Human behaviour is complex and nuanced. Consider how an act as simple as purchasing a cup of coffee involves an intricate spatio-temporal sequence of actions and perception: instructions, clarifications, and feedback weave across language, touch, and visual communicative cues, with the precise timing of each providing yet more information to our interactive partners. If we ever hope to create artificial agents that can participate in similar interactions, we must develop effective ways to evaluate their behaviour in naturalistic settings with humans. One obvious approach to evaluating interactive agent behaviour is to leverage a human's judgement during the course of their interaction with an agent. However, this requires a high human cost, both in number of human participants required and in total number of human hours spent, and has no straightforward mechanism to control for human behavioural diversity. The latter problem in particular can result in highly variable metrics if human behaviour is too noisy, or imprecise metrics if human behaviour is not diverse enough. Human behavior is also non-stationary over time, as it can be subtly impacted by agent performance, causing drift. Thus, despite being a "gold standard", the opacity of the online human-agent evaluation setting makes any generated metrics difficult to interpret and communicate, and hence, difficult to optimize for. Researchers therefore typically rely on other methods of evaluation, such as validation performance of the agent's optimized objective (e.g.

Pathology is rapidly evolving as it begins to embrace artificial intelligence (AI) as an enabling technology. In addition to the basic concepts of machine learning, it is important to understand the overall framework in which digital pathology and computational pathology are creating tremendous opportunities for advancing 21st century diagnostics. From a discussion of machine learning modalities, it will be shown that both now and in the foreseeable future, the computer will not replace human cognition, creativity, and imagination. AI, however, will become a tireless and accurate pathologist's assistant.

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Artificial intelligence and Machine Learning are expressing incredible potential in various application fields; however, very few companies engaged in a 4.0 transition path can successfully implement these technologies in business processes. What needs to be done to make such applications profitable? Artificial Intelligence represents a set of studies and techniques, typical of information technology but with significant philosophical and social implications, which has as its purpose the realization of programs and technological systems capable of solving problems and carrying out tasks normally attributable to the mind and human capabilities. Given recent progress, it is possible to identify Artificial Intelligence as the discipline that deals with creating machines (hardware and software) capable of operating autonomously. The growing attention created in this discipline is motivated by the results that can be achieved thanks to the technological maturity achieved, both in the computational calculation and in the ability to analyze in real-time and in a short time of huge amounts of data in any form [Big Data Analytics].

The term artificial intelligence (AI) refers to computing systems that perform tasks normally considered within the realm of human decision making. These software-driven systems and intelligent agents incorporate advanced data analytics and Big Data applications. AI systems leverage this knowledge repository to make decisions and take actions that approximate cognitive functions, including learning and problem solving. AI, which was introduced as an area of science in the mid 1950s, has evolved rapidly in recent years. It has become a valuable and essential tool for orchestrating digital technologies and managing business operations.

Recent model-based Recommender Systems (RecSys) algorithms emphasize on the use of features, also called side information, in their design similar to algorithms in Machine Learning (ML). In contrast, some of the most popular and traditional algorithms for RecSys solely focus on a given user-item-rating relation without including side information. The goal of this case study is to provide a performance comparison and assessment of RecSys and ML algorithms when side information is included. We chose the Movielens-100K data set since it is a standard for comparing RecSys algorithms. We compared six different feature sets with varying quantities of features which were generated from the baseline data and evaluated on a total of 19 RecSys algorithms, baseline ML algorithms, Automated Machine Learning (AutoML) pipelines, and state-of-the-art RecSys algorithms that incorporate side information. The results show that additional features benefit all algorithms we evaluated. However, the correlation between feature quantity and performance is not monotonous for AutoML and RecSys. In these categories, an analysis of feature importance revealed that the quality of features matters more than quantity. Throughout our experiments, the average performance on the feature set with the lowest number of features is about 6% worse compared to that with the highest in terms of the Root Mean Squared Error. An interesting observation is that AutoML outperforms matrix factorization-based RecSys algorithms when additional features are used. Almost all algorithms that can include side information have higher performance when using the highest quantity of features. In the other cases, the performance difference is negligible (<1%). The results show a clear positive trend for the effect of feature quantity as well as the important effects of feature quality on the evaluated algorithms.

In task-oriented dialogue (ToD), a user holds a conversation with an artificial agent  with the aim of completing a concrete task. Although this technology represents one of  the central objectives of AI and has been the focus of ever more intense research and  development efforts, it is currently limited to a few narrow domains (e.g., food ordering,  ticket booking) and a handful of languages (e.g., English, Chinese). This work provides an  extensive overview of existing methods and resources in multilingual ToD as an entry point  to this exciting and emerging field. We find that the most critical factor preventing the  creation of truly multilingual ToD systems is the lack of datasets in most languages for  both training and evaluation. In fact, acquiring annotations or human feedback for each  component of modular systems or for data-hungry end-to-end systems is expensive and  tedious. Hence, state-of-the-art approaches to multilingual ToD mostly rely on (zero- or  few-shot) cross-lingual transfer from resource-rich languages (almost exclusively English),  either by means of (i) machine translation or (ii) multilingual representations. These  approaches are currently viable only for typologically similar languages and languages with  parallel / monolingual corpora available. On the other hand, their effectiveness beyond these  boundaries is doubtful or hard to assess due to the lack of linguistically diverse benchmarks  (especially for natural language generation and end-to-end evaluation). To overcome this  limitation, we draw parallels between components of the ToD pipeline and other NLP tasks,  which can inspire solutions for learning in low-resource scenarios. Finally, we list additional  challenges that multilinguality poses for related areas (such as speech, fluency in generated  text, and human-centred evaluation), and indicate future directions that hold promise to  further expand language coverage and dialogue capabilities of current ToD systems. 

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