Bridging the Gap Between Symbolic and Subsymbolic AI

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. Traditionally, in neuro-symbolic AI research, emphasis is on either incorporating symbolic abilities in a neural approach, or coupling neural and symbolic components such that they seamlessly interact [2]. 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.

Nonetheless, a Symbolic AI program still works purely as described in our little example – and it is precisely why Symbolic AI dominated and revolutionized the computer science field during its time. Symbolic AI systems can execute human-defined logic at an extremely fast pace. For example, a computer system with an average 1 GHz CPU can process around 200 million logical operations per second (assuming a CPU with a RISC-V instruction set). This processing power enabled Symbolic AI systems to take over manually exhaustive and mundane tasks quickly. In the Symbolic AI paradigm, we manually feed knowledge represented as symbols for the machine to learn.

What the ducklings do so effortlessly turns out to be very hard for artificial intelligence. This is especially true of a branch of AI known as deep learning or deep neural networks, the technology powering the AI that defeated the world’s Go champion Lee Sedol in 2016. Such deep nets can struggle to figure out simple abstract relations between objects and reason about them unless they study tens or even hundreds of thousands of examples.

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. The following chapters will focus on and discuss the sub-symbolic paradigm in greater detail. In the next chapter, we will start by shedding some light on the NN revolution and examine the current situation regarding AI technologies. Being the first major revolution in AI, Symbolic AI has been applied to many applications – some with more success than others. Despite the proven limitations we discussed, Symbolic AI systems have laid the groundwork for current AI technologies.

In finance, it can analyze transactions within the context of evolving regulations to detect fraud and ensure compliance. However, this also required much manual effort from experts tasked with deciphering the chain of thought processes that connect various symptoms to diseases or purchasing patterns to fraud. This downside is not a big issue with deciphering the meaning of children’s stories or linking common knowledge, but it becomes more expensive with specialized knowledge. This will only work as you provide an exact copy of the original image to your program.

Symbolic AI is extensively used in automated reasoning tasks, such as

theorem proving, logic programming, and constraint satisfaction. Symbols

are used to represent logical statements, and inference rules are

applied to derive new conclusions or prove mathematical theorems. When a patient’s symptoms are input into the system, it applies these

rules to infer the most likely diagnosis based on the symbolic

representations and logical inference. Symbols are created to represent the relevant entities, concepts, and

relationships in a given domain. For example, in a natural language

processing system, symbols may be created for words, phrases, and

grammatical structures.

To illustrate these concepts, we present examples and diagrams that

visualize the workings of Symbolic AI systems. We also contrast Symbolic

AI with other AI paradigms, highlighting their fundamental differences

and the unique strengths and limitations of Symbolic AI. One of the biggest is to be able to automatically encode better rules for symbolic AI.

Knowledge representation and reasoning

The unlikely marriage of two major artificial intelligence approaches has given rise to a new hybrid called neurosymbolic AI. It’s taking baby steps toward reasoning like humans and might one day take the wheel in self-driving cars. For example, AI models might benefit from combining more structural information across various levels of abstraction, such as transforming a raw invoice document into information about purchasers, products and payment terms. An internet of things stream could similarly benefit from translating raw time-series data into relevant events, performance analysis data, or wear and tear.

An inference engine, also known as a reasoning engine, is a critical

component of Symbolic AI systems. It is responsible for deriving new

knowledge or conclusions based on the existing knowledge represented in

the system. The inference engine applies logical rules and deduction

mechanisms to the knowledge base to infer new facts, answer queries, and

solve problems. Throughout the 1960s and 1970s, Symbolic AI continued to make

significant strides. Researchers developed various knowledge

representation formalisms, such as first-order logic, semantic networks,

and frames, to capture and reason about domain knowledge.

That is, we carry out an algebraic process of symbols – using semantics for reasoning about individual symbols and symbolic relationships. Semantics allow us to define how the different symbols relate to each other. Symbolic AI, GOFAI, or Rule-Based AI (RBAI), is a sub-field of AI concerned with learning the internal symbolic representations of the world around it. The main objective of Symbolic AI is the explicit embedding of human knowledge, behavior, and “thinking rules” into a computer or machine.

