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As the impact of the COVID‐19 pandemic grew in 2020, uncertainty surrounding its origins and nature led to widespread conspiracy‐related theories (CRT). Use of technological platforms enabled the rapid and exponential dissemination of COVID‐19 CRT. This study applies social contagion theory to examine how Google Autocomplete (GA) propagates and perpetuates these CRT. An in‐house software program, Autocomplete Search Logging Tool (ASLT) captured a snapshot of GA COVID‐19 related searches early in the pandemic (from March to May 2020) across 76 randomly‐selected countries to gain insight into search behaviors around the world. Analysis identified 15 keywords relating to COVID‐19 CRT predictions and demonstrate how searches across different countries received varying degrees of GA predictions. When grouped with similar keywords, two major categories were identified “Man‐Made Biological Weapon” (42%, n = 2,111), and “Questioning Reality/Severity of COVID‐19” (44%, n = 2,224). This investigation is also among the first to apply social contagion theory to autocomplete applications and can be used in future research to explain and perhaps mitigate the spread of CRT.

In this data package, you will find thecheckpoints for the various T5 models we trained as well as an excel spreadsheet with all of our results including ones not reported in our paper. Specifically, at the topyou will find two top level folders, one for the models trained on short, medium, long, and mixedlengths and one for simple, medium, complex, and mixedcomplexities. These checkpoints arefinal checkpoint, whichis the checkpoint after all epochs have been finished. Thesefinal checkpoints arethe ones we used for our evaluation. In the excel spreadsheet, you will find three different sheets. The "All" sheet contains the overview of the experiments we ran the the results. Concretely, it contains the hyperparameter search performed, the final configuration chosen, experiments ran, and all their results. The "Lengths" sheet contains all the results for our models trained on various lengths organized by metric as well as including the deltas across models and datasets. Similarly, the "Complexities" sheet contains the results for for our models trained on various complexities.

In this data package, you will find thecheckpoints for the various ALiBi models we trained as well as an excel spreadsheet with all of our results including ones not reported in our paper. Specifically, at the topyou will find two top level folders, one for the models trained on short, medium, long, and mixedlengths and one for simple, medium, complex, and mixedcomplexities. These checkpoints arefinal checkpoint, whichis the checkpoint after all epochs have been finished. Thesefinal checkpoints arethe ones we used for our evaluation. In the excel spreadsheet, you will find three different sheets. The "All" sheet contains the overview of the experiments we ran the the results. Concretely, it contains the hyperparameter search performed, the final configuration chosen, experiments ran, and all their results. The "Lengths" sheet contains all the results for our models trained on various lengths organized by metric as well as including the deltas across models and datasets. Similarly, the "Complexities" sheet contains the results for for our models trained on various complexities.

In financial markets, investment options are relatively fixed but may occasionally change as new securities (tickers) are introduced to the markets. Theory on exploratory behavior suggests that when new tickers are introduced, the text completion decision aid (autocomplete) can facilitate diversion of market participants from their initially intended ticker to the newly introduced ticker. We analyze whether such diversions have an economic impact in terms of transaction loss. Consistent with our hypothesis, we provide evidence that upon ticker introduction, turnover of securities that are syntactically similar to a newly introduced security is significantly reduced by 3%–5% around the starting day of trade of the new security. Autocomplete appears to have a significant economic effect on market transactions.

