The global Artificial Intelligence company Expert System announced the release of the expert.ai NL API, the cloud-based Natural Language API that enables data scientists, computational linguists, knowledge engineers and developers to easily embed advanced Natural Language Understanding and Natural Language Processing capabilities (NLU / NLP) into their applications. This release is the first step in executing on the company's strategy to become the global platform of reference for AI-based Natural Language problem solving. The growing need for accessible and accurate AI-based NLU / NLP applications in the enterprise places increased demand on the developer ecosystem to bring speed, scale and precision to linguistic analysis. According to Gartner, "during recent years, advances in the application of machine learning (including neural networks) and knowledge graphs to natural language processing have enabled machine-based attribution that diminishes the need for human oversight. Application of the technology is broadening as well as deepening -- across industries and functional domains, and into use cases -- pushing this innovation from many years in the Tough of Disillusionment toward the Slope of Enlightenment."

Expert System's Cogito is the only Natural Language Understanding AI technology that provides a human-like understanding of the meaning of each word in a text. Cogito leverages the deepest text analysis, starting from linguistics (morphological, grammatical and syntactical analysis) to semantics, including word disambiguation and an embedded, domain-independent and pre-trained linguistic model (Knowledge Graph). This translates into the fastest, most accurate and cost effective implementation of AI in the enterprise.

Unsatisfying accuracy of learning methods is mostly caused by omitting the influence of important parameters such as membership assignments, type of data objects, and distance or similarity functions. The proposed method, called Bounded Fuzzy Possibilistic Method (BFPM) addresses different issues that previous clustering or classification methods have not sufficiently considered in their membership assignments. In fuzzy methods, the object's memberships should sum to 1. Hence, any data object may obtain full membership in at most one cluster or class. Possibilistic methods relax this condition, but the method can be satisfied with the results even if just an arbitrary object obtains the membership from just one cluster, which prevents the objects' movement analysis. Whereas, BFPM differs from previous fuzzy and possibilistic approaches by removing these restrictions. Furthermore, BFPM provides the flexible search space for objects' movement analysis. Data objects are also considered as fundamental keys in learning methods, and knowing the exact type of objects results in providing a suitable environment for learning algorithms. The Thesis introduces a new type of object, called critical, as well as categorizing data objects into two different categories: structural-based and behavioural-based. Critical objects are considered as causes of miss-classification and miss-assignment in learning procedures. The Thesis also proposes new methodologies to study the behaviour of critical objects with the aim of evaluating objects' movements (mutation) from one cluster or class to another. The Thesis also introduces a new type of feature, called dominant, that is considered as one of the causes of miss-classification and miss-assignments. Then the Thesis proposes new sets of similarity functions, called Weighted Feature Distance (WFD) and Prioritized Weighted Feature Distance (PWFD).

The paper presents the approach for the building of consistent and applicable clinical decision support systems (CDSS) using a data-driven predictive model aimed to resolve a problem of low applicability and scalability of CDSS in real-world applications. The approach is based on the three-stage application of domain-specific and data-driven supportive procedures to integrate into clinical business-processes with higher trust and explainability of the prediction results and recommendations. Within the considered three stages, the regulatory policy, data-driven modes, and interpretation procedures are integrated to enable natural domain-specific interaction with decision-makers with sequential narrowing of the intelligent decision support focus. The proposed methodology enables a higher level of automation, scalability, and semantic interpretability of CDSS. The approach was implemented in software solutions and tested within a case study in T2DM prediction, enabling to improve known clinical scales (such as FINDRISK), keeping the problem-specific reasoning interface similar to existing applications. Such inheritance, together with the three-stages approach, provide higher compatibility of the solution and leads to trust, valid, and explainable application of data-driven solution in real-world cases.

Global artificial intelligence (AI) company, Expert System has announced that the French National Institute of Health and Medical Research, Inserm, will implement its Clinical Research Navigator (CRN) tool and make it available to 100 of its researchers for the next six months. This move will enable researchers to identify key clinical trials, sponsoring research facilities, lead researchers and related work, and even map networks of collaborators and key players. With CRN provided from Expert System, Inserm is able to provide researchers with unlimited access to over 100 million documents and reference information on 12 million clinical studies. One of the core functions of Expert System's CRN platform is to intelligently research and analyse content based on concepts and not just keywords. Through this centralised platform, researchers will be able to discover insights to drive their research by semantically revealing hidden connections across various information sources.

