ccclyu awesome-deeplogic: A collection of papers of neural-symbolic AI mainly focus on NLP applications

More advanced knowledge-based systems, such as Soar can also perform meta-level reasoning, that is reasoning about their own reasoning in terms of deciding how to solve problems and monitoring the success of problem-solving strategies. A manually exhaustive process that tends to be rather complex to capture and define all symbolic rules. The primary motivation behind Artificial Intelligence (AI) systems has always been to allow computers to mimic our behavior, to enable machines to think like us and act like us, to be like us.

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. During the first AI summer, many people thought that machine intelligence could be achieved in just a few years. By the mid-1960s neither useful natural language translation systems nor autonomous tanks had been created, and a dramatic backlash set in. 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.

The Rise of Deep Learning

Thus, standard learning algorithms are improved by fostering a greater understanding of what happens between input and output. 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. 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.

Hence, neurosymbolic AI seeks to ground rich knowledge into efficient sub-symbolic representations and to explain sub-symbolic representations and deep learning by offering high-level symbolic descriptions for such learning systems. Logic Tensor Networks (LTN) are a neurosymbolic AI system for querying, learning and reasoning with rich data and abstract knowledge. LTN introduces Real Logic, a fully differentiable first-order language with concrete semantics such that every symbolic expression has an interpretation that is grounded onto real numbers in the domain.

Neuro Symbolic Artificial Intelligence

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. 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.

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. An LNN consists of a neural network trained to perform symbolic reasoning tasks, such as logical inference, theorem proving, and planning, using a combination of differentiable logic gates and differentiable inference rules. These gates and rules are designed to mimic the operations performed by symbolic reasoning systems and are trained using gradient-based optimization techniques. We introduce the Deep Symbolic Network (DSN) model, which aims at becoming the white-box version of Deep Neural Networks (DNN).

TDWI Training & Research Business Intelligence, Analytics, Big Data, Data Warehousing

The full value of Neuro-Symbolic AI isn’t just in its elimination of the training data or taxonomy building delays that otherwise impede Natural Language Processing applications, cognitive search, or conversational AI. Nor is it only in the ease of generating queries and bettering the results of constraint systems, all of which it inherently does. The real reason for the adoption of composite AI is that, as Marvin Minsky alluded to in his society of mind metaphor, human intelligence is comprised of numerous systems (analogous to diverse society members or machines) working together to produce intelligent behavior. Similarly, AI requires an assortment of approaches and techniques working in conjunction to solve the myriad business problems organizations regularly apply to it. Together, these AI approaches create total machine intelligence with logic-based systems that get better with each application.

In Data Science, methods that exploit the semantics of knowledge graphs and Semantic Web technologies [7] as a way to add background knowledge to machine learning models have already started to emerge. Symbolic AI is an approach that trains Artificial Intelligence (AI) the same way human brain learns. It learns to understand the world by forming internal symbolic representations of its “world”.Symbols play a vital role in the human thought and reasoning process. We learn both objects and abstract concepts, then create rules for dealing with these concepts. These rules can be formalized in a way that captures everyday knowledge.Symbolic AI mimics this mechanism and attempts to explicitly represent human knowledge through human-readable symbols and rules that enable the manipulation of those symbols. Symbolic AI entails embedding human knowledge and behavior rules into computer programs.

Understanding the impact of open-source language models

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. We will highlight some main categories and applications where Symbolic AI remains highly relevant.

In symbolic AI, discourse representation theory and first-order logic have been used to represent sentence meanings. Latent semantic analysis (LSA) and explicit semantic analysis also provided vector representations of documents. In the latter case, vector components are interpretable as concepts named by Wikipedia articles.

Marrying expert systems with the neural network: the new neuro symbolic AI revolution

We have laid out some of the most important currently investigated research directions, and provided literature pointers suitable as entry points to an in-depth study of the current state of the art. We investigate an unconventional direction of research that aims at converting neural networks, a class of distributed, connectionist, sub-symbolic models into a symbolic level with the ultimate goal of achieving AI interpretability and safety. To that end, we propose Object-Oriented Deep Learning, a novel computational paradigm of deep learning that adopts interpretable “objects/symbols” as a basic representational atom instead of N-dimensional tensors (as in traditional “feature-oriented” deep learning). For visual processing, each “object/symbol” can explicitly package common properties of visual objects like its position, pose, scale, probability of being an object, pointers to parts, etc., providing a full spectrum of interpretable visual knowledge throughout all layers. It achieves a form of “symbolic disentanglement”, offering one solution to the important problem of disentangled representations and invariance.

• Data Science studies all steps of the data life cycle to tackle specific and general problems across the whole data landscape.
• Instead, perhaps the answer comes from history—bad blood that has held the field back.
• Extensions to first-order logic include temporal logic, to handle time; epistemic logic, to reason about agent knowledge; modal logic, to handle possibility and necessity; and probabilistic logics to handle logic and probability together.
• In the past decade, thanks to the large availability of data and processing power, deep learning has gained popularity and has pushed past symbolic AI systems.
• Symbolic AI is able to deal with more complex problems, and can often find solutions that are more elegant than those found by traditional AI algorithms.
• At Bosch Research in Pittsburgh, we are particularly interested in the application of neuro-symbolic AI for scene understanding.