Search engines have become an essential part of everyone's life, with Google being the most popular. Google Search provides the autocomplete feature for faster and easier search results, offering 10 top suggestions at a time, and these may influence how users view different social groups. Different scholars have explored online discourse to reveal stereotypes about certain groups. However, little or no attention has been paid to technological affordances to reveal broader gender biases and stereotypes in the Arab World. This study examines how Google autocomplete searches can reflect the Arabs' perspectives on gender. Google Egypt is selected since it is top-rated in the number of internet users. Google is queried by entering a combination of Arabic question words followed by the Arabic equivalents for men and women. One hundred and ninety questions were generated and categorized according to the qualities they referenced. The most common assumptions about men indicate that they are cheaters, liars, self-dominant, emotionally strong, and smarter than women. They are also stereotyped as being more likely to admire young women, prefer sons over daughters, and desire polygamy. Women, on the other hand, are stereotyped as plotting, materialistic, emotional, and sensitive. The study concludes that since such generalizations may entail exaggerations and are not evidently right all the time, one must be careful about adopting such stereotypes and making them part of each gender's views of the other. Bearing in mind the perpetuating function that technology may have of existing stereotypes and social norms, users and developers of Google alike must pay more attention to gender biases that algorithms may establish and disseminate.

Проаналізовано декілька різних підходів до автодоповнення програмного коду та різні методи їхнього покращення. Досліджено системи автодоповнення програмного коду у різних середовищах розробки (Visual Studio, Visual Studio Code, Eclipse, IntelliJ, тощо) та для різних мов програмування (C#, Visual Basic, JavaScript, Java, Python, тощо). Детально проаналізовано алгоритм IntelliSense та його використання в середовищах розробки програмного забезпечення (ПЗ). Встановлено підходи, за допомогою яких розробники систем автодоповнення програмного коду досягали кращих результатів у знаходженні найкращої пропозиції стосовно завершення коду. Оглянуто системи, що використовують сторонні бази даних для кращого аналізу програмного коду користувача та знаходження найбільш відповідної пропозиції автодоповнення на підставі контексту. Розглянуто різні ідеї для кращого алгоритму та комбінацію декількох у певних системах. Встановлено, що жодна з наявних систем повністю не зменшує фізичну роботу розробника. Запропоновано нову ідею для покращення досвіду користувача під час написання програмного коду. Проведено модифікацію алгоритму IntelliSense, внаслідок чого він підтримує використання фільтрів проекту та впорядковує пропозиції відповідно до популярності їх використання у заданих фільтрах. Спроектовано базу даних, у якій зберігаються створені користувачем ПЗ профілі, їхній статус (увімкнутий чи вимкнений) та кількість використань різних доповнень у цих профілях. Розроблено програмний засіб, що використовує згадану вище модифікацію алгоритму IntelliSense із використанням мови програмування TypeScript та технології розроблення розширень до середовища розробки ПЗ Visual Studio Code. Інтегровано цей програмний засіб у середовище розробки Visual Studio Code за допомогою підтримуваних нею функцій. Проаналізовано приріст у зменшенні часу обирання варіанта доповнення програмного коду від параметрів використання цього програмного засобу.

This workshop paper outlines a conceptual browser plugin that enables crowdworkers to store and later rapidly provide personal information frequently requested in crowdsourcing tasks. Personal data, including demographic data such as age and ethnicity, as well as responses to commonly used personality-related survey instruments, is often critical to collect in crowdsourcing tasks but results in a repetitive experience for crowdworkers. From a requesters perspective, this repetition can result in reduced data quality or the decision to abstain from collecting extensive information on the workers completing a given task. Moreover, given the extensive role of crowdworkers in labelling training data for artificial intelligence applications, ensuring awareness of the workers’ characteristics can help alleviate future biases. In this work, we present the motivation and design requirements for this (hypothetical) plugin and seek input from the community towards its future development.

Natural language processing (NLP) is the task of converting unstructured human language data into structured data that a machine can understand. While its applications are far and wide in healthcare, and are growing considerably every day, this chapter will focus on one particularly relevant application for healthcare professionals-reducing the burden of clinical documentation. More specifically, the chapter will discuss two studies (Gopinath et al., Fast, structured clinical documentation via contextual autocomplete. arXiv: 2007.15153, 2020; Greenbaum et al., Contextual autocomplete: a novel user interface using machine learning to improve ontology usage and structured data capture for presenting problems in the emergency department, 2017) that have implemented contextual autocompletion in electronic medical records and their promising results with regards to time saved for clinicians. The goals of this chapter are to introduce to the curious healthcare provider the basics of natural language processing, zoom into the use case of contextual autocomplete for electronic medical records, and provide a hands-on tutorial that introduces the basic NLP concepts required to build a model for predictive suggestions.

