Data Science and symbolic AI: Synergies, challenges and opportunities IOS Press

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. 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. Qualitative Spatial & Temporal Reasoning (QSTR)

is a major field of study in Symbolic AI that deals

with the representation and reasoning of spatio-

temporal information in an abstract, human-like

manner. Neuro-symbolic AI is a synergistic integration of knowledge representation (KR) and machine learning (ML) leading to improvements in scalability, efficiency, and explainability.

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. 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.

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Discover the fascinating fusion of knowledge graphs and LLMs in Neuro-symbolic AI, unlocking new frontiers of understanding and intelligence. Now that AI is increasingly being called upon to interact with humans, a more logical, knowledge-based approach is needed. “As impressive as things like transformers are on our path to natural language understanding, they are not sufficient,” Cox said. “Our vision is to use neural networks as a bridge to get us to the symbolic domain,” Cox said, referring to work that IBM is exploring with its partners.

Whenever one talks of some form of orthogonality in description spaces, this is in fact related to the notion of symbol, which you can oppose to entangled, irreducible descriptions. Commonly used for NLP and natural language understanding (NLU), symbolic follows an IF-THEN logic structure. This makes it easy to establish clear and explainable rules, providing full transparency into how it works. In doing so, you essentially bypass the “black box” problem endemic to machine learning. 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.

More from Michelle Zhao and Becoming Human: Artificial Intelligence Magazine

WordLift is leveraging a Generative AI Layer to create engaging, SEO-optimized content. We want to further extend its creativity to visuals (Image and Video AI subsystem), enhancing any multimedia asset and creating an immersive user experience. WordLift employs a Linked Data subsystem to market metadata to search engines, improving content visibility and user engagement directly on third-party channels. We are adding a new Chatbot AI subsystem to let users engage with their audience and offer real-time assistance to end customers.

• Inspired by progress in Data Science and statistical methods in AI, Kitano [37] proposed a new Grand Challenge for AI “to develop an AI system that can make major scientific discoveries in biomedical sciences and that is worthy of a Nobel Prize”.
• Thomas Hobbes, a British philosopher, famously said that thinking is nothing more than symbol manipulation, and our ability to reason is essentially our mind computing that symbol manipulation.
• They can be as simple as binary decision trees, or as complex as some elaborated python-like code or some other DSL (Domain Specific Language) adapted for AI.
• Newly introduced rules are added to the existing knowledge, making Symbolic AI significantly lack adaptability and scalability.
• These symbols can represent objects, concepts, or situations, and the rules define how these symbols can be manipulated or combined to derive new knowledge or make inferences.

So, as humans creating intelligent systems, it makes sense to have applications that have understandable and interpretable blocks/processes in them. Therefore, throwing the symbols away may put AI out of circulation from human understanding, and after a point, intelligent systems will make decisions because “they mathematically can”. Also, Non-symbolic AI systems generally depend on formally defined mathematical optimization tools and concepts. That involves modeling the whole problem statement in terms of an optimization problem.

All you need to know about symbolic artificial intelligence

Literature references within this text are limited to general overview articles, but a supplementary online document referenced at the end contains references to concrete examples from the recent literature. Examples for historic overview works that provide a perspective on the field, including cognitive science aspects, prior to the recent acceleration in activity, are Refs [1,3]. Logical Neural Networks (LNNs) are neural networks that incorporate symbolic reasoning in their architecture. In the context of neuro-symbolic AI, LNNs serve as a bridge between the symbolic and neural components, allowing for a more seamless integration of both reasoning methods. Symbolic AI provides numerous benefits, including a highly transparent, traceable, and interpretable reasoning process. So, maybe we are not in a position yet to completely disregard Symbolic AI.

Therefore, a well-defined and robust knowledge base (correctly structuring the syntax and semantic rules of the respective domain) is vital in allowing the machine to generate logical conclusions that we can interpret and understand. We propose the Neuro-Symbolic Concept Learner (NS-CL), a model that learns visual concepts, words, and semantic parsing of sentences without explicit supervision on any of them; instead, our model learns by simply looking at images and reading paired questions and answers. 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.

