Neuro-Symbolic AI: Bridging the Gap Between Traditional and Modern AI Approaches

ML describes the ability to find patterns and make decisions without instruction or pre-programming, that is, the power of computer systems to truly “learn” on their own. ML, therefore, includes a subset of AI, but not the other way around. It is daunting to contemplate a future in which machines are better than humans at human things. Moreover, we cannot accurately predict the impact of AI advances on our future world. Even the problem of eradicating things like disease and poverty is not fully understood yet.

• For other AI programming languages see this list of programming languages for artificial intelligence.
• Unlike the other two learning types, RL changes the nature of supervision.
• For model-theoretic languages, it is also possible to analyze the model structures instead of the statements entailed from a knowledge graph.
• Therefore, symbols have also played a crucial role in the creation of artificial intelligence.

This chapter also briefly introduced the topic of Boolean logic and how it relates to Symbolic AI. Typically, an easy process but depending on use cases might be resource exhaustive. Based on our knowledge base, we can see that movie X will probably not be watched, while movie Y will be watched.

What is Artificial Intelligence?

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. A key component of the system architecture for all expert systems is the knowledge base, which stores facts and rules for problem-solving.[52]
The simplest approach for an expert system knowledge base is simply a collection or network of production rules. Production rules connect symbols in a relationship similar to an If-Then statement.

These models are able to represent entire paragraphs of text in context as a vector, and not only each word individually. In particular, people started predicting (inferring) next word in web-scale datasets and getting high accuracies and high text compression. Back then, the approach was that you would have even, like, dedicated hardware, like the one on the right, and you would write for your problem. You would collect a group of experts that would understand the domain.

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The similarity search on these wide vectors can be efficiently computed by exploiting physical laws such as Ohm’s law and Kirchhoff’s current summation law. These soft reads and writes form a bottleneck when implemented in the conventional von Neumann architectures (e.g., CPUs and GPUs), especially for AI models demanding over millions of memory entries. Thanks to the high-dimensional geometry of our resulting vectors, their real-valued components can be approximated by binary, or bipolar components, taking up less storage. More importantly, this opens the door for efficient realization using analog in-memory computing.

Notably, deep learning algorithms are opaque, and figuring out how they work perplexes even their creators. And it’s very hard to communicate and troubleshoot their inner-workings. There have been several efforts to create complicated symbolic AI systems that encompass the multitudes of rules of certain domains. Called expert systems, these symbolic AI models use hardcoded knowledge and rules to tackle complicated tasks such as medical diagnosis. But they require a huge amount of effort by domain experts and software engineers and only work in very narrow use cases. As soon as you generalize the problem, there will be an explosion of new rules to add (remember the cat detection problem?), which will require more human labor.

AI systems have very different strengths and weaknesses than human scientists. The expectation is that combining both ways of thinking will provide synergies. Indeed, the evidence from human-software symbiosis has shown that the fusion of automated and human exploration of complex systems can yield efficient and effective solution discovery (Kasparov, 2017).

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”. 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 [60], it is possible to focus on special (canonical) models in some languages such as the Description Logics ALC.

Intelligent machines can help to collect, store, search, process and reason over both data and knowledge. For a long time, a dominant approach to AI was based on symbolic representations and treating “intelligence” or intelligent behavior primarily as symbol manipulation. In a physical symbol system [46], entities called symbols (or tokens) are physical patterns that stand for, or denote, information from the external environment.

The world faces many global challenges, from climate change to antibiotic bacterial resistance. Solutions to many – if not all – of these challenges require augmented scientific knowledge. Until quite recently, the role of artificial intelligence (AI) in science received little attention.

All you need to know about symbolic artificial intelligence

The model’s predictive power depends, in part, on how well we selected our function beforehand. Finally, these models are exceedingly good at reflex-based games and not so great at reasoning and memory. In the Dota 2 example, the developers had to set an artificial reaction time limit to produce fair competition between the players and the model. On the other hand, it can’t beat decently experienced players at some games where complex thought is the primary force of competition. As removing outliers manually can be daunting and prone to human error, a machine learning model can do it without any issues.

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. The concept of fuzziness adds a lot of extra complexities to designing Symbolic AI systems. Due to fuzziness, multiple concepts become deeply abstracted and complex for Boolean evaluation.

Models trained through RL have beaten world-class players at chess, poker, StarCraft, Dota 2, and many other incredibly complicated games. From all the headlines it may seem that reinforcement learning brings us close to true AI. Following Turing, there have been some developments in philosophy that have questioned his approach with the famous one being Searle’s Chinese Room Argument. In short, Searle thought that there was an important difference between the ability to imitate (which can be expressed with if-then statements) and understanding.

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. Note that none of the above methods are any better than one another.

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. Limitations were discovered in using simple first-order logic to reason about dynamic domains. Problems were discovered both with regards to enumerating the preconditions for an action to succeed and in providing axioms for what did not change after an action was performed. 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. Symbolic artificial intelligence, also known as Good, Old-Fashioned AI (GOFAI), was the dominant paradigm in the AI community from the post-War era until the late 1980s.

The purpose of this paper is to generate broad interest to develop it within an open source project centered on the Deep Symbolic Network (DSN) model towards the development of general AI. 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.

What constitutes “replicating human cognition or intelligence” remains a hotly debated topic in numerous fields, including ones less spoken of such as philosophy. We can identify humans in pictures and videos, and AI has also gained that capability. We never expect a human to have four wheels and emit carbon like a car. Yet an AI system couldn’t surmise this unless trained on enough data. Such transformed binary high-dimensional vectors are stored in a computational memory unit, comprising a crossbar array of memristive devices. A single nanoscale memristive device is used to represent each component of the high-dimensional vector that leads to a very high-density memory.

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