Knowledge base completion (KBC) methods aim at inferring missing facts from the information present in a knowledge base (KB) by estimating the likelihood of candidate facts. In the prevailing evaluation paradigm, models do not actually decide whether a new fact should be accepted or not but are solely judged on the position of true facts in a likelihood ranking with other candidates. We argue that consideration of binary predictions is essential to reflect the actual KBC quality, and propose a novel evaluation paradigm, designed to provide more transparent model selection criteria for a realistic scenario. We construct the data set FB14k-QAQ where instead of single facts, we use KB queries, i.e., facts where one entity is replaced with a variable, and construct corresponding sets of entities that are correct answers. We randomly remove some of these correct answers from the data set, simulating the realistic scenario of real-world entities missing from a KB. This way, we can explicitly measure a model's ability to handle queries that have more correct answers in the real world than in the KB, including the special case of queries without any valid answer. The latter especially contrasts the ranking setting. We evaluate a number of state-of-the-art KB embeddings models on our new benchmark. The differences in relative performance between ranking-based and classification-based evaluation that we observe in our experiments confirm our hypothesis that good performance on the ranking task does not necessarily translate to good performance on the actual completion task. Our results motivate future work on KB embedding models with better prediction separability and, as a first step in that direction, we propose a simple variant of TransE that encourages thresholding and achieves a significant improvement in classification F1 score relative to the original TransE.

Medical toxicology is the clinical specialty that treats the toxic effects of substances, be it an overdose, a medication error, or a scorpion sting. The volume of toxicological knowledge and research has, as with other medical specialties, outstripped the ability of the individual clinician to entirely master and stay current with it. The application of machine learning techniques to medical toxicology is challenging because initial treatment decisions are often based on a few pieces of textual data and rely heavily on prior knowledge. ML techniques often do not represent knowledge in a way that is transparent for the physician, raising barriers to usability. Rule-based systems and decision tree learning are more transparent approaches, but often generalize poorly and require expert curation to implement and maintain. Here, we construct a probabilistic logic network to represent a portion of the knowledge base of a medical toxicologist. Our approach transparently mimics the knowledge representation and clinical decision-making of practicing clinicians. The software, dubbed Tak, performs comparably to humans on straightforward cases and intermediate difficulty cases, but is outperformed by humans on challenging clinical cases. Tak outperforms a decision tree classifier at all levels of difficulty. Probabilistic logic provides one form of explainable artificial intelligence that may be more acceptable for use in healthcare, if it can achieve acceptable levels of performance.

The field of Natural language processing can be formally defined as - "A theoretically motivated range of computational techniques for analyzing and representing naturally occurring texts at one or more levels of linguistic analysis for the purpose of achieving human-like language processing for a range of tasks or applications"[69]. The naturally occurring text can be in written or spoken form.A wide array of domains contribute to NLP development like linguistics, computer science and psychology.The linguistics field helps to understand the formal structure of language while computer science domain helps to find efficient internal representations and data structures.The study of "Psychology" can be useful to understand the methodology used by humans for dealing with languages. NLP can be considered to be having two distinct focus namely (1)Natural Language Generation(NLG) and (2)Natural Language Understanding(NLU). The NLG deals with planning to use the representation of language to decide what should be generated at each point in interaction, while NLU needs to analyze language and decide which is best way to represent it meaningfully.We, in this survey paper, concentrate on area of NLU for written text.Hence the NLP henceforth might be considered as NLU and vice versa. Motivation for designing Indian NLP systems Hindi and English are the official languages in central government of India(GOI). Indian community faces a "Digital Divide" due to dominance of English as mode of communication in higher education, judiciary, corporate sector and Public administration at Central level whereas the government in states work in their respective regional languages [67].The expansion of Internet has inter-connected the socioeconomic environment of the world and redefined the concept of global culture.As per a report in 2017 by the companies kpmg and Google

The field of Artificial Intelligence (AI) has undoubtedly received significant attention in recent years. AI is being adopted to provide solutions to problems in fields such as medicine, engineering, education, government and several other domains. In order to analyze the state of the art of research in the field of AI, we present a systematic literature review focusing on the Evolution of AI programming languages. We followed the systematic literature review method by searching relevant databases like SCOPUS, IEEE Xplore and Google Scholar. EndNote reference manager was used to catalog the relevant extracted papers. Our search returned a total of 6565 documents, whereof 69 studies were retained. Of the 69 retained studies, 15 documents discussed LISP programming language, another 34 discussed PROLOG programming language, the remaining 20 documents were spread between Logic and Object Oriented Programming (LOOP), ARCHLOG, Epistemic Ontology Language with Constraints (EOLC), Python, C++, ADA and JAVA programming languages. This review provides information on the year of implementation, development team, capabilities, limitations and applications of each of the AI programming languages discussed. The information in this review could guide practitioners and researchers in AI to make the right choice of languages to implement their novel AI methods.