Through logical rules, Symbolic AI systems can efficiently find solutions that meet all the required constraints. Symbolic AI is widely adopted throughout the banking and insurance industries to automate processes such as contract reading. Another recent example of logical inferencing is a system based on the physical activity guidelines provided by the World Health Organization (WHO).

This figure summarizes our vision of Data Science as the core intersection between disciplines that fosters integration, communication and synergies between them. Data Science studies all steps of the data life cycle to tackle specific and general problems across the whole data landscape. While both frameworks have their advantages and drawbacks, it is perhaps a combination of the two that will bring scientists closest to achieving true artificial human intelligence. WordLift will become an intelligent orchestrator for the company’s online presence. It builds a comprehensive Knowledge Graph, the pulsing heart of the platform.

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. Deep reinforcement learning (DRL) brings the power of deep neural networks to bear on the generic task of trial-and-error learning, and its effectiveness has been convincingly demonstrated on tasks such as Atari video games and the game of Go. However, contemporary DRL systems inherit a number of shortcomings from the current generation of deep learning techniques. For example, they require very large datasets to work effectively, entailing that they are slow to learn even when such datasets are available.

While symbolic AI requires constant information input, neural networks could train on their own given a large enough dataset. Although everything was functioning perfectly, as was already noted, a better system is required due to the difficulty in interpreting the model and the amount of data required to continue learning. Knowledge representation and formalization are firmly based on the categorization of various types of symbols.

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You’ll begin by exploring the decline of symbolic AI and the recent neural network revolution, as well as their limitations. The book then delves into the importance of building trustworthy and transparent AI solutions using explainable AI techniques. As you advance, you’ll explore the emerging field of neuro-symbolic AI, which combines symbolic AI and modern neural networks to improve performance and transparency.

Together, these AI approaches create total machine intelligence with logic-based systems that get better with each application. One of Galileo’s key contributions was to realize that laws of nature are inherently mathematical and expressed symbolically, and to identify symbols that stand for force, objects, mass, motion, and velocity, ground these symbols in perceptions of phenomena in the world. This task may be achievable through feature learning or ontology learning methods, together with an ontological commitment [23] that assigns an ontological interpretation to mathematical symbols. However, given sufficient data about moving objects on Earth, any statistical, data-driven algorithm will likely come up with Aristotle’s theory of motion [56], not Galileo’s principle of inertia. On a high level, Aristotle’s theory of motion states that all things come to a rest, heavy things on the ground and lighter things on the sky, and force is required to move objects.

Scagliarini says the rules of symbolic AI resist drift, so models can be created much faster and with far less data to begin with, and then require less retraining once they enter production environments. 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 time-consuming job, as there are 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. At a more concrete level, realizing the above program for developmental AI involves building child-like machines that are immersed in a rich cultural environment, involving humans, where they will be able to participate in learning games. These games are not innate (they are part of the cultural background, so they are subject to another type of evolutionary dynamics than the one of the genes), but must be learned from adults and passed on to other generations.

Also, some tasks can’t be translated to direct rules, including speech recognition and natural language processing. The two big arrows symbolize the integration, retro-donation, communication needed between Data Science and methods to process knowledge from symbolic AI that enable the flow of information in both directions. The Bosch code of ethics for AI emphasizes the development of safe, robust, and explainable AI products. By providing explicit symbolic representation, neuro-symbolic methods enable explainability of often opaque neural sub-symbolic models, which is well aligned with these esteemed values.

This process is also widely used to discover and eliminate physical bias in a machine learning model. For example, ILP was previously used to aid in an automated recruitment task by evaluating candidates’ Curriculum Vitae (CV). Due to its expressive nature, Symbolic AI allowed the developers to trace back the result to ensure that the inferencing model was not influenced by sex, race, or other discriminatory properties. We observe its shape and size, its color, how it smells, and potentially its taste. In short, we extract the different symbols and declare their relationships.

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