The power of neural networks is that they help automate the process of generating models of the world. This has led to several significant milestones in artificial intelligence, giving rise to deep learning models that, for example, could beat humans in progressively complex games, including Go and StarCraft. But it can be challenging to reuse these deep learning models or extend them to new domains. On the other hand, neural networks, the cornerstone of deep learning, have demonstrated remarkable success in tasks such as image recognition, natural language processing, and game playing. These models can understand and duplicate complicated patterns and charts from large amounts of data.

Expert systems

For example, debuggers can inspect the knowledge base or processed question and see what the AI is doing. AllegroGraph is a horizontally distributed Knowledge Graph Platform that supports multi-modal Graph (RDF), Vector, and Document (JSON, JSON-LD) storage. It is equipped with capabilities such as SPARQL, Geospatial, Temporal, Social Networking, Text Analytics, and Large Language Model (LLM) functionalities. These features enable scalable Knowledge Graphs, which are essential for building Neuro-Symbolic AI applications that require complex data analysis and integration. For example, AI developers created many rule systems to characterize the rules people commonly use to make sense of the world.

Critics, such as Hubert Dreyfus, argued that

Symbolic AI was fundamentally limited in its ability to capture the full

richness of human intelligence. Through the fusion of learning and reasoning capabilities, these systems have the capacity to comprehend and engage with the world in a manner closely resembling human cognition. This attribute makes it effective at tackling problems where logical rules are exceptionally complex, numerous, and ultimately impractical to code, like deciding how a single pixel in an image should be labeled. 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. ” This development represents an initial stride toward empowering authors by placing them at the center of the creative process while maintaining complete control. Using LLMs to extract and organize knowledge from unstructured data, we can enrich the data in a knowledge graph and bring additional insights to our SEO’s automated workflows.

At its core, the symbolic program must define what makes a movie watchable. Then, we must express this knowledge as logical propositions to build our knowledge base. Following this, we can create the logical propositions for the individual movies and use our knowledge base to evaluate the said logical propositions as either TRUE or FALSE. So far, we have discussed what we understand by symbols and how we can describe their interactions using relations. The final puzzle is to develop a way to feed this information to a machine to reason and perform logical computation.

1) Hinton, Yann LeCun and Andrew Ng have all suggested that work on unsupervised learning (learning from unlabeled data) will lead to our next breakthroughs. Like our product, our medium articles are written by novel generative AI models, with human feedback on the edge cases. 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. This will give a “Semantic Coincidence Score” which allows the query to be matched with a pre-established frequently-asked question and answer, and thereby provide the chatbot user with the answer she was looking for.

Unlike its counterpart, sub-symbolic AI (such as neural networks), which focuses on pattern recognition and statistical inference, symbolic AI deals with the representation and manipulation of explicit knowledge using symbols and rules. In this blog post, we will delve into specific examples of symbolic AI in practice, shedding light on its technical intricacies. For much of the AI era, symbolic approaches held the upper hand in adding value through apps including expert systems, fraud detection and argument mining. But innovations in deep learning and the infrastructure for training large language models (LLMs) have shifted the focus toward neural networks. Parsing, tokenizing, spelling correction, part-of-speech tagging, noun and verb phrase chunking are all aspects of natural language processing long handled by symbolic AI, but since improved by deep learning approaches.

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. Our model builds an object-based scene representation and translates sentences into executable, symbolic programs. To bridge the learning of two modules, we use a neuro-symbolic reasoning module that executes these programs on the latent scene representation.

We previously discussed how computer systems essentially operate using symbols. Another way the two AI paradigms can be combined is by using neural networks to help prioritize how symbolic programs organize and search through multiple facts related to a question. For example, if an AI is trying to decide if a given statement is true, a symbolic algorithm needs to consider whether thousands of combinations of facts are relevant. This is important because all AI systems in the real world deal with messy data.

Implementations of symbolic reasoning are called rules engines or expert systems or knowledge graphs. Google made a big one, too, which is what provides the information in the top box under your query when you search for something easy like the capital of Germany. You can foun additiona information about ai customer service and artificial intelligence and NLP. These systems are essentially piles of nested if-then statements drawing conclusions about entities (human-readable concepts) and their relations (expressed in well understood semantics like X is-a man or X lives-in Acapulco). In contrast to symbolic AI, subsymbolic AI focuses on the use of numerical representations and machine learning algorithms to extract patterns from data.