Agricultural research has been traditionally driven by linear approaches dictated by hypothesis-testing. With the advent of powerful data science capabilities, predictive, empirical approaches are possible that operate over large data pools to discern patterns. Such data pools need to contain well-described, machine-interpretable, and openly available data (represented by high-scoring Findable, Accessible, Interoperable, and Reusable—or FAIR—resources). CGIAR's Platform for Big Data in Agriculture has developed several solutions to help researchers generate open and FAIR outputs, determine their FAIRness in quantitative terms1, and to create high-value data products drawing on these outputs. By accelerating the speed and efficiency of research, these approaches facilitate innovation, allowing the agricultural sector to respond agilely to farmer challenges. In this paper, we describe the Agronomy Field Information Management System or AgroFIMS, a web-based, open-source tool that helps generate data that is “born FAIRer” by addressing data interoperability to enable aggregation and easier value derivation from data. Although license choice to determine accessibility is at the discretion of the user, AgroFIMS provides consistent and rich metadata helping users more easily comply with institutional, founder and publisher FAIR mandates. The tool enables the creation of fieldbooks through a user-friendly interface that allows the entry of metadata tied to the Dublin Core standard schema, and trial details via picklists or autocomplete that are based on semantic standards like the Agronomy Ontology (AgrO). Choices are organized by field operations or measurements of relevance to an agronomist, with specific terms drawn from ontologies. Once the user has stepped through required fields and desired modules to describe their trial management practices and measurement parameters, they can download the fieldbook to use as a standalone Excel-driven file, or employ via free Android-based KDSmart, Fieldbook, or ODK applications for digital data collection. Collected data can be imported back to AgroFIMS for statistical analysis and reports. Development plans for 2021 include new features such ability to clone fieldbooks and the creation of agronomic questionnaires. AgroFIMS will also allow archiving of FAIR data after collection and analysis from a database and to repository platforms for wider sharing.

Virtual assistants like Google Assistant and Siri often interface with external apps when they cannot directly perform a task. Currently, developers must manually expose the capabilities of their apps to virtual assistants, using App Actions on Android or Shortcuts on iOS. This paper presents savant, a system that automatically generates task shortcuts for virtual assistants by mapping user tasks to relevant UI screens in apps. For a given natural language task (e.g., “send money to Joe”), savant leverages text and semantic information contained within UIs to identify relevant screens, and intent modeling to parse and map entities (e.g., “Joe”) to required UI inputs. Therefore, savant allows virtual assistants to interface with apps and handle new tasks without requiring any developer effort. To evaluate savant, we performed a user study to identify common tasks users perform with virtual assistants. We then demonstrate that savant can find relevant app screens for those tasks and autocomplete the UI inputs.

When the Internet user keys a search term and clicks “enter”, a series of snippets, images and html links will appear typically running into several web pages. In the case of Autocomplete suggestions, the result appearing on the bar changes with each keystroke even before the user clicks “enter”. As a result, in the course of finding search results from the original search term, the user is constantly provided with suggestions of other search terms. The search results and Autocomplete suggestions may be defamatory of individuals and businesses by associating them with dishonest and improper activities or conduct. Should search engines be regarded as a publisher of such defamatory search results and/or Autocomplete suggestions? What is the appropriate legal approach for establishing search engine responsibility in such instances? The paper considers the above questions by reference to case precedents drawn primarily from common law jurisdictions and commentaries on the liability of search engines and other Internet intermediaries as well as policy rationales and considerations.