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. The Symbolic AI paradigm led to seminal ideas in search, symbolic programming languages, agents, multi-agent systems, the semantic web, and the strengths and limitations of formal knowledge and reasoning systems. The greatest promise here is analogous to experimental particle physics, where large particle accelerators are built to crash atoms together and monitor their behaviors.

NAUTILUS: SCIENCE CONNECTED

Throughout the rest of this book, we will explore how we can leverage symbolic and sub-symbolic techniques in a hybrid approach to build a robust yet explainable model. It is also an excellent idea to represent our symbols and relationships using predicates. In short, a predicate is a symbol that denotes the individual components within our knowledge base. For example, we can use the symbol M to represent a movie and P to describe people. In the Symbolic AI paradigm, we manually feed knowledge represented as symbols for the machine to learn.

• But symbolic AI starts to break when you must deal with the messiness of the world.
• Many leading scientists believe that symbolic reasoning will continue to remain a very important component of artificial intelligence.
• Abstract

  Smart building and smart city specialists agree that complex, innovative use cases, especially those using cross-domain and multi-source data, need to make use of Artificial Intelligence (AI).

• Learn and understand each of these approaches and their main differences when applied to Natural Language Processing.elping all kinds of brands grasp what their consumers really want and fulfill their needs in real-time.
• Last but not least, it is more friendly to unsupervised learning than DNN.
• Symbolic AI is a sub-field of artificial intelligence that focuses on the high-level symbolic (human-readable) representation of problems, logic, and search.

The program improved as it played more and more games and ultimately defeated its own creator. In 1959, it defeated the best player, This created a fear of AI dominating AI. This lead towards the connectionist paradigm of AI, also called non-symbolic AI which gave rise to learning and neural network-based approaches to solve AI. The difficulties encountered by symbolic AI have, however, been deep, possibly unresolvable ones. One difficult problem encountered by symbolic AI pioneers came to be known as the common sense knowledge problem.

Data and knowledge in research – The case of the Life Sciences

In natural language processing, researchers have built large models with massive amounts of data using deep neural networks that cost millions of dollars to train. The next step lies in studying the networks to see how this can improve the construction of symbolic representations required for higher order language tasks. Recent approaches towards solving these challenges include representing symbol manipulation as operations performed by neural network [53,64], thereby enabling symbolic inference with distributed representations grounded in domain metadialog.com data. Other methods rely, for example, on recurrent neural networks that can combine distributed representations into novel ways [17,62]. In the future, we expect to see more work on formulating symbol manipulation and generation of symbolic knowledge as optimization problems. Differentiable theorem proving [53,54], neural Turing machines [20], and differentiable neural computers [21] are promising research directions that can provide the general framework for such an integration between solving optimization problems and symbolic representations.

• He was the founder and CEO of Geometric Intelligence, a machine-learning company acquired by Uber in 2016, and is Founder and Executive Chairman of Robust AI.
• We use symbols all the time to define things (cat, car, airplane, etc.) and people (teacher, police, salesperson).
• Regarding implementing symbolic AI, one of the oldest, yet still, the most popular, logic programming languages is Prolog comes in handy.
• Neuro Symbolic AI not only combines highly-acclaimed AI and machine learning approaches, but it also manages to bypass the majority of weak points and disadvantages that come with using each system separately.
• Moreover, they lack the ability to reason on an abstract level, which makes it difficult to implement high-level cognitive functions such as transfer learning, analogical reasoning, and hypothesis-based reasoning.
• Symbolic AI is reasoning oriented field that relies on classical logic (usually monotonic) and assumes that logic makes machines intelligent.

In fact, rule-based systems still account for most computer programs today, including those used to create deep learning applications. 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.

Monotonic means one directional, i.e. when one thing goes up, another thing goes up. Because ML algorithms can be retrained on new data and will revise their parameters based on that new data, they are better at encoding tentative knowledge that can be retracted later, if necessary, that is. if they need to learn something new, like when data is non-stationary. The development of neuro-symbolic AI is still in its early stages, and much work must be done to realize its potential fully. However, the progress made so far and the promising results of current research make it clear that neuro-symbolic AI has the potential to play a major role in shaping the future of AI.

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. Another way the two AI paradigms can be combined is by using neural networks to help prioritize how symbolic programs organize and search through multiple facts related to a question. For example, if an AI is trying to decide if a given statement is true, a symbolic algorithm needs to consider whether thousands of combinations of facts are relevant. Insufficient language-based data can cause issues when training an ML model.

In panicular, the problem of how to use neural networks to perform tedious Truth Maintenance System (TMS) functions of a multiple-context and/or nonmonotonic KBS is addressed. This directed mapping helps the system to use high-dimensional algebraic operations for richer object manipulations, such as variable binding — an open problem in neural networks. When these “structured” mappings are stored in the AI’s memory (referred to as explicit memory), they help the system learn—and learn not only fast but also all the time.