Symbolic AI v s Non-Symbolic AI, and everything in between? by Rhett D’souza DataDrivenInvestor
By doing this, the inference engine is able to draw conclusions based on querying the knowledge base, and applying those queries to input from the user. In a nutshell, Symbolic AI has been highly performant in situations where the problem is already known and clearly defined (i.e., explicit knowledge). Translating our world knowledge into logical rules can quickly become a complex task. While in Symbolic AI, we tend to rely heavily on Boolean logic computation, the world around us is far from Boolean. For example, a digital screen’s brightness is not just on or off, but it can also be any other value between 0% and 100% brightness.
In those cases, rules derived from domain knowledge can help generate training data. For example, the fact that two concepts are disjoint can provide crucial information about the relation between two concepts, but this information can be encoded syntactically in many different ways. For model-theoretic languages, it is also possible to analyze the model structures instead of the statements entailed from a knowledge graph. While there are usually infinitely many models of arbitrary cardinality , it is possible to focus on special (canonical) models in some languages such as the Description Logics ALC. These model structures can then be analyzed instead of syntactically formed graphs, and for example used to define similarity measures .
Symbolic AI and Expert Systems: Unveiling the Foundation of Early Artificial Intelligence
As proof-of-concept, we present a preliminary implementation of the architecture and apply it to several variants of a simple video game. We show that the resulting system – though just a prototype – learns effectively, and, by acquiring a set of symbolic rules that are easily comprehensible to humans, dramatically outperforms a conventional, fully neural DRL system on a stochastic variant of the game. In the history of the quest for human-level artificial intelligence, a number of rival paradigms have vied for supremacy. Symbolic artificial intelligence was dominant for much of the 20th century, but currently a connectionist paradigm is in the ascendant, namely machine learning with deep neural networks. However, both paradigms have strengths and weaknesses, and a significant challenge for the field today is to effect a reconciliation.
A neural network can carry out certain tasks exceptionally well, but much of its inner reasoning is “black boxed,” rendered inscrutable to those who want to know how it made its decision. Again, this doesn’t matter so much if it’s a bot that recommends the wrong track on Spotify. But if you’ve been denied a bank loan, rejected from a job application, or someone has been injured in an incident involving an autonomous car, you’d better be able to explain why certain recommendations have been made. 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. Non-Symbolic AI (like Deep Learning algorithms) are intensely data hungry.
Fundamentals and Applications
Researchers aimed to create programs that could reason logically and manipulate symbols to solve complex problems. 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”. 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.
Symbolic approaches to Artificial Intelligence (AI) represent things within a domain of knowledge through physical symbols, combine symbols into symbol expressions, and manipulate symbols and symbol expressions through inference processes. While a large part of Data Science relies on statistics and applies statistical approaches to AI, there is an increasing potential for successfully applying symbolic approaches as well. Here we discuss the role symbolic representations and inference can play in Data Science, highlight the research challenges from the perspective of the data scientist, and argue that symbolic methods should become a crucial component of the data scientists’ toolbox.
But it’s next to impossible for today’s state-of-the-art neural networks. And it needs to happen by reinventing artificial intelligence as we know it. Thus “messy” problems such as image recognition are ideally handled by neural networks — subsymbolic AI. 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.
Symbolic Artificial Intelligence
A neuro-symbolic system employs logical reasoning and language processing to respond to the question as a human would. However, in contrast to neural networks, it is more effective and takes extremely less training data. One of the most common applications of symbolic AI is natural language processing (NLP).
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. They have created a revolution in computer vision applications such as facial recognition and cancer detection. Symbolic AI involves the explicit embedding of human knowledge and behavior rules into computer programs. The practice showed a lot of promise in the early decades of AI research.
The source of this mistrust lies in the algorithms used in the most common AI models like machine learning (ML) and deep learning (DL). These are often described as the “black box” of AI because their models are usually trained to use inference rather than actual knowledge to identify patterns and leverage information. In addition to this, by design, most models must be rebuilt from scratch whenever they produce inaccurate or undesirable results, which only increases costs and breeds frustration that can hamper AI’s adoption in the knowledge workforce.
In the black box world of ML and DL, changes to input data can cause models to drift, but without a deep analysis of the system, it is impossible to determine the root cause of these changes. In this world, almost everything can be well understood by humans using symbols. Suppose it’s describing objects, actions, abstract activities, things that don’t occur physically.
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Pushing performance for NLP systems will likely be akin to augmenting deep neural networks with logical reasoning capabilities. For almost any type of programming outside of statistical learning algorithms, symbolic processing is used; consequently, it is in some way a necessary part of every AI system. Indeed, Seddiqi said he finds it’s often easier to program a few logical rules to implement some function than to deduce them with machine learning. It is also usually the case that the data needed to train a machine learning model either doesn’t exist or is insufficient.
They require huge amounts of data to be able to learn any representation effectively. They also create representations that are too mathematically abstract or complex, to be viewed and understood.Taking the example of the Mandarin translator, he would translate it for you, but it would be very hard for him to exactly explain how he did it so instantaneously. Additionally, becoming an expert in English to Mandarin translation is no easy process. For example, we use neural networks to recognize the color and shape of an object. When symbolic reasoning is applied in this system, it will now have the ability to identify furthermore properties of the object such as its volume, total area, etc. As we got deeper into researching and innovating the sub-symbolic computing area, we were simultaneously digging another hole for ourselves.
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). During training and inference using such an AI system, the neural network accesses the explicit memory using expensive soft read and write operations.
- Our model builds an object-based scene representation and translates sentences into executable, symbolic programs.
- Early work covered both applications of formal reasoning emphasizing first-order logic, along with attempts to handle common-sense reasoning in a less formal manner.
- However, in contrast to neural networks, it is more effective and takes extremely less training data.
- 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.
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Complex problem solving through coupling of deep learning and symbolic components. 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. Very tight coupling can be achieved for example by means of Markov logics.
Newly introduced rules are added to the existing knowledge, making Symbolic AI significantly lack adaptability and scalability. One power that the human mind has mastered over the years is adaptability. Humans can transfer knowledge from one domain to another, adjust our skills and methods with the times, and reason about and infer innovations. For Symbolic AI to remain relevant, it requires continuous interventions where the developers teach it new rules, resulting in a considerably manual-intensive process. Surprisingly, however, researchers found that its performance degraded with more rules fed to the machine. Additionally, it introduces a severe bias due to human interpretability.
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