Autocomplete enhances teleoperation by recognizing operator intentions and automating motions when instructed. This has shown to improve performance and reduce operator load. Traditionally, recognizing operator intended motions is accomplished using pre-trained deep learning (DL) models. In this paper, we introduce personalization to the Autocomplete Teleoperation Framework. By customize the Autocom-plete DL model using transfer learning with partial feedback. This approach allows the model to adapt to the specific operator in a relatively short period. Several scenarios have been evaluated to balance the speed of learning with the accuracy of the model. Results demonstrate the effectiveness of the proposed method in adapting to the specific operator.

Teleoperation of robots can be challenging, especially for novice users with little to no experience at such tasks. The difficulty is largely due to the numerous degrees of freedom users must control and their limited perception bandwidth. To help mitigate these challenges, we propose in this paper a solution which relies on artificial intelligence to understand user intended motion and then on mixed reality to communicate the estimated trajectories to the users in an intuitive manner. User intended motion is estimated using a deep learning network trained on a dataset of motion primitives. During teleoperation, the estimated motions are augmented onto a first-person live video feed from the robot. Finally, if a suggested motion is accepted by the user, the robot is driven along that trajectory in an autonomous manner. We validate our proposed mixed reality teleoperation scheme with simulation experiments on a drone and demonstrate, through subjective and objective evaluation, its advantages over other teleoperation methods.

Billions of people turn to Google to find information, but there is no guarantee that what you find there is accurate. As awareness of fake news has risen in recent years, so has the pressure on Google to find ways of modifying its algorithms so that trustworthy content rises to the top. Fake news is not limited to Google’s main web search platform—deceptive and harmful content also play a role on other Google products such as Google Maps, Google News, and Google Images, and it also shows up on Google’s autocomplete tool that feeds into all these different products. In this chapter, I’ll look at the role fake news plays in all these contexts and what Google has done about it over the years. In doing so, I’ll take a somewhat more expansive view of the term “fake news” compared with previous chapters to include hateful racist stereotypes and bigoted misinformation.

All popular search engines implement HTTPS to protect the privacy of their users. Unfortunately, HTTPS encryption only covers Application layer headers and information will still leak through side-channels and other protocols used in a conversation between browser and server. This paper presents a novel eavesdropping approach called ESQABE, which combines these sources of information in order to determine what a subject is querying a search engine for in a real-life situation. To achieve this goal, packet length and timing information of the autocomplete functionality are used in combination with the home page contents of the search result links subsequently opened by the user. ESQABE is evaluated by automated tests using realistic search queries and based on real-life behavior. The technique is able to correctly predict the search query in 33% of the cases which is a significant improvement when compared to related work. In 41% of the cases, the correct query was included in the top 3 of most likely predictions. In most other cases no prediction could be made. To better protect the user, we contribute a browser extension that effectively hides the search query for the eavesdropper. The tool not only protects users but also visualizes what information is leaking to an eavesdropper.

The goal of text simplification (TS) is to transform difficult text into a version that is easier to understand and more broadly accessible to a wide variety of readers. In some domains, such as healthcare, fully automated approaches cannot be used since information must be accurately preserved. Instead, semi-automated approaches can be used that assist a human writer in simplifying text faster and at a higher quality. In this paper, we examine the application of autocomplete to text simplification in the medical domain. We introduce a new parallel medical data set consisting of aligned English Wikipedia with Simple English Wikipedia sentences and examine the application of pretrained neural language models (PNLMs) on this dataset. We compare four PNLMs(BERT, RoBERTa, XLNet, and GPT-2), and show how the additional context of the sentence to be simplified can be incorporated to achieve better results (6.17% absolute improvement over the best individual model). We also introduce an ensemble model that combines the four PNLMs and outperforms the best individual model by 2.1%, resulting in an overall word prediction accuracy of 64.52%., 9 pages, 3 figures, and 8 tables, Accpeted to COLING 2020

