Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Neural networks trained on datasets such as ImageNet have led to major advances in visual object classification. One obstacle that prevents networks from reasoning more deeply about complex scenes and situations, and from integrating visual knowledge with natural language, like humans do, is their lack of common sense knowledge about the physical world. Videos, unlike still images, contain a wealth of detailed information about the physical world. However, most labelled video datasets represent high-level concepts rather than detailed physical aspects about actions and scenes. In this work, we describe our ongoing collection of the “something-something” database of video prediction tasks whose solutions require a common sense understanding of the depicted situation. The database currently contains more than 100,000 videos across 174 classes, which are defined as caption-templates. We also describe the challenges in crowd-sourcing this data at scale.

The “Something Something” Video Database for Learning and Evaluating Visual Common Sense

Deep learning has triggered the current rise of artificial intelligence and is the workhorse of today’s machine intelligence. Numerous success stories have rapidly spread all over science, industry and society, but its limitations have only recently come into focus. In this Perspective we seek to distil how many of deep learning’s failures can be seen as different symptoms of the same underlying problem: shortcut learning. Shortcuts are decision rules that perform well on standard benchmarks but fail to transfer to more challenging testing conditions, such as real-world scenarios. Related issues are known in comparative psychology, education and linguistics, suggesting that shortcut learning may be a common characteristic of learning systems, biological and artificial alike. Based on these observations, we develop a set of recommendations for model interpretation and benchmarking, highlighting recent advances in machine learning to improve robustness and transferability from the lab to real-world applications. Deep learning has resulted in impressive achievements, but under what circumstances does it fail, and why? The authors propose that its failures are a consequence of shortcut learning, a common characteristic across biological and artificial systems in which strategies that appear to have solved a problem fail unexpectedly under different circumstances.

Shortcut learning in deep neural networks

大規模要約資源としてのNew York Times Annotated Corpus

Recent years have witnessed a great deal of attention to the emergence of nationalism in late-eighteenth-century Europe, and critics such as Seamus Deane have drawn particular attention to the way in which the new nationalist narratives tended to displace the older cosmopolitan narrative of universal subjects and universal reason. As Deane points out, Enlightenment forms of narrative (like the philosophical tale) were threatened by a "newly assertive nationalism, predicated on notions of national character."'1 The kind of national thinking-and narrative-that he has in mind is represented by a figure like Edmund Burke and his well-known argument anchoring national identity in attachment to one's "little platoon."2 But the same Anglo-Irish milieu that produced Burke also produced Maria Edgeworth, who offered a rather different reading of national identity in the same period. Her writing on Ireland, especially her early Irish tales, offers an important rearticulation of Burkean local attachment and philosophical cosmopolitanism to produce an understanding of the nation as neither tightly bordered (like nations based on historical premises such as blood or inheritance) nor borderless (like those based on rational notions of universal inclusion). This effort to rethink nationness makes Edgeworth more than the colonial writer who figures in current criticism.3 Having herself been both immersed in Continental Enlightenment thought and personally affected by the nationalist upsurge of the 1798 Rebellion, she used herwriting to reconsider the meaning of the denomination "AngloIrish."4 And through her interrogation she reinterpreted both cosmopolitan and national definitions of belonging so as to reconstitute "Anglo-Irish" less as a category than as an ongoing mediation between borders.

of National Identity

User beliefs about algorithmic systems are constantly co-produced through user interaction and the complex socio-technical systems that generate recommendations. Identifying these beliefs is crucial because they influence how users interact with recommendation algorithms. With no prior work on user beliefs of algorithmic video recommendations, practitioners lack relevant knowledge to improve the user experience of such systems. To address this problem, we conducted semi-structured interviews with middle-aged YouTube video consumers to analyze their user beliefs about the video recommendation system. Our analysis revealed different factors that users believe influence their recommendations. Based on these factors, we identified four groups of user beliefs: Previous Actions, Social Media, Recommender System, and Company Policy. Additionally, we propose a framework to distinguish the four main actors that users believe influence their video recommendations: the current user, other users, the algorithm, and the organization. This framework provides a new lens to explore design suggestions based on the agency of these four actors. It also exposes a novel aspect previously unexplored: the effect of corporate decisions on the interaction with algorithmic recommendations. While we found that users are aware of the existence of the recommendation system on YouTube, we show that their understanding of this system is limited.

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Middle-Aged Video Consumers' Beliefs About Algorithmic Recommendations on YouTube

This paper explores student’s daily activity in a virtual learning environment in the anonymized Open University Learning Analytics Dataset (OULAD). We show that the daily activity of students can be used to predict their success, i.e. whether they pass or fail a course, with high accuracy. This is important since daily activity can be easily obtained and anonymized. To support this, we show that the binary information whether a student was active on a given day has similar predictive power as a combination of the exact number of clicks on the given day and sensitive private data like gender, disability, and highest educational level. We further show that the anonymized activity data can be used to group students. We identify different student types based on their daily binarized activity and outline how educators and system developers can utilize this to address different learning types. Our primary stakeholders are designers and developers of learning analytics systems as well as those who commission such systems. We discuss the privacy and design implications of our findings for data mining in educational contexts against the background of the principle of data minimization and the General Data Protection Regulation (GDPR) of the European Union.

Student Success Prediction and the Trade-Off between Big Data and Data Minimization

This paper investigates trust in news on a social media platform. The paper is motivated by the finding that social media is the primary news source for a large group of people, especially young adults. Considering the challenges posed by online misinformation and fake news, an understanding of how users quantify trust in news and what factors influence this trust is needed. In a study with 108 participants, German high-school students provided trust ratings for online news including quality media and fake news. The study shows that users can quantify their trust in news items and that these trust ratings correspond to rankings of the sources by experts. The paper finds that psychometric scales that measure interpersonal trust are predictive of a user's mean trust rating across different news items. We show how this can be used to provide interventions for those prone to false trust and false distrust.

Trust in news on social media

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This paper describes a technique to compare large text sources using word vector representations (word2vec) and dimensionality reduction (t-SNE) and how it can be implemented using Python. The technique provides a bird's-eye view of text sources, e.g. text summaries and their source material, and enables users to explore text sources like a geographical map. Word vector representations capture many linguistic properties such as gender, tense, plurality and even semantic concepts like "capital city of". Using dimensionality reduction, a 2D map can be computed where semantically similar words are close to each other. The technique uses the word2vec model from the gensim Python library and t-SNE from scikit-learn.

Hendrik Heuer

Papers

Middle-Aged Video Consumers' Beliefs About Algorithmic Recommendations on YouTube

Student Success Prediction and the Trade-Off between Big Data and Data Minimization

Trust in news on social media

Middle-Aged Video Consumers' Beliefs About Algorithmic Recommendations on YouTube

Text comparison using word vector representations and dimensionality reduction.