional tensors (as in traditional “feature-oriented” deep learning).

In the CLEVR challenge, artificial intelligences were faced with a world containing geometric objects of various sizes, shapes, colors and materials. The AIs were then given English-language questions (examples shown) about the objects in their world. 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. Don’t get us wrong, machine learning is an amazing tool that enables us to unlock great potential and AI disciplines such as image recognition or voice recognition, but when it comes to NLP, we’re firmly convinced that machine learning is not the best technology to be used.

The second reason is tied to the field of AI and is based on the observation that neural and symbolic approaches to AI complement each other with respect to their strengths and weaknesses. For example, deep learning systems are trainable from raw data and are robust against outliers or errors in the base data, while symbolic systems are brittle with respect to outliers and data errors, and are far less trainable. It is therefore natural to ask how neural and symbolic approaches can be combined or even unified in order to overcome the weaknesses of either approach.

The key AI programming language in the US during the last symbolic AI boom period was LISP. LISP is the second oldest programming language after FORTRAN and was created in 1958 by John McCarthy. LISP provided the first read-eval-print loop to support rapid program development. Program tracing, stepping, and breakpoints were also provided, along with the ability to change values or functions and continue from breakpoints or errors. It had the first self-hosting compiler, meaning that the compiler itself was originally written in LISP and then ran interpretively to compile the compiler code.

Knowledge representation is used in a variety of applications, including expert systems and decision support systems. In NLP, symbolic AI contributes to machine translation, question answering, and information retrieval by interpreting text. For knowledge representation, it underpins expert systems and decision support systems, organizing and accessing information efficiently.

Other ways of handling more open-ended domains included probabilistic reasoning systems and machine learning to learn new concepts and rules. McCarthy’s Advice Taker can be viewed as an inspiration here, as it could incorporate new knowledge provided by a human in the form of assertions or rules. For example, experimental symbolic machine learning systems explored the ability to take high-level natural language advice and to interpret it into domain-specific actionable rules.