Keyframe-based sculpting provides unprecedented freedom to author animated organic models, which can be difficult to create with other methods such as simulation, scripting, and rigging. However, sculpting animated objects can require significant artistic skill and manual labor, even more so than sculpting static 3D shapes or drawing 2D animations, which are already quite challenging. We present a keyframe-based animated sculpting system with the capability to autocomplete user editing under a simple and intuitive brushing interface. Similar to current desktop sculpting and VR brushing tools, users can brush surface details and volume structures. Meanwhile, our system analyzes their workflows and predicts what they might do in the future, both spatially and temporally. Users can accept or ignore these suggestions and thus maintain full control. We propose the first interactive suggestive keyframe sculpting system, specifically for spatio-temporal repetitive tasks, including low-level spatial details and high-level brushing structures across multiple frames. Our key ideas include a deformation-based optimization framework to analyze recorded workflows and synthesize predictions, and a semi-causal global similarity measurement to support flexible brushing stroke sequences and complex shape changes. Our system supports a variety of shape and motion styles, including those difficult to achieve via existing animation systems, such as topological changes that cannot be accomplished via simple rig-based deformations and stylized physically-implausible motions that cannot be simulated. We evaluate our system via a pilot user study that demonstrates the effectiveness of our system.

While people primarily communicate with text in mobile chat applications, they are increasingly using visual elements such as images, emojis, and memes. Using such visual elements could help users communicate clearly and make chatting experience enjoyable. However, finding and inserting contextually appropriate images during the chat can be both tedious and distracting. We introduce MilliCat, a real-time image suggestion system that recommends images that match the chat content within a mobile chat application (i.e., autocomplete with images). MilliCat combines natural language processing (e.g., keyword extraction, dependency parsing) and mobile computing (e.g., resource and energy-efficiency) techniques to autonomously make image suggestions when users might want to use images. Through multiple user studies, we investigated the effectiveness of our design choices, the frequency and motivation of image usage by the participants, and the impact of MilliCat on mobile chat experiences. Our results indicate that MilliCat’s real-time image suggestion enables users to quickly and conveniently select and display images on mobile chat by significantly reducing the latency in the image selection process (3.19 × improvement) and consequently more frequent image usage (1.8 ×) than existing solutions. Our study participants reported that they used images more often with MilliCat as the images helped them convey information more effectively, emphasize their opinion, express emotions, and have fun chatting experience.

This is a formula sheet for "Coursera Natural Language Processing Specialization".Natural Language Processing (NLP) uses algorithms to understand and manipulate human language. This technology is one of the most broadly applied areas of machine learning. Use logistic regression, naïve Bayes, and word vectors to implement sentiment analysis, complete analogies & translate words. Use dynamic programming, hidden Markov models, and word embeddings to implement autocorrect, autocomplete & identify part-of-speech tags for words. Use recurrent neural networks, LSTMs, GRUs & Siamese networksfor sentiment analysis, text generation & named entity recognition. Use encoder-decoder, causal, & self-attention to machine translate complete sentences, summarize text, build chatbots & question-answering.

Software systems are continuously modified to implement new features, to fix bugs, and to improve quality attributes. Most of these activities are not atomic changes, but rather the result of several related changes affecting different parts of the code. For this reason, it may happen that developers omit some of the needed changes and, as a consequence, leave a task partially unfinished, introduce technical debt or, in the worst case scenario, inject bugs. Knowing the changes that are mistakenly omitted by developers can help in designing recommender systems able to automatically identify risky situations in which, for example, the developer is likely to be pushing an incomplete change to the software repository. We present a qualitative study investigating "quick remedy commits" performed by developers with the goal of implementing changes omitted in previous commits. With quick remedy commits we refer to commits that (i) quickly follow a commit performed by the same developer in the same repository, and (ii) aim at remedying issues introduced as the result of code changes omitted in the previous commit (e.g., fix references to code components that have been broken as a consequence of a rename refactoring). Through a manual analysis of 500 quick remedy commits, we define a taxonomy categorizing the types of changes that developers tend to omit. The defined taxonomy can guide the development of tools aimed at detecting omitted changes, and possibly autocomplete them.

