Previously I have written about the reality and potential of ongoing efforts to integrate blockchain with the internet of things (IoT). Now I am going to look at how encrypted, distributed ledgers could unlock new frontiers for another cutting-edge technology: artificial intelligence (AI). AI, as the term is most often used today is, simply put, the theory and practice of building machines capable of performing tasks that seem to require intelligence. Currently, cutting-edge technologies striving to make this a reality include machine learning, artificial neural networks and deep learning. Meanwhile, blockchain is essentially a new filing system for digital information, which stores data in an encrypted, distributed ledger format.

Previously I have written about the reality and potential of ongoing efforts to integrate blockchain with the internet of things (IoT). Now I am going to look at how encrypted, distributed ledgers could unlock new frontiers for another cutting-edge technology: artificial intelligence (AI). AI, as the term is most often used today is, simply put, the theory and practice of building machines capable of performing tasks that seem to require intelligence. Currently, cutting-edge technologies striving to make this a reality include machine learning, artificial neural networks and deep learning. Meanwhile, blockchain is essentially a new filing system for digital information, which stores data in an encrypted, distributed ledger format.

Previously I have written about the reality and potential of ongoing efforts to integrate blockchain with internet of things (IoT). Now I am going to look at how encrypted, distributed ledgers could unlock new frontiers for another cutting-edge technology: artificial intelligence (AI). AI, as the term is most often used today is, simply put, the theory and practice of building machines capable of performing tasks that seem to require intelligence. Currently, cutting-edge technologies striving to make this a reality include machine learning, artificial neural networks and deep learning. Meanwhile, blockchain is essentially a new filing system for digital information, which stores data in an encrypted, distributed ledger format.

Previously I have written about the reality and potential of ongoing efforts to integrate blockchain with internet of things (IoT). Now I am going to look at how encrypted, distributed ledgers could unlock new frontiers for another cutting-edge technology: artificial intelligence (AI). AI, as the term is most often used today is, simply put, the theory and practice of building machines capable of performing tasks that seem to require intelligence. Currently, cutting-edge technologies striving to make this a reality include machine learning, artificial neural networks and deep learning. Meanwhile, blockchain is essentially a new filing system for digital information, which stores data in an encrypted, distributed ledger format.

Businesses are naturally interested in detecting anomalies in their internal processes, because these can be indicators for fraud and inefficiencies. Within the domain of business intelligence, classic anomaly detection is not very frequently researched. In this paper, we propose a method, using autoencoders, for detecting and analyzing anomalies occurring in the execution of a business process. Our method does not rely on any prior knowledge about the process and can be trained on a noisy dataset already containing the anomalies. We demonstrate its effectiveness by evaluating it on 700 different datasets and testing its performance against three state-of-the-art anomaly detection methods. This paper is an extension of our previous work from 2016 [30]. Compared to the original publication we have further refined the approach in terms of performance and conducted an elaborate evaluation on more sophisticated datasets including real-life event logs from the Business Process Intelligence Challenges of 2012 and 2017. In our experiments our approach reached an F1 score of 0.87, whereas the best unaltered state-of-the-art approach reached an F1 score of 0.72. Furthermore, our approach can be used to analyze the detected anomalies in terms of which event within one execution of the process causes the anomaly.

The consumer packaged goods (CPG) and Retail industry are going through a period of significant change. Both retailers and manufacturers are struggling to find growth and improve profitability. One strategy is through consolidation - e.g., Kraft-Heinz, Keurig- Dr Pepper Snapple Group on the manufacturer side, as well as Safeway-Albertsons, Ahold-Delhaize, Walgreens-Rite Aid on the retailer side. The thinking here is that these mergers would lead to large operational efficiencies and focused growth strategies. Another important lever to drive growth is pricing and promotion.

Some of us understand how Artificial Intelligence will impact manufacturing or R&D, but what about other areas of the business? For example, what role will it play in Learning & Development? What do leaders in L&D and HR need to consider in developing, using and promoting the use of AI products to their internal customers? Can it be used effectively to teach management skills? How is bias eliminated in such a program?

The well-known Influence Maximization (IM) problem has been actively studied by researchers over the past decade, with emphasis on marketing and social networks. Existing research have obtained solutions to the IM problem by obtaining the influence spread and utilizing the property of submodularity. This paper is based on a novel approach to the IM problem geared towards optimizing clicks and consequently revenue within anOnline Social Network (OSN). Our approach diverts from existing approaches by adopting a novel, decision-making perspective through implementing Stochastic Dynamic Programming (SDP). Thus, we define a new problem Influence Maximization-Revenue Optimization (IM-RO) and propose SDP as a method in which this problem can be solved. The SDP method has lucrative gains for an advertiser in terms of optimizing clicks and generating revenue however, one drawback to the method is its associated "curse of dimensionality" particularly for problems involving a large state space. Thus, we introduce the Lawrence Degree Heuristic (LDH), Adaptive Hill-Climbing (AHC) and Multistage Particle Swarm Optimization (MPSO) heuristics as methods which are orders of magnitude faster than the SDP method whilst achieving near-optimal results. Through a comparative analysis on various synthetic and real-world networks we present the AHC and LDH as heuristics well suited to to the IM-RO problem in terms of their accuracy, running times and scalability under ideal model parameters. In this paper we present a compelling survey on the SDP method as a practical and lucrative method for spreading information and optimizing revenue within the context of OSNs.

The fourth industrial revolution stands out from its predecessors in a critical way: rather than making it easier for humans to use their surroundings more effectively for their own benefit, technology is displacing humans in the workplace. The question is who will benefit now. Automated or otherwise technology-enabled services can increase profit margins for companies, while representing for users cheaper, more convenient or more reliable options than those produced exclusively by humans. But, of course, this comes at a high cost for the humans who previously filled those roles. People all over the world have embraced ride-sharing and transport services such as Uber, to the detriment of traditional taxi drivers. In stock trading, 79 per cent of market transactions are now performed by software, according to Frank Zhang of the Yale School of Management, reflecting the hope that machines will be able to identify patterns more effectively than a human could – a hope that may have contributed to the recent stock market correction.

This paper offers a multi-disciplinary review of knowledge acquisition methods in human activity systems. The review captures the degree of involvement of various types of agencies in the knowledge acquisition process, and proposes a classification with three categories of methods: the human agent, the human-inspired agent, and the autonomous machine agent methods. In the first two categories, the acquisition of knowledge is seen as a cognitive task analysis exercise, while in the third category knowledge acquisition is treated as an autonomous knowledge-discovery endeavour. The motivation for this classification stems from the continuous change over time of the structure, meaning and purpose of human activity systems, which are seen as the factor that fuelled researchers' and practitioners' efforts in knowledge acquisition for more than a century. We show through this review that the KA field is increasingly active due to the higher and higher pace of change in human activity, and conclude by discussing the emergence of a fourth category of knowledge acquisition methods, which are based on red-teaming and co-evolution.