Neuro-Symbolic Artificial Intelligence: Current Trends

In that context, we can understand artificial neural networks as an abstraction of the physical workings of the brain, while we can understand formal logic as an abstraction of what we perceive, through introspection, when contemplating explicit cognitive reasoning. In order to advance the understanding of the human mind, it therefore appears to be a natural question to ask how these two abstractions can be related or even unified, or how symbol manipulation can arise from a neural substrate [1]. The general promise of NeSy AI lies in the hopes of a best-of-both worlds scenario, where the complementary strengths of neural and symbolic approaches can be combined in a favorable way. Due to the shortcomings of these two methods, they have been combined to create neuro-symbolic AI, which is more effective than each alone. According to researchers, deep learning is expected to benefit from integrating domain knowledge and common sense reasoning provided by symbolic AI systems.

Also, some tasks can’t be translated to direct rules, including speech recognition and natural language processing. In addition to the development of neuro-symbolic models which are inherently explainable and transparent, this project requires the application of these methods on social media data. The recent rise in hate, abuse, and fake news in online discourse [3, 4, 5, 6] has made it imperative that effective methods are developed, in particular, those which are interpretable metadialog.com [7]. 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.

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The general theme is also of importance in the context of the field of Cognitive Science [Besold17survey]. The workshop will include over 15 IBM talks, and 5 panels in various areas of theory and the application of neuro-symbolic AI. We will also have over 15 distinguished external speakers to share an overview of neuro-symbolic AI and its history. The registered participants will get access to the recording of all sessions after the event. The main limitation of symbolic AI is its inability to deal with complex real-world problems.

In addition, symbolic AI algorithms can often be more easily interpreted by humans, making them more useful for tasks such as planning and decision-making. Symbolic AI algorithms are designed to solve problems by reasoning about symbols and relationships between symbols. It is one form of assumption, and a strong one, while deep neural architectures contain other assumptions, usually about how they should learn, rather than what conclusion they should reach. The ideal, obviously, is to choose assumptions that allow a system to learn flexibly and produce accurate decisions about their inputs.

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In particular, it would be helpful for advancing the field to conduct experiments with different levels of sophistication in the logical aspects. For example, systems that utilize “flat” annotations (metadata tags that are simply keywords) are essentially operating on the logical level of “facts” only. We can then ask the question whether such a system can be improved by using, say, a class hierarchy of annotations (such as schema.org), which corresponds to making use of a knowledge base with simple logical implications (i.e., subclass relationships). If the answer to this is affermative, then more complex logical background knowledge can be attempted to be leveraged.

• One task of particular importance is known as knowledge completion (i.e., link prediction) which has the objective of inferring new knowledge, or facts, based on existing KG structure and semantics.
• Planning is used in a variety of applications, including robotics and automated planning.
• The lopsided count for first-order logic as opposed to propositional logic illustrates another shift in the subfield.
• 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 have been utilizing neural networks, for instance, to determine an item’s type of shape or color.
• But the benefits of deep learning and neural networks are not without tradeoffs.

Finally, symbolic AI is often used in conjunction with other AI approaches, such as neural networks and evolutionary algorithms. This is because it is difficult to create a symbolic AI algorithm that is both powerful and efficient. 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.

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Symbols also serve to transfer learning in another sense, not from one human to another, but from one situation to another, over the course of a single individual’s life. That is, a symbol offers a level of abstraction above the concrete and granular details of our sensory experience, an abstraction that allows us to transfer what we’ve learned in one place to a problem we may encounter somewhere else. In a certain sense, every abstract category, like chair, asserts an analogy between all the disparate objects called chairs, and we transfer our knowledge about one chair to another with the help of the symbol. The technology actually dates back to the 1950s, says expert.ai’s Luca Scagliarini, but was considered old-fashioned by the 1990s when demand for procedural knowledge of sensory and motor processes was all the rage. Now that AI is tasked with higher-order systems and data management, the capability to engage in logical thinking and knowledge representation is cool again. Maybe in the future, we’ll invent AI technologies that can both reason and learn.