Textual explanations have proved to help improve user satisfaction on machine-made recommendations. However, current mainstream solutions loosely connect the learning of explanation with the learning of recommendation: for example, they are often separately modeled as rating prediction and content generation tasks. In this work, we propose to strengthen their connection by enforcing the idea of sentiment alignment between a recommendation and its corresponding explanation. At training time, the two learning tasks are joined by a latent sentiment vector, which is encoded by the recommendation module and used to make word choices for explanation generation. At both training and inference time, the explanation module is required to generate explanation text that matches sentiment predicted by the recommendation module. Extensive experiments demonstrate our solution outperforms a rich set of baselines in both recommendation and explanation tasks, especially on the improved quality of its generated explanations. More importantly, our user studies confirm our generated explanations help users better recognize the differences between recommended items and understand why an item is recommended.

To reduce Operation and Maintenance (O&M) costs on offshore wind farms, wherein 80% of the O&M cost relates to deploying personnel, the offshore wind sector looks to robotics and Artificial Intelligence (AI) for solutions. Barriers to Beyond Visual Line of Sight (BVLOS) robotics include operational safety compliance and resilience, inhibiting the commercialization of autonomous services offshore. To address safety and resilience challenges we propose a symbiotic system; reflecting the lifecycle learning and co-evolution with knowledge sharing for mutual gain of robotic platforms and remote human operators. Our methodology enables the run-time verification of safety, reliability and resilience during autonomous missions. We synchronize digital models of the robot, environment and infrastructure and integrate front-end analytics and bidirectional communication for autonomous adaptive mission planning and situation reporting to a remote operator. A reliability ontology for the deployed robot, based on our holistic hierarchical-relational model, supports computationally efficient platform data analysis. We analyze the mission status and diagnostics of critical sub-systems within the robot to provide automatic updates to our run-time reliability ontology, enabling faults to be translated into failure modes for decision making during the mission. We demonstrate an asset inspection mission within a confined space and employ millimeter-wave sensing to enhance situational awareness to detect the presence of obscured personnel to mitigate risk. Our results demonstrate a symbiotic system provides an enhanced resilience capability to BVLOS missions. A symbiotic system addresses the operational challenges and reprioritization of autonomous mission objectives. This advances the technology required to achieve fully trustworthy autonomous systems.

Predictions obtained by, e.g., artificial neural networks have a high accuracy but humans often perceive the models as black boxes. Insights about the decision making are mostly opaque for humans. Particularly understanding the decision making in highly sensitive areas such as healthcare or finance, is of paramount importance. The decision-making behind the black boxes requires it to be more transparent, accountable, and understandable for humans. This survey paper provides essential definitions, an overview of the different principles and methodologies of explainable Supervised Machine Learning (SML). We conduct a state-of-the-art survey that reviews past and recent explainable SML approaches and classifies them according to the introduced definitions. Finally, we illustrate principles by means of an explanatory case study and discuss important future directions.

Although AI holds promise for improving human decision making in societally critical domains, it remains an open question how human-AI teams can reliably outperform AI alone and human alone in challenging prediction tasks (also known as complementary performance). We explore two directions to understand the gaps in achieving complementary performance. First, we argue that the typical experimental setup limits the potential of human-AI teams. To account for lower AI performance out-of-distribution than in-distribution because of distribution shift, we design experiments with different distribution types and investigate human performance for both in-distribution and out-of-distribution examples. Second, we develop novel interfaces to support interactive explanations so that humans can actively engage with AI assistance. Using in-person user study and large-scale randomized experiments across three tasks, we demonstrate a clear difference between in-distribution and out-of-distribution, and observe mixed results for interactive explanations: while interactive explanations improve human perception of AI assistance's usefulness, they may magnify human biases and lead to limited performance improvement. Overall, our work points out critical challenges and future directions towards complementary performance.

Multi-hop Knowledge Base Question Answering (KBQA) aims to find the answer entities that are multiple hops away in the Knowledge Base (KB) from the entities in the question. A major challenge is the lack of supervision signals at intermediate steps. Therefore, multi-hop KBQA algorithms can only receive the feedback from the final answer, which makes the learning unstable or ineffective. To address this challenge, we propose a novel teacher-student approach for the multi-hop KBQA task. In our approach, the student network aims to find the correct answer to the query, while the teacher network tries to learn intermediate supervision signals for improving the reasoning capacity of the student network. The major novelty lies in the design of the teacher network, where we utilize both forward and backward reasoning to enhance the learning of intermediate entity distributions. By considering bidirectional reasoning, the teacher network can produce more reliable intermediate supervision signals, which can alleviate the issue of spurious reasoning. Extensive experiments on three benchmark datasets have demonstrated the effectiveness of our approach on the KBQA task.

The emergence and continued reliance on the Internet and related technologies has resulted in the generation of large amounts of data that can be made available for analyses. However, humans do not possess the cognitive capabilities to understand such large amounts of data. Machine learning (ML) provides a mechanism for humans to process large amounts of data, gain insights about the behavior of the data, and make more informed decision based on the resulting analysis. ML has applications in various fields. This review focuses on some of the fields and applications such as education, healthcare, network security, banking and finance, and social media. Within these fields, there are multiple unique challenges that exist. However, ML can provide solutions to these challenges, as well as create further research opportunities. Accordingly, this work surveys some of the challenges facing the aforementioned fields and presents some of the previous literature works that tackled them. Moreover, it suggests several research opportunities that benefit from the use of ML to address these challenges.