This limitation makes it very hard to apply neural networks to tasks that require logic and reasoning, such as science and high-school math. A Neuro-Symbolic AI system in this context would use a neural network to learn to recognize objects from data (images from the car’s cameras) and a symbolic system to reason about these objects https://chat.openai.com/ and make decisions according to traffic rules. This combination allows the self-driving car to interact with the world in a more human-like way, understanding the context and making reasoned decisions. Symbolic AI is one of the earliest forms based on modeling the world around us through explicit symbolic representations.

This entailed incorporating explicit human knowledge and behavioral guidelines into computer programs, forming the basis of rule-based symbolic AI. However, this approach heightened system costs and diminished accuracy with the addition of more rules. These systems combine symbolic logic (for learning rules) with neural networks (for learning from data). This combination enables AI to comprehend intricate patterns while also interpreting logical rules effectively.

Note that implicit knowledge can eventually be formalized and structured to become explicit knowledge. For example, if learning to ride a bike is implicit knowledge, writing a step-by-step guide on how to ride a bike becomes explicit knowledge. Explicit knowledge is any clear, well-defined, and easy-to-understand information. In a dictionary, words and their respective definitions are written down (explicitly) and can be easily identified and reproduced. However, in the 1980s and 1990s, Symbolic AI faced increasing challenges

and criticisms. The brittleness of symbolic systems, the difficulty of

scaling to real-world complexity, and the knowledge acquisition

bottleneck became apparent.

Symbols are abstract representations of real-world entities, concepts,

or relationships. These symbols are organized into structured

representations, such as hierarchies, semantic networks, or frames, to

capture the relationships and properties of the entities they represent. Symbolic AI is fundamentally grounded in formal logic, which provides a

rigorous framework for representing and manipulating knowledge.

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. 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.

All operations are executed in an input-driven fashion, thus sparsity and dynamic computation per sample are naturally supported, complementing recent popular ideas of dynamic networks and may enable new types of hardware accelerations. We experimentally show on CIFAR-10 that it can perform flexible visual processing, rivaling the performance of ConvNet, but without using any convolution. Furthermore, it can generalize to novel rotations of images that it was not trained for. Symbolic AI offers powerful tools for representing and manipulating explicit knowledge.

While symbolic models aim for complicated connections, they are good at capturing compositional and causal structures. Now, new training techniques in generative AI (GenAI) models have automated much of the human effort required to build better systems for symbolic AI. But these more statistical approaches tend to hallucinate, struggle with math and are opaque. 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. Companies now realize how important it is to have a transparent AI, not only for ethical reasons but also for operational ones, and the deterministic (or symbolic) approach is now becoming popular again.

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. 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.

• “It’s one of the most exciting areas in today’s machine learning,” says Brenden Lake, a computer and cognitive scientist at New York University.
• 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.
• Now researchers and enterprises are looking for ways to bring neural networks and symbolic AI techniques together.
• In fact, rule-based systems still account for most computer programs today, including those used to create deep learning applications.

Through enhancing and merging the advantages of statistical AI, such as machine learning, with the prowess of human-like symbolic knowledge and reasoning, our goal is to spark a revolution in AI, rather than a mere evolution. A key factor in evolution of AI will be dependent on a common programming framework that allows simple integration of both deep learning and symbolic logic. Chat GPT Symbolic AI relies on explicit, top-down knowledge representation and reasoning. Symbolic AI, more often than not, relies on explicit rules and algorithms to make decisions and solve problems, and humans can easily understand and explain their reasoning. The primary difference between neural networks and symbolic AI lies in their representation and processing of information.

These smart assistants leverage Symbolic AI to structure sentences by placing nouns, verbs, and other linguistic properties in their correct place to ensure proper grammatical syntax and semantic execution. For some, it is cyan; for others, it might be aqua, turquoise, or light blue. As such, initial input symbolic representations lie entirely in the developer’s mind, making the developer crucial.

To build AI that can do this, some researchers are hybridizing deep nets with what the research community calls “good old-fashioned artificial intelligence,” otherwise known as symbolic AI. The offspring, which they call neurosymbolic AI, are showing duckling-like abilities and then some. “It’s one of the most exciting areas in today’s machine learning,” says Brenden Lake, a computer and cognitive scientist at New York University.

