Managing rich content in a QnA Maker chatbot has always been a challenge, since the users had to edit raw markdown. Now QnA Maker enables your to add and edit rich content right in the portal, so what you see in the edit experience is what you see in the Bot response. Also introducing new access roles (Editor and Reader) which can be assigned to a QnA Maker service, to restrict allowed operations. The AI Show's Favorite links: Don't miss new episodes, subscribe to the AI Show https://aka.ms/aishowsubscribe

The common thread across all AI technologies is the ability to impart human-like decision-making capabilities into applications and systems. Artificial intelligence (AI) refers to the simulation of human intelligence in systems programmed to think like humans and mimic their actions. AI includes a broad range of technologies, including cognitive computing, deep learning, expert systems, machine learning, natural language processing, and IBM Watson. The common thread across these areas, and all of AI, for that matter, is the ability to impart human-like decision-making capabilities into applications and systems. Experts predict AI will be rapidly adopted because they believe it will be a disruptive technology across many industries.

This section focuses on "Expert System" in Artificial Intelligence. These Multiple Choice Questions (mcq) should be practiced to improve the AI skills required for various interviews (campus interviews, walk-in interviews, company interviews), placements, entrance exams and other competitive examinations. Explanation: Expert System introduced by the researchers at Stanford University, Computer Science Department. Explanation: Expanding is not Capabilities of Expert Systems. Explanation: The components of ES include: Knowledge Base, Inference Engine, User Interface.

ABSTRACT We describe an Object Oriented Model for building Expert Systems. This model and the detection of similarities allow to implement reasoning modes as induction, deduction and simulation. We specially focus on similarity and its use in induction. We propose original algorithms which deal with total and partial structural similitude of objects to facilitate knowledge acquisition. Keywords: Knowledge acquisition, object oriented model, structural similarity, induction. Colloc, J. & Boulanger, D. Automatic knowledge acquisition for object oriented expert systems AVIGNON'93, 13th International Conference Artificial Intelligence, Expert Systems, Natural Language, 1993, 99-108 (Preprint version) 1. INTRODUCTION This paper proposes an object oriented model for building expert systems. While this model enhances the knowledge modularity, it supports some other reasoning modes than traditional deduction. First, we present the characteristics of our object oriented model (COLL 89), then we highlight the features used to implement reasoning and allow knowledge acquisition.

While for classical logics, the motto ``Truth is invariant under the change of notation'' has been studied extensively, less attention has been paid to this aspect in defeasible logics. In this paper, we address equivalences and transformations among conditional knowledge bases that take renamings of the underlying signature into account. Extending previous proposals, we introduce the concepts of \emph{renaming normal form} and \emph{renaming antecedent normal form} for arbitrary knowledge bases and across different signatures. We present procedures to transform every knowledge base to corresponding, up to propositional normalization uniquely determined normal forms and study their properties. Using the obtained normal forms allows for systematically identifying equivalences among knowledge bases, for easier and more transparent comparisons, and for simplified descriptions of algorithms operating on knowledge bases by avoiding tedious, but uninteresting borderline cases.

These are the initial findings from Expert System's Artificial Intelligence platform, Clinical Research Navigator (CRN), which is collecting biomedical research information from official reports and studies published worldwide. Following the launch of its AI-based Clinical Research Navigator (CRN), which is focused on accelerating research on COVID-19, Expert System mined over 620,000 clinical trials, including more than 1,700 trials related to the virus that are taking place around the globe. Clinical landscape is changing rapidly in the context of the current pandemic situation. It is therefore critical to have a global coverage of the trial registries to serve clinical experts with appropriate and effective means to conduct their research on the disease. Expert System analyzed data collected with its Artificial Intelligence CRN platform to gain some insight on key trends correlated to official reports and studies published worldwide.

In many applications labeled data is not readily available, and needs to be collected via painstaking human supervision. We propose a rule-exemplar method for collecting human supervision to combine the efficiency of rules with the quality of instance labels. The supervision is coupled such that it is both natural for humans and synergistic for learning. We propose a training algorithm that jointly denoises rules via latent coverage variables, and trains the model through a soft implication loss over the coverage and label variables. The denoised rules and trained model are used jointly for inference. Empirical evaluation on five different tasks shows that (1) our algorithm is more accurate than several existing methods of learning from a mix of clean and noisy supervision, and (2) the coupled rule-exemplar supervision is effective in denoising rules. With the ever-increasing reach of machine learning, a common hurdle to new adoptions is the lack of labeled data and the painstaking process involved in collecting human supervision. Over the years, several strategies have evolved.

Robots are still limited to controlled conditions, that the robot designer knows with enough details to endow the robot with the appropriate models or behaviors. Learning algorithms add some flexibility with the ability to discover the appropriate behavior given either some demonstrations or a reward to guide its exploration with a reinforcement learning algorithm. Reinforcement learning algorithms rely on the definition of state and action spaces that define reachable behaviors. Their adaptation capability critically depends on the representations of these spaces: small and discrete spaces result in fast learning while large and continuous spaces are challenging and either require a long training period or prevent the robot from converging to an appropriate behavior. Beside the operational cycle of policy execution and the learning cycle, which works at a slower time scale to acquire new policies, we introduce the redescription cycle, a third cycle working at an even slower time scale to generate or adapt the required representations to the robot, its environment and the task. We introduce the challenges raised by this cycle and we present DREAM (Deferred Restructuring of Experience in Autonomous Machines), a developmental cognitive architecture to bootstrap this redescription process stage by stage, build new state representations with appropriate motivations, and transfer the acquired knowledge across domains or tasks or even across robots. We describe results obtained so far with this approach and end up with a discussion of the questions it raises in Neuroscience.

This paper describes implementation details for a 3-level cognitive model, described in the paper series. The whole architecture is now modular, with different levels using different types of information. The ensemble-hierarchy relationship is maintained and placed in the bottom optimising and middle aggregating levels, to store memory objects and their relations. The top-level cognitive layer has been re-designed to model the Cognitive Process Language (CPL) of an earlier paper, by refactoring it into a network structure with a light scheduler. The cortex brain region is thought to be hierarchical - clustering from simple to more complex features. The refactored network might therefore challenge conventional thinking on that brain region. It is also argued that the function and structure in particular, of the new top level, is similar to the psychology theory of chunking. The model is still only a framework and does not have enough information for real intelligence. But a framework is now implemented over the whole design and so can give a more complete picture about the potential for results.