Artificial intelligence (AI) is hot. Billions of dollars in venture capital have been invested in AI firms, including firms that focus on solving supply chain problems. Machine learning (ML), a subset of AI, is particularly hot. Interestingly, while AI is not a new technology in supply chain management, so much more data is becoming available for analysis, that we're seeing a new focus on using these techniques to improve supply chain applications, including warehouse technologies. Any device that perceives its environment and takes actions that maximize its chance of success toward some goal is using artificial intelligence in some manner.

We'd like to offer you a complimentary copy of Gartner's Market Guide for Data Preparation. Tamr, the leader in enterprise data unification, has again been recognized as a Representative Vendor in Gartner's Market Guide for Data Preparation. "Data and analytics leaders are struggling due to their over-reliance on IT-centric tools for finding, cataloging and transforming relevant data and making it accessible to the growing number of distributed users in the enterprise both inside and outside of centralized data and analytics teams. Due to this, organizations report that they spend more than 60% of their time in data preparation, leaving little time for actual analysis." Our view of the underlying cause of the problem is that the deterministic, rules-based approaches inherent in traditional approaches to data integration can't keep pace with the number of sources that must be unified to generate big breakthroughs.

The influence of Artificial Intelligence (AI) in smart manufacturing is growing rapidly. Artificial Intelligence, according to the ARC Advisory Group, applies to any device that perceives its environment and takes actions that maximize its chance of success toward some goal. This includes a vast range of technologies, such as traditional logic and rules-based systems, that enable computers to solve problems in ways that at least superficially resemble thinking. According to a recent Accenture Artificial intelligence (AI) research report, corporate profits will increase by an average of 38% by 2035 in large part thanks to a more advanced deployment of Artificial Intelligence into financial, IT and manufacturing applications. But at this early stage of AI implementation, is it still not clear how it will be deployed across many possible use cases.

The recent years have witnessed the rise of accurate but obscure decision systems which hide the logic of their internal decision processes to the users. The lack of explanations for the decisions of black box systems is a key ethical issue, and a limitation to the adoption of machine learning components in socially sensitive and safety-critical contexts. In this paper we focus on the problem of black box outcome explanation, i.e., explaining the reasons of the decision taken on a specific instance. We propose LORE, an agnostic method able to provide interpretable and faithful explanations. LORE first leans a local interpretable predictor on a synthetic neighborhood generated by a genetic algorithm. Then it derives from the logic of the local interpretable predictor a meaningful explanation consisting of: a decision rule, which explains the reasons of the decision; and a set of counterfactual rules, suggesting the changes in the instance's features that lead to a different outcome. Wide experiments show that LORE outperforms existing methods and baselines both in the quality of explanations and in the accuracy in mimicking the black box.

I recently read an article (paywall) in the WSJ about Paul Allen's Vulcan initiative to curb illegal fishing. It's insightful and sheds light on Big Data techniques to address societal problems. After thinking on the story, it struck me that it could be used as a pedagogical tool to synthesize data science with domain knowledge. To me, this stands as the biggest limitation of what I refer to as'data science thinking'– letting technical skills drive the analysis, only later incorporating domain understanding. This post somewhat reads like a case note from business school and the idea is to get data scientists, product managers and engineers talking earlier on in the process.

This paper considers the learning of Boolean rules in either disjunctive normal form (DNF, OR-of-ANDs, equivalent to decision rule sets) or conjunctive normal form (CNF, AND-of-ORs) as an interpretable model for classification. An integer program is formulated to optimally trade classification accuracy for rule simplicity. Column generation (CG) is used to efficiently search over an exponential number of candidate clauses (conjunctions or disjunctions) without the need for heuristic rule mining. This approach also bounds the gap between the selected rule set and the best possible rule set on the training data. To handle large datasets, we propose an approximate CG algorithm using randomization. Compared to three recently proposed alternatives, the CG algorithm dominates the accuracy-simplicity trade-off in 7 out of 15 datasets. When maximized for accuracy, CG is competitive with rule learners designed for this purpose, sometimes finding significantly simpler solutions that are no less accurate.

Unlike rules-based systems, which are fairly easy for fraudsters to test and circumvent, machine learning adapts to changing behaviors in a population through automated model building. With every iteration, the algorithms get smarter and more accurately find activities that represent risk to the firm. It's easy to see the value of machine learning for keeping pace with evolving fraud tactics. Learn 10 proven ways machine learning can boost the efficiency and effectiveness of fraud and financial crimes teams – from data collection to detection to investigation and reporting.

I am by no means a particularly good example of study habits, but generally I tend to read what I need and go from there... Basically this in practice often means starting somewhere relevant to whatever work/assignment/project I'm trying to do, and then going backwards building a recursive stack of readings that seem important to understanding the previous thing until I reach a point where I am familiar with the material already. Then I work through the stack until I'm back to wherever I started. Essentially this is the backward chaining algorithm. I also, if I need to learn a lot from a book for some reason (i.e. a course) or have no particular goal in mind but find my self with a text that piques my interest, then I tend to skim from cover to cover everything that actually attracts my attention, occasionally flipping back to something that I realize is important for understanding later stuff. If it seems especially critical and I can't understand it, then I'll look through exercises and maybe do them if it seems worthwhile.

What is artificial intelligence, machine learning, and deep learning mean in general? When is a rule-based approach the right solution and when do you need machine learning? What does machine learning mean for time-series data? What is the difference between supervised and unsupervised learning in this area? Thanks to Devoxx for giving us permission to post this talk.

The curse of dimensionality in the realm of association rules is twofold. Firstly, we have the well known exponential increase in computational complexity with increasing item set size. Secondly, there is a \emph{related curse} concerned with the distribution of (spare) data itself in high dimension. The former problem is often coped with by projection, i.e., feature selection, whereas the best known strategy for the latter is avoidance. This work summarizes the first attempt to provide a computationally feasible method for measuring the extent of dimension curse present in a data set with respect to a particular class machine of learning procedures. This recent development enables the application of various other methods from geometric analysis to be investigated and applied in machine learning procedures in the presence of high dimension.