Symbolic AI vs Machine Learning in Natural Language Processing

Notably, deep learning algorithms are opaque, and figuring out how they work perplexes even their creators. 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.

(…) 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”. 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. Seddiqi expects many advancements to come from natural language processing. Language is a type of data that relies on statistical pattern matching at the lowest levels but quickly requires logical reasoning at higher levels.

Neuro Symbolic AI: Enhancing Common Sense in AI

For other AI programming languages see this list of programming languages for 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. When trying to develop intelligent systems, we face the issue of choosing how the system picks up information from the world around it, represents it and processes the same. Symbolic Artificial Intelligence, also known as Good Old Fashioned AI (GOFAI), makes use of strings that represent real-world entities or concepts. Non-Symbolic Artificial Intelligence involves providing raw environmental data to the machine and leaving it to recognize patterns and create its own complex, high-dimensionality representations of the raw sensory data being provided to it.

Using a simple statement as an example, we discussed the fundamental steps required to develop a symbolic program. An essential step in designing Symbolic AI systems is to capture and translate world knowledge into symbols. 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.

Explainability and Understanding

We are already integrating data from the KG inside reporting platforms like Microsoft Power BI and Google Looker Studio. A user-friendly interface (Dashboard) ensures that SEO teams can navigate smoothly through its functionalities. Against the backdrop, the Security and Compliance Layer shall be added to keep your data safe and in line with upcoming AI regulations (are we watermarking the content? Are we fact-checking the information generated?). The platform also features a Neural Search Engine, serving as the website’s guide, helping users navigate and find content seamlessly. Thanks to Content embedding, it understands and translates existing content into a language that an LLM can understand.

• For example, the insurance industry manages a lot of unstructured linguistic data from a variety of formats.
• This process loses the potentially recursive structure of the elements of the input space, which could be easily exploited by a program in a non-differentiable framework (think of graphs, hypergraphs, or even programs themselves as inputs).
• One of the main stumbling blocks of symbolic AI, or GOFAI, was the difficulty of revising beliefs once they were encoded in a rules engine.

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. Machine learning is an application of AI where statistical models perform specific tasks without using explicit instructions, relying instead on patterns and inference. Machine learning algorithms build mathematical models based on training data in order to make predictions.

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By utilizing the knowledge base effectively, businesses can ensure their AI chatbots provide outstanding customer service and support, leading to improved customer satisfaction and loyalty. Research in neuro-symbolic AI has a very long tradition, and we refer the interested reader to overview works such as Refs [1,3] that were written before the most recent developments. Indeed, neuro-symbolic AI has seen a significant increase in activity and research output in recent years, together with an apparent shift in emphasis, as discussed in Ref. [2]. Below, we identify what we believe are the main general research directions the field is currently pursuing.

Symbolic AI, also known as rule-based AI or classical AI, uses a symbolic representation of knowledge, such as logic or ontologies, to perform reasoning tasks. Symbolic AI relies on explicit rules and algorithms to make decisions and solve problems, and humans can easily understand and explain their reasoning. Although Symbolic AI paradigms can learn new logical rules independently, providing an input knowledge base that comprehensively represents the problem is essential and challenging. The symbolic representations required for reasoning must be predefined and manually fed to the system. With such levels of abstraction in our physical world, some knowledge is bound to be left out of the knowledge base.

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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. Symbolic AI assumes that the key to making machines intelligent is providing them with the rules and logic that make up our knowledge of the world.

“This is a prime reason why language is not wholly solved by current deep learning systems,” Seddiqi said. Now researchers and enterprises are looking for ways to bring neural networks and symbolic AI techniques together. How to explain the input-output behavior, or even inner activation states, of deep learning networks is a highly important line of investigation, as the black-box character of existing systems hides system biases and generally fails to provide a rationale for decisions. Recently, awareness is growing that explanations should not only rely on raw system inputs but should reflect background knowledge. While we cannot give the whole neuro-symbolic AI field due recognition in a brief overview, we have attempted to identify the major current research directions based on our survey of recent literature, and we present them below.

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Funnily enough, its limitations resulted in its inevitable death but are also primarily responsible for its resurrection. 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. This is not to say that Symbolic AI is wholly forgotten or no longer used. On the contrary, there are still prominent applications that rely on Symbolic AI to this day and age.

• Opposing Chomsky’s views that a human is born with Universal Grammar, a kind of knowledge, John Locke[1632–1704] postulated that mind is a blank slate or tabula rasa.
• 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.
• Unlike other branches of AI, such as machine learning and neural networks, which rely on statistical patterns and data-driven algorithms, symbolic AI emphasizes the use of explicit knowledge and explicit reasoning.
• Overall, LNNs is an important component of neuro-symbolic AI, as they provide a way to integrate the strengths of both neural networks and symbolic reasoning in a single, hybrid architecture.

Symbolic AI is typically rule-driven and uses symbolic representations for problem-solving.Neural AI, on the other hand, refers to artificial intelligence models based on neural networks, which are computational models inspired by the human brain. Neural AI focuses on learning patterns from data and making predictions or decisions based on the learned knowledge. It excels at tasks such as image and speech recognition, natural language processing, and sequential data analysis. Neural AI is more data-driven and relies on statistical learning rather than explicit rules. The recent availability of large-scale data combining multiple data modalities has opened various research and commercial opportunities in Artificial Intelligence (AI).

Symbolic AI vs Connectionism

Based on our knowledge base, we can see that movie X will probably not be watched, while movie Y will be watched. There are some other logical operators based on the leading operators, but these are beyond the scope of this chapter. Our journey through symbolic awareness ultimately significantly influenced how we design, program, and interact with AI technologies.

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. 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. Although deep learning has historical roots going back decades, neither the term “deep learning” nor the approach was popular just over five years ago, when the field was reignited by papers such as Krizhevsky, Sutskever and Hinton’s now classic (2012) deep network model of Imagenet. A second flaw in symbolic reasoning is that the computer itself doesn’t know what the symbols mean; i.e. they are not necessarily linked to any other representations of the world in a non-symbolic way.

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. 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]. A. Deep learning is a subfield of neural AI that uses artificial neural networks with multiple layers to extract high-level features and learn representations directly from data. Symbolic AI, on the other hand, relies on explicit rules and logical reasoning to solve problems and represent knowledge using symbols and logic-based inference.

Data Science and symbolic AI are the natural candidates to make such a combination happen. Data Science can connect research data with knowledge expressed in publications or databases, and symbolic AI can detect inconsistencies and generate plans to resolve them (see Fig. 2). In the context of autonomous driving, knowledge completion with KGEs can be used to predict entities in driving scenes that may have been missed by purely data-driven techniques.

We could go as far as providing a scene graph of existing and visible objects, assuming that identifying and locating objects could potentially be done via deep networks further down the architecture (with potential top-down influence added to the mix). The point is here to focus on the study of the cultural interaction and how the cultural hook works, not on the animal-level intelligence which is, in this developmental approach, not necessarily the most important part to get to human-level intelligence. As a side note, it’s interesting to see that the requirement of differentiable functions to process input data brings along the requirement to “flatten” data into vectors for input (or matrices, tensors, etc). This process loses the potentially recursive structure of the elements of the input space, which could be easily exploited by a program in a non-differentiable framework (think of graphs, hypergraphs, or even programs themselves as inputs).

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