Task-oriented dialogue systems (TODS) are continuing to rise in popularity as various industries find ways to effectively harness their capabilities, saving both time and money. However, even state-of-the-art TODS are not yet reaching their full potential. TODS typically have a primary design focus on completing the task at hand, so the metric of task-resolution should take priority. Other conversational quality attributes that may point to the success, or otherwise, of the dialogue, may be ignored. This can cause interactions between human and dialogue system that leave the user dissatisfied or frustrated. This paper explores the literature on evaluative frameworks of dialogue systems and the role of conversational quality attributes in dialogue systems, looking at if, how, and where they are utilised, and examining their correlation with the performance of the dialogue system.

What AI and data analytics trends are taking the industry by storm this year? This comprehensive review highlights upcoming directions in AI to carefully watch and consider implementing in your personal work or organization.

How Powerful are Graph Neural Networks?

The purpose of this article is to explain the relationships (dependencies, interdependencies, interrelationships, benefits) between Data, AI, Cybersecurity, Cloud, IoT and Blockchain, and to understand their consistency. The link between all these fields leads us towards a new convergence which makes possible their interoperability, their security and their standardization. We thus provide a summary of the state of the art and we also present an inventory of standardization, methods and actors in the field, as well as the use cases identified in the literature. Why and how can we think of the "convergence" of Data, AI, Cloud, Blockchain, IoT and Cybersecurity? What types of relationships do these technologies have?

On November 30, a panel discussion was conducted with a focus for credit unions addressing "Assessing risk and optimizing growth for each member", hosted by Neuton.AI. This event brought together thought leaders from the industry who shared views on how credit unions can uncover new growth opportunities and mitigate risks by leveraging AI. Needless to say, the pandemic has caused a seismic shift in how we interact with customers such as how members are now expecting to consume services, their digital expectations which have in turn forced credit unions to rethink the way they interact and respond to member needs. This has subsequently led more and more credit unions to adopt a more data-driven mindset while leveraging innovative technologies such as artificial intelligence or machine learning. Beginning this journey, institutions are faced with a number of challenges such as where you begin, why data is important, what the possibilities are, and how I complete this journey when I may not have the resource or financial capital that is historically required to implement such services.

Developer tools that use a neural machine learning model to make predictions about previously unseen code.

In this piece, we start by covering the important topic of marketing attribution and how AI approaches improve upon existing techniques. Attribution is one of the key issues in marketing these days. If a customer is exposed to ads via multiple advertising channels and finally converts, how should we attribute this conversion? The answer to this question is crucial for optimal budget allocation during future advertising campaigns. One of the simplest approaches is to assign all credit to the last ad clicked before a conversion.

In recent years, algorithms and neural architectures based on the Weisfeiler-Leman algorithm, a well-known heuristic for the graph isomorphism problem, emerged as a powerful tool for machine learning with graphs and relational data. Here, we give a comprehensive overview of the algorithm's use in a machine learning setting, focusing on the supervised regime. We discuss the theoretical background, show how to use it for supervised graph- and node representation learning, discuss recent extensions, and outline the algorithm's connection to (permutation-)equivariant neural architectures. Moreover, we give an overview of current applications and future directions to stimulate further research.

The increasing number of product reviews posted online is a gold mine for designers to know better about the products they develop, by capturing the voice of customers, and to improve these products accordingly. In the meantime, product design and development have an essential role in creating a more sustainable future. With the recent advance of artificial intelligence techniques in the field of natural language processing, this research aims to develop an integrated machine learning solution to obtain sustainable design insights from online product reviews automatically. In this paper, the opportunities and challenges offered by existing frameworks - including Python libraries, packages, as well as state-of-the-art algorithms like BERT - are discussed, illustrated, and positioned along an ad hoc machine learning process. This contribution discusses the opportunities to reach and the challenges to address for building a machine learning pipeline, in order to get insights from product reviews to design more sustainable products, including the five following stages, from the identification of sustainability-related reviews to the interpretation of sustainable design leads: data collection, data formatting, model training, model evaluation, and model deployment. Examples of sustainable design insights that can be produced out of product review mining and processing are given. Finally, promising lines for future research in the field are provided, including case studies putting in parallel standard products with their sustainable alternatives, to compare the features valued by customers and to generate in fine relevant sustainable design leads.

Machine Learning (ML) has achieved great successes in recent decades, both in research and in practice. In Cyber-Physical Systems (CPS), ML can for example be used to optimize systems, to detect anomalies or to identify root causes of system failures. However, existing algorithms suffer from two major drawbacks: (i) They are hard to interpret by human experts. (ii) Transferring results from one systems to another (similar) system is often a challenge. Concept learning, or Representation Learning (RepL), is a solution to both of these drawbacks; mimicking the human solution approach to explain-ability and transfer-ability: By learning general concepts such as physical quantities or system states, the model becomes interpretable by humans. Furthermore concepts on this abstract level can normally be applied to a wide range of different systems. Modern ML methods are already widely used in CPS, but concept learning and transfer learning are hardly used so far. In this paper, we provide an overview of the current state of research regarding methods for learning physical concepts in time series data, which is the primary form of sensor data of CPS. We also analyze the most important methods from the current state of the art using the example of a three-tank system. Based on these concrete implementations1, we discuss the advantages and disadvantages of the methods and show for which purpose and under which conditions they can be used.