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. While these advancements mark significant steps towards replicating human reasoning skills, current iterations of Neuro-symbolic AI systems still fall short of being able to solve more advanced and abstract mathematical problems. However, the future of AI with Neuro-Symbolic AI looks promising as researchers continue to explore and innovate in this space. The potential of Neuro-Symbolic AI in advancing AI capabilities and adaptability is immense, and we can expect to see more breakthroughs in the near future. Recently, awareness is growing that explanations should not only rely on raw system inputs but should reflect background knowledge.

This simple symbolic intervention drastically reduces the amount of data needed to train the AI by excluding certain choices from the get-go. “If the agent doesn’t need to encounter a bunch of bad states, then it needs less data,” says Fulton. While the project still isn’t ready for use outside the lab, Cox envisions a future in which cars with neurosymbolic AI could learn out in the real world, with the symbolic component acting as a bulwark against bad driving. “You can check which module didn’t work properly and needs to be corrected,” says team member Pushmeet Kohli of Google DeepMind in London.

Using symbolic knowledge bases and expressive metadata to improve deep learning systems. Metadata that augments network input is increasingly being used to improve deep learning system performances, e.g. for conversational agents. Metadata are a form of formally represented background knowledge, for example a knowledge base, a knowledge graph or other structured background knowledge, that adds further information or context to the data or system. 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.

“You could think of it as the space of possible questions that people can ask.” For a given state of the game board, the symbolic AI has to search this enormous space of possible questions to find a good question, which makes it extremely slow. Once trained, the deep nets far outperform the purely symbolic AI at generating questions. This symbolic ai example video shows a more sophisticated challenge, called CLEVRER, in which artificial intelligences had to answer questions about video sequences showing objects in motion. The video previews the sorts of questions that could be asked, and later parts of the video show how one AI converted the questions into machine-understandable form.

The challenge for any AI is to analyze these images and answer questions that require reasoning. 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.

These neuro-symbolic hybrid systems require less training data and track the steps required to make inferences and draw conclusions. We believe these systems will usher in a new era of AI where machines can learn more like the way humans do, by connecting words with images and mastering abstract concepts. Neuro-symbolic artificial intelligence can be defined as the subfield of artificial intelligence (AI) that combines neural and symbolic approaches. By symbolic we mean approaches that rely on the explicit representation of knowledge using formal languages—including formal logic—and the manipulation of language items (‘symbols’) by algorithms to achieve a goal.

One challenge is the knowledge acquisition bottleneck, as manually encoding all the rules and facts can be time-consuming and labor-intensive. Additionally, symbolic AI systems may struggle with handling uncertainty and reasoning about incomplete or ambiguous information, which are areas where sub-symbolic AI techniques like probabilistic models and neural networks excel. The hybrid artificial intelligence learned to play a variant of the game Battleship, in which the player tries to locate hidden “ships” on a game board.

It can, for example, use neural networks to interpret a complex image and then apply symbolic reasoning to answer questions about the image’s content or to infer the relationships between objects within it. AI neural networks are modeled after the statistical properties of interconnected neurons in the human brain and brains of other animals. In the case of images, this could include identifying features such as edges, shapes and objects. Like in so many other respects, deep learning has had a major impact on neuro-symbolic AI in recent years. This appears to manifest, on the one hand, in an almost exclusive emphasis on deep learning approaches as the neural substrate, while previous neuro-symbolic AI research often deviated from standard artificial neural network architectures [2]. However, we may also be seeing indications or a realization that pure deep-learning-based methods are likely going to be insufficient for certain types of problems that are now being investigated from a neuro-symbolic perspective.

As we progress, Google’s Search Generative Experience will mainly feature AI-generated content. Our company started automating and scaling content production for large brands during the Transformers era, which began in 2020. While we prioritize maintaining a good relationship between humans and technology, it’s evident that user expectations have evolved, and content creation has fundamentally changed already.

On the contrary, there are still prominent applications that rely on Symbolic AI to this day and age. We will highlight some main categories and applications where Symbolic AI remains highly relevant. Another concept we regularly neglect is time as a dimension of the universe. Some examples are our daily caloric requirements as we grow older, the number of stairs we can climb before we start gasping for air, and the leaves on trees and their colors during different seasons.