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.

Analyzing encircling situation is the most crucial part of autonomous adaptation. Since there are many unknown and constantly changing factors in the real environment, momentary adjustment to the consistently alternating circumstances is highly required for addressing autonomy. To respond properly to changing environment, an utterly self-ruling vehicle ought to have the capacity to realize/comprehend its particular position and the surrounding environment. However, these vehicles extremely rely on human involvement to resolve entangled missions that cannot be precisely characterized in advance, which restricts their applications and accuracy. Reducing dependence on human supervision can be achieved by improving level of autonomy. Over the previous decades, autonomy and mission planning have been extensively researched on different structures and diverse conditions; nevertheless, aiming at robust mission planning in extreme conditions, here we provide exhaustive study of UVs autonomy as well as its related properties in internal and external situation awareness. In the following discussion, different difficulties in the scope of AUVs and UAVs will be discussed.

The Apperception Engine is an unsupervised learning system. Given a sequence of sensory inputs, it constructs a symbolic causal theory that both explains the sensory sequence and also satisfies a set of unity conditions. The unity conditions insist that the constituents of the theory - objects, properties, and laws - must be integrated into a coherent whole. Once a theory has been constructed, it can be applied to predict future sensor readings, retrodict earlier readings, or impute missing readings. In this paper, we evaluate the Apperception Engine in a diverse variety of domains, including cellular automata, rhythms and simple nursery tunes, multi-modal binding problems, occlusion tasks, and sequence induction intelligence tests. In each domain, we test our engine's ability to predict future sensor values, retrodict earlier sensor values, and impute missing sensory data. The engine performs well in all these domains, significantly outperforming neural net baselines and state of the art inductive logic programming systems. These results are significant because neural nets typically struggle to solve the binding problem (where information from different modalities must somehow be combined together into different aspects of one unified object) and fail to solve occlusion tasks (in which objects are sometimes visible and sometimes obscured from view). We note in particular that in the sequence induction intelligence tests, our system achieved human-level performance. This is notable because our system is not a bespoke system designed specifically to solve intelligence tests, but a general-purpose system that was designed to make sense of any sensory sequence.

The rule extraction literature contains the notion of a fidelity-accuracy dilemma: when building an interpretable model of a black box function, optimising for fidelity is likely to reduce performance on the underlying task, and vice versa. I reassert the relevance of this dilemma for the modern field of explainable artificial intelligence, and highlight how it is compounded when the black box is an agent interacting with a dynamic environment. I then discuss two independent research directions - building white box agents and interpreting black box agents - which are both coherent and worthy of attention, but must not be conflated by researchers embarking on projects in the domain of agent interpretability.