Recent work has suggested that language models (LMs) store both common-sense and factual knowledge learned from pre-training data. In this paper, we leverage this implicit knowledge to create an effective end-to-end fact checker using a solely a language model, without any external knowledge or explicit retrieval components. While previous work on extracting knowledge from LMs have focused on the task of open-domain question answering, to the best of our knowledge, this is the first work to examine the use of language models as fact checkers. In a closed-book setting, we show that our zero-shot LM approach outperforms a random baseline on the standard FEVER task, and that our fine-tuned LM compares favorably with standard baselines. Though we do not ultimately outperform methods which use explicit knowledge bases, we believe our exploration shows that this method is viable and has much room for exploration.

Human collaborators can effectively communicate with their partners to finish a common task by inferring each other's mental states (e.g., goals, beliefs, and desires). Such mind-aware communication minimizes the discrepancy among collaborators' mental states, and is crucial to the success in human ad-hoc teaming. We believe that robots collaborating with human users should demonstrate similar pedagogic behavior. Thus, in this paper, we propose a novel explainable AI (XAI) framework for achieving human-like communication in human-robot collaborations, where the robot builds a hierarchical mind model of the human user and generates explanations of its own mind as a form of communications based on its online Bayesian inference of the user's mental state. To evaluate our framework, we conduct a user study on a real-time human-robot cooking task. Experimental results show that the generated explanations of our approach significantly improves the collaboration performance and user perception of the robot. Code and video demos are available on our project website: https://xfgao.github.io/xCookingWeb/.

Agricultural production has become a complex business that requires the accumulation and integration of knowledge, in addition to information from many different sources. To remain competitive, the modern farmer often relies on agricultural specialists and advisors who provide them with information for decision making in their crops. But unfortunately, the help of the agricultural specialist is not always available when the farmer needs it. To alleviate this problem, expert systems have become a powerful instrument that has great potential within agriculture. This paper presents an Expert System for the diagnosis of diseases and pests in rice, tobacco, tomato, pepper, corn, cucumber and bean crops. For the development of this Expert System, SWI-Prolog was used to create the knowledge base, so it works with predicates and allows the system to be based on production rules. This system allows a fast and reliable diagnosis of pests and diseases that affect these crops.

New York State Troopers PBA President Thomas Mungeer says Mayor de Blasio's new laws are putting officers at risk. The president of a union representing New York State troopers said Friday that New York City's restrictions on police officers are setting the men and women on the force up for failure. "By raising the bar and almost making it impossible for my members to safely arrest, we've had enough. I want them out," New York State Troopers PBA President Thomas Mungeer told "Fox & Friends." "What has me alarmed is that troopers that are trained in certain tactics to arrest violent people can now be arrested for using those tactics within the five counties of New York City. Those tactics are still legal in the other 57 counties that make up New York state," Mungeer said.

Knowledge base completion (KBC) aims to automatically infer missing facts by exploiting information already present in a knowledge base (KB). A promising approach for KBC is to embed knowledge into latent spaces and make predictions from learned embeddings. However, existing embedding models are subject to at least one of the following limitations: (1) theoretical inexpressivity, (2) lack of support for prominent inference patterns (e.g., hierarchies), (3) lack of support for KBC over higher-arity relations, and (4) lack of support for incorporating logical rules. Here, we propose a spatio-translational embedding model, called BoxE, that simultaneously addresses all these limitations. BoxE embeds entities as points, and relations as a set of hyper-rectangles (or boxes), which spatially characterize basic logical properties. This seemingly simple abstraction yields a fully expressive model offering a natural encoding for many desired logical properties. BoxE can both capture and inject rules from rich classes of rule languages, going well beyond individual inference patterns. By design, BoxE naturally applies to higher-arity KBs. We conduct a detailed experimental analysis, and show that BoxE achieves state-of-the-art performance, both on benchmark knowledge graphs and on more general KBs, and we empirically show the power of integrating logical rules.