Complex query answering (CQA) goes beyond the well-studied link prediction task by addressing more sophisticated queries that require multi-hop reasoning over incomplete knowledge graphs (KGs). Research on neural and neurosymbolic CQA methods is still an emerging field. Almost all of these methods can be regarded as black-box models, which may raise concerns about user trust. Although neurosymbolic approaches like CQD are slightly more interpretable, allowing intermediate results to be tracked, the importance of different parts of the query remains unexplained. In this paper, we propose CQD-SHAP, a novel framework that computes the contribution of each query part to the ranking of a specific answer. This contribution explains the value of leveraging a neural predictor that can infer new knowledge from an incomplete KG, rather than a symbolic approach relying solely on existing facts in the KG. CQD-SHAP is formulated based on Shapley values from cooperative game theory and satisfies all the fundamental Shapley axioms. Automated evaluation of these explanations in terms of necessary and sufficient explanations, and comparisons with various baselines, shows the effectiveness of this approach for most query types.

We introduce the concept of a \textbf{neuro-symbolic pair} -- neural and symbolic approaches that are linked through a common knowledge representation. Next, we present \textbf{taxonomic networks}, a type of discrimination network in which nodes represent hierarchically organized taxonomic concepts. Using this representation, we construct a novel neuro-symbolic pair and evaluate its performance. We show that our symbolic method learns taxonomic nets more efficiently with less data and compute, while the neural method finds higher-accuracy taxonomic nets when provided with greater resources. As a neuro-symbolic pair, these approaches can be used interchangeably based on situational needs, with seamless translation between them when necessary. This work lays the foundation for future systems that more fundamentally integrate neural and symbolic computation.

Exceptional added value may lie in connecting two complementary areas of computer science. This statement is particularly true when applying mature techniques developed in one area to solve complex problems that arise in a new area. The accompanying paper, "An Automata-Based Framework for Verification and Bug Hunting in Quantum Circuits" by Lengál et al., is a case in point. It applies techniques developed in logic, automata, and symbolic verification to analyze the correctness of quantum programs. The current quest of quantum computing is achieving quantum supremacy--that is, to reach the point where we solve problems that are practically unsolvable using conventional computing.

There is a disagreement among professionals over artificial general intelligence definition. Artificial general intelligence (AGI) is the capacity for machines to perceive, learn, and carry out intellectual tasks in a manner similar to that of humans. AGI allows machines to mimic human behavior and thought processes in order to tackle any kind of complex problem. The performance of these machines is identical to that of humans due to their design, which includes comprehensive knowledge and cognitive computing skills. Artificial general intelligence (AGI) is the software representation of generalized human cognitive capabilities so that the AGI system can come up with a solution when presented with a challenging issue. AGI systems are designed to carry out any task that humans are capable of. Because specialists in various domains interpret human intelligence differently, there are many possible definitions of AGI.

From intelligent robots to multi-player gaming, from pattern recognition to fraud prevention, from human safety to weather prediction, AI is shaping every industry in one way or the other. Read on to know what is Artificial Intelligence and what are its methods, goals, and application areas. Artificial Intelligence (AI) has been the talking point of technological advancement for over seven decades. Researchers have managed to develop several features using AI otherwise believed to be impossible. From facial recognition to Chabot, from personal assistants to preference-based ads, AI lies at the centre of several amazing fields today.

From your average technology consumer to some of the most sophisticated organizations, it is amazing how many people think machine learning is artificial intelligence or consider it the best of AI. This perception persists mostly because of the general public's fascination with deep learning and neural networks, which several people regard as the most cutting-edge deployments of modern AI. The reality, however, is much more complex. There are certainly use cases in which machine learning is very capable. For example, it works well for computer vision applications of image recognition or object detection.

You're likely familiar with the saying, "Texting is a brilliant way to miscommunicate how you feel and misinterpret what other people mean." You've probably even experienced it directly! Substitute "texting" with "email" or "online reviews" and you've struck the nerve of businesses worldwide. Gaining a proper understanding of what clients and consumers have to say about your product or service or, more importantly, how they feel about your brand, is a universal struggle for businesses everywhere. What if I told you it doesn't have to be this way?

We find it in everything from emails to videos to business documents and beyond. However, as pervasive as language data is to the enterprise, organizations struggle to maximize its value. Not only is there an incredible amount of language data available to and contained within organizations, but an exponentially increasing volume of it, as well. There is no ignoring the importance of language to the enterprise ecosystem. Organizations are listening, as 42% have already adopted natural language processing (NLP) systems while 26% plan to within the next year, according to IBM's Global AI Adoption Index 2021.

As the research community makes progress in artificial intelligence and deep learning, scientists are increasingly feeling the need to move towards hybrid artificial intelligence. Hybrid AI is touted to solve fundamental problems that deep learning faces today. Hybrid AI brings together the best aspects of neural networks and symbolic AI. Combining huge data sets (visual and audio, textual, emails, chat logs, etc.) allows neural networks to extract patterns. Then, rule-based AI systems can manipulate the retrieved information by using algorithms to manipulate symbols.

The story of machine learning starts in 1943 when neurophysiologist Warren McCulloch and mathematician Walter Pitts introduced a mathematical model of a neural network. The field gathered steam in 1956 at a summer conference on the campus of Dartmouth College. There, 10 researchers came together for six weeks to lay the ground for a new field that involved neural networks, automata theory and symbolic reasoning. The distinguished group, many of whom would go on to make seminal contributions to this new field, gave it the name artificial intelligence to distinguish it from cybernetics, a competing area of research focused on control systems. In some ways these two fields are now starting to converge with the growth of IoT, but that is a topic for another day.