In this line of effort, deep learning systems are trained to solve problems such as term rewriting, planning, elementary algebra, logical deduction or abduction or rule learning. These problems are known to often require sophisticated and non-trivial symbolic algorithms. Attempting these hard but well-understood problems using deep learning adds to the general understanding of the capabilities and limits of deep learning. It also provides deep learning modules that are potentially faster (after training) and more robust to data imperfections than their symbolic counterparts. Symbolic AI algorithms are designed to deal with the kind of problems that require human-like reasoning, such as planning, natural language processing, and knowledge representation.

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In this article, discover some examples of the most popular Natural Language Processing use cases and how NLP has been applied in different industries. To think that we can simply abandon symbol-manipulation is to suspend disbelief. Qualitative simulation, such as Benjamin Kuipers’s QSIM,[92] approximates human reasoning about naive physics, such as what happens when we heat a liquid in a pot on the stove. 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.

This prediction task requires knowledge of the scene that is out of scope for traditional computer vision techniques. More specifically, it requires an understanding of the semantic relations between the various aspects of a scene – e.g., that the ball is a preferred toy of children, and that children often live and play in residential neighborhoods. Knowledge completion enables this type of prediction with high confidence, given that such relational knowledge is often encoded in KGs and may subsequently be translated into embeddings.

What is Symbolic AI?

There seems to be a reasonable expectation that deep learning solutions may be much more suitable to address the subsymbolic-symbolic gap than previous connectionist machine learning technology. Symbolic AI algorithms are used in a variety of AI applications, including knowledge representation, planning, and natural language processing. 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. 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. Expert systems are monotonic; that is, the more rules you add, the more knowledge is encoded in the system, but additional rules can’t undo old knowledge.

The recent adaptation of deep neural network-based methods to reinforcement learning and planning domains has yielded remarkable progress on individual tasks. In pursuit of efficient and robust generalization, we introduce the Schema Network, an object-oriented generative physics simulator capable of disentangling multiple causes of events and reasoning backward through causes to achieve goals. The richly structured architecture of the Schema Network can learn the dynamics symbolic artificial intelligence of an environment directly from data. We compare Schema Networks with Asynchronous Advantage Actor-Critic and Progressive Networks on a suite of Breakout variations, reporting results on training efficiency and zero-shot generalization, consistently demonstrating faster, more robust learning and better transfer. We argue that generalizing from limited data and learning causal relationships are essential abilities on the path toward generally intelligent systems.

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Second, it can learn symbols from the world and construct the deep symbolic networks automatically, by utilizing the fact that real world objects have been naturally separated by singularities. Third, it is symbolic, with the capacity of performing causal deduction and generalization. Fourth, the symbols and the links between them are transparent to us, and thus we will know what it has learned or not – which is the key for the security of an AI system. We present the details of the model, the algorithm powering its automatic learning ability, and describe its usefulness in different use cases. 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. Symbolic AI’s adherents say it more closely follows the logic of biological intelligence because it analyzes symbols, not just data, to arrive at more intuitive, knowledge-based conclusions.

Still, from the resulting Table 2 we can see that the first four Kautz categories seem to provide a reasonably balanced perspective on the recent publications at these conferences. The fifth category in fact, as also discussed by Kautz in his address, was meant to be more forward-looking, a goal to aspire to in future research. We started out on our investigations with the hypothesis that the NeSy AI field has shifted focus in recent years, e.g. rendering some of the 2005 dimensions obsolete, at least for the time being. At this stage, however, we remark that the Kautz categories listed above seem to address only Interrelation aspects. However they cover these in a rather different way than the 2005 survey, with a focus on more precise architectural description of the system workflows. Formal applications should be accompanied by a research proposal and made via the University of Bath’s online application form.