All the sessions from Transform 2021 are available on-demand now. "An underlying issue that most enterprise organizations struggle with is that their data is a disaster," noted Anthony Deighton, chief product officer at AI-powered data unification company Tamr. Deighton was moderating a panel at VentureBeat's Transform 2021 event today, which delved into practical and academic perspectives on how companies -- particularly financial institutions -- can use machine learning (ML) to improve the quality and reliability of their data. Deighton was joined by Tamr cofounder Michael Stonebraker, winner of the 2015 Turing award and a renowned computer scientist who specializes in database research; and Jonathan Holman, head of digital transformation at financial services company Santander U.K., a Tamr customer. So what is the problem that Tamr, ultimately, is setting out to solve?

Cases of the virus have risen sharply since the Delta variant emerged six weeks ago but, thanks to our fantastic vaccination programme, we are not seeing these translate into large numbers of people falling seriously ill or needing hospital treatment.

In this paper, we consider existential rules, an expressive formalism well suited to the representation of ontological knowledge and data-to-ontology mappings in the context of ontology-based data integration. The chase is a fundamental tool to do reasoning with existential rules as it computes all the facts entailed by the rules from a database instance. We introduce parallelisable sets of existential rules, for which the chase can be computed in a single breadth-first step from any instance. The question we investigate is the characterization of such rule sets. We show that parallelisable rule sets are exactly those rule sets both bounded for the chase and belonging to a novel class of rules, called pieceful. The pieceful class includes in particular frontier-guarded existential rules and (plain) datalog. We also give another characterization of parallelisable rule sets in terms of rule composition based on rewriting.

AI-enhanced cybersecurity is a must in 2021 and beyond. Clearly, the industry agrees -- you'll find an endless list of AI security platforms in the marketplace. What do vendors really mean when they use the term "artificial intelligence?" AI can be a fluid term, and sometimes mean different things to different people, and although marketing teams at cyber companies are using this ambiguity to their advantage, too often when it comes to the actual implementation and use of these platforms, the technology and promise falls short of AI in it's true scientific sense. Some artificial intelligence is and will be groundbreaking for the cybersecurity industry.

With the launch of VisaNet AI, we are seeing Visa in this particular case take matters into their own hands and develop products and services that are targeted at improving the services provided by their own clients, who are not able to keep up with the pace of technological advancement. For a long time we have known that data is the new oil, and that companies who wish to stay competitive in today's landscape, need to take aggressive steps into ensuring that their strategy, infrastructure and processes are data-driven. However, within the Payments industry we know that a lot of companies are still struggling to do so. VisaNet AI, which is a set of network services that helps deliver smarter authorization, clearing, and settlement for banks, merchants and consumers, is a great example of how companies should work on improving their core services. For years, we have worked with issuers, acquirers and merchants to drive through that the performance of their authorization is at the core of what payments should be.

As with everything else in Coupa, AI has been thoughtfully applied to areas where it adds real value. One such area is financial fraud. Detecting financial fraud can be challenging, costly, and time-consuming for organizations. However, with Coupa's robust AI-powered fraud detection solution, Spend Guard, we are able to help customers catch fraud and errors in-flight before they are even paid. Within Spend Guard, one of the many checks that our customers have found valuable is in detecting duplicate invoices.

Electronic health records (EHRs), digital collections of patient healthcare events and observations, are ubiquitous in medicine and critical to healthcare delivery, operations, and research. Despite this central role, EHRs are notoriously difficult to process automatically. Well over half of the information stored within EHRs is in the form of unstructured text (e.g. provider notes, operation reports) and remains largely untapped for secondary use. Recently, however, newer neural network and deep learning approaches to Natural Language Processing (NLP) have made considerable advances, outperforming traditional statistical and rule-based systems on a variety of tasks. In this survey paper, we summarize current neural NLP methods for EHR applications. We focus on a broad scope of tasks, namely, classification and prediction, word embeddings, extraction, generation, and other topics such as question answering, phenotyping, knowledge graphs, medical dialogue, multilinguality, interpretability, etc.

Boris Johnson says the success of the vaccine means England can begin to look beyond restrictions.

It is an important management task of metro agencies to formulate reasonable improvement schemes based on the result of service quality surveys. Considering scores, weights, and improvement feasibility of service quality attributes in a certain period, this paper integrates Decision Tree (DT) into Importance-Performance analysis (IPA) to build a DT-IPA model, which is used to determine the improvement priority of attributes, and to quantify the improvement degree. If-then rules extracted from the optimal decision tree and the improvement feasibility computed by analytic hierarchy process are two main items derived from the DT-IPA model. They are used to optimize the initial improvement priority of attributes determined by IPA and to quantify the degree of improvement of the adjusted attributes. Then, the overall service quality can reach a high score, realizing the operation goal. The effectiveness of the DT-IPA model was verified through an empirical study which was taken place in Changsha Metro, China. The proposed method can be a decision-making tool for metro agency managers to improve the quality of metro service.

In recent years, machine learning has begun automating decision making in fields as varied as college admissions, credit lending, and criminal sentencing. The socially sensitive nature of some of these applications together with increasing regulatory constraints has necessitated the need for algorithms that are both fair and interpretable. In this paper we consider the problem of building Boolean rule sets in disjunctive normal form (DNF), an interpretable model for binary classification, subject to fairness constraints. We formulate the problem as an integer program that maximizes classification accuracy with explicit constraints on two different measures of classification parity: equality of opportunity and equalized odds. Column generation framework, with a novel formulation, is used to efficiently search over exponentially many possible rules. When combined with faster heuristics, our method can deal with large data-sets. Compared to other fair and interpretable classifiers, our method is able to find rule sets that meet stricter notions of fairness with a modest trade-off in accuracy.