Repair-based semantics have been extensively studied as a means of obtaining meaningful answers to queries posed over inconsistent knowledge bases (KBs). While several works have considered how to exploit a priority relation between facts to select optimal repairs, the question of how to specify such preferences remains largely unaddressed. This motivates us to introduce a declarative rule-based framework for specifying and computing a priority relation between conflicting facts. As the expressed preferences may contain undesirable cycles, we consider the problem of determining when a set of preference rules always yields an acyclic relation, and we also explore a pragmatic approach that extracts an acyclic relation by applying various cycle removal techniques. Towards an end-to-end system for querying inconsistent KBs, we present a preliminary implementation and experimental evaluation of the framework, which employs answer set programming to evaluate the preference rules, apply the desired cycle resolution techniques to obtain a priority relation, and answer queries under prioritized-repair semantics.

Recently, Large Vision-Language Models (LVLMs) have demonstrated impressive capabilities in multi-modal context comprehension. However, they still suffer from hallucination problems referring to generating inconsistent outputs with the image content. To mitigate hallucinations, previous studies mainly focus on retraining LVLMs with custom datasets. Although effective, they inherently come with additional computational costs. In this paper, we propose a training-free framework, \textbf{MVP}, that aims to reduce hallucinations by making the most of the innate capabilities of the LVLMs via \textbf{M}ulti-\textbf{V}iew Multi-\textbf{P}ath Reasoning. Specifically, we first devise a multi-view information-seeking strategy to thoroughly perceive the comprehensive information in the image, which enriches the general global information captured by the original vision encoder in LVLMs. Furthermore, during the answer decoding, we observe that the occurrence of hallucinations has a strong correlation with the certainty of the answer tokens. Thus, we propose multi-path reasoning for each information view to quantify and aggregate the certainty scores for each potential answer among multiple decoding paths and finally decide the output answer. By fully grasping the information in the image and carefully considering the certainty of the potential answers when decoding, our MVP can effectively reduce hallucinations in LVLMs.The extensive experiments verify that our proposed MVP significantly mitigates the hallucination problem across four well-known LVLMs. The source code is available at: \url{https://github.com/GasolSun36/MVP}.

In conversational search, the user's real search intent for the current turn is dependent on the previous conversation history. It is challenging to determine a good search query from the whole conversation context. To avoid the expensive re-training of the query encoder, most existing methods try to learn a rewriting model to de-contextualize the current query by mimicking the manual query rewriting. However, manually rewritten queries are not always the best search queries. Training a rewriting model on them would limit the model's ability to produce good search queries. Another useful hint is the potential answer to the question. In this paper, we propose ConvGQR, a new framework to reformulate conversational queries based on generative pre-trained language models (PLMs), one for query rewriting and another for generating potential answers. By combining both, ConvGQR can produce better search queries. In addition, to relate query reformulation to retrieval performance, we propose a knowledge infusion mechanism to optimize both query reformulation and retrieval. Extensive experiments on four conversational search datasets demonstrate the effectiveness of ConvGQR.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. The first job for many artificial intelligence (AI) algorithms is to examine the data and find the best classification. An autonomous car, for example, may take an image of a street sign; the classification algorithm must interpret the street sign by reading any words and comparing it to a list of known shapes and sizes. A phone must listen to a sound and determine whether it is one of its wake-up commands ("Alexa," "Siri," "Hey Google"). The job of classification is sometimes the ultimate goal of an algorithm.

As we enter a new realm of how we work in a post-pandemic world, you may have noticed that a lot of people are taking new opportunities that may not have been available before. I'm specifically referring to how the advent of remote work has opened up new opportunities for positions where location may have been a barrier before. There's also the unfortunate coincidence that some people may now be seeking new opportunities due to job loss as a cause of the pandemic. Having been through a data science interview myself, I can definitely relate to just how nerve wracking the interview process can be! The data science interview process is generally a multi-phase approach, often consisting of one or more coding assessments, a "culture fit" interview, and of course, a technical question and answer time.

Artificial Intelligence (AI) approaches to problem-solving and decision-making are becoming more and more complex, leading to a decrease in the understandability of solutions. The European Union's new General Data Protection Regulation tries to tackle this problem by stipulating a "right to explanation" for decisions made by AI systems. One of the AI paradigms that may be affected by this new regulation is Answer Set Programming (ASP). Thanks to the emergence of efficient solvers, ASP has recently been used for problem-solving in a variety of domains, including medicine, cryptography, and biology. To ensure the successful application of ASP as a problem-solving paradigm in the future, explanations of ASP solutions are crucial. In this survey, we give an overview of approaches that provide an answer to the question of why an answer set is a solution to a given problem, notably off-line justifications, causal graphs, argumentative explanations and why-not provenance, and highlight their similarities and differences. Moreover, we review methods explaining why a set of literals is not an answer set or why no solution exists at all.

But the condition is so rare that it's still far from fully understood. Though it's undeniably horrific for those experiencing it, Cotard's Syndrome presents a fascinating conundrum for those studying the disorder. The condition's central contradiction -- how can someone articulate the thought that they don't exist? A 2013 case study of a Cotard's sufferer showed low activity in the brain network associated with awareness of the body. It's only one example (as with much of Cotard's Syndrome research, because the condition is so rare), but unpacking how the brains of those with the syndrome work offers hints as to how normally-functioning brains develop a sense of existence.

If you're going down to the racetrack, you might want to have an AI by your side. An artificial intelligence program developed by Unanimous A.I. successfully predicted the Superfecta at the 142nd Kentucky Derby last Saturday, turning a 20 bet into nearly 11,000. Using'Swarm Intelligence,' the AI was able to correctly choose the winning horse, Nyquist – along with the second, third, and fourth finishers. Unanimous AI's platform works by using the knowledge a group of people online, all logged into an the same interface where they can answer a series of questions. This animation shows how UNU's swarm intelligence makes its predictions Unanimous AI's platform works by using the knowledge a group of people online, all logged into an the same interface where they can answer a series of questions.

The “Cognitive Master Teacher” is a result of discussions with teachers, members of educational institutions, government bodies and other thought leaders in the United States who have helped us shape its the requirements. It is conceived as a cloud-based and mobile-accessible personal agent that is readily available for teachers to use at anytime and assist them with various issues related to day-to-day teaching activities as well as professional development.