Developers authoring HTML documents define elements following patterns which establish and reflect the visual structure of a document, such as making all images in a footer the same height by applying a class to each. To surface these patterns to developers and support developers in authoring consistent with these patterns, we propose a mixed human-AI technique for creating code patterns. Patterns are first learned from individual HTML documents through a decision tree, generating a representation which developers may view and edit. Code patterns are used to offer developers autocomplete suggestions, list examples, and flag violations. To evaluate our technique, we conducted a user study in which 24 participants wrote, edited, and corrected HTML documents. We found that our technique enabled developers to edit and correct documents more quickly and create, edit, and correct documents more successfully.

Controlling and manning robots from a remote location is difficult because of the limitations one faces in perception and available degrees of actuation. Although humans can become skilled teleoperators, the amount of training time required to acquire such skills is typically very high. In this paper, we propose a novel solution (named Autocomplete) to aid novice teleoperators in manning robots adroitly. At the input side, Autocomplete relies on machine learning to detect and categorize human inputs as one from a group of motion primitives. Once a desired motion is recognized, at the actuation side an automated command replaces the human input in performing the desired action. So far, Autocomplete can recognize and synthesize lines, arcs, full circles, 3-D helices, and sine trajectories. Autocomplete was tested in simulation on the teleoperation of an unmanned aerial vehicle, and results demonstrate the advantages of the proposed solution versus manual steering.

International audience; Aggregate elements are ubiquitous in natural and man-made objects. Interactively authoring these elements with varying anisotropy and deformability can require high artistic skills and manual labor. To reduce input workload and enhance output quality, we present an autocomplete system that can help users distribute and align such elements over different domains. Through a brushing interface, users can place and mix a few elements, and let our system automatically populate more elements for the remaining output. Furthermore, aggregate elements often require proper direction/scalar fields for proper arrangements, but fully specifying such fields across entire domains can be difficult or inconvenient for ordinary users. To address this usability challenge, we formulate "element fields" that can smoothly orient all the elements based on partial user specifications without requiring full input fields in any step.We validate our prototype system with a pilot user study and show applications in design, collage, and modeling.

An important question in interactive information retrieval (IIR) is: How can we support searchers with specific types of search tasks? We describe an auxiliary support tool referred to as the "Matrix''. The Matrix tool was designed to support searchers with comparative search tasks, which require comparing items along different dimensions. The Matrix was designed as a grid of rows and columns representing the items and dimensions related to a comparative task. The Matrix was integrated with a custom-built search interface, which allowed users to search for information and drag-and-drop relevant passages directly into cells in the Matrix. We investigate the following general question: Given a partially completed Matrix, can a system automatically populate empty cells in the Matrix with relevant passages? To this end, we conducted two crowdsourced studies in which participants were assigned comparative tasks and asked to use our system (integrated search interface + Matrix) to populate every cell in the Matrix. After gathering this data, we evaluated machine-learned models for ranking passages in response to an empty Matrix cell and partially completed Matrix. We address two research questions: (RQ1) What are useful types of features for this predictive task? and (RQ2) How does performance vary based on the level of Matrix completion? We view our research as a step towards designing support tools that: (1) help users organize information while searching and (2) can autocomplete search tasks by exploiting the task structure and a searcher's partial solution.

Digital sculpting is a popular means to create 3D models but remains a challenging task for many users. This can be alleviated by recent advances in data-driven and procedural modeling, albeit bounded by the underlying data and procedures. We propose a 3D sculpting system that assists users in freely creating models without predefined scope. With a brushing interface similar to common sculpting tools, our system silently records and analyzes users' workflows, and predicts what they might or should do in the future to reduce input labor or enhance output quality. Users can accept, ignore, or modify the suggestions and thus maintain full control and individual style. They can also explicitly select and clone past workflows over output model regions. Our key idea is to consider how a model is authored via dynamic workflows in addition to what it is shaped in static geometry, for more accurate analysis of user intentions and more general synthesis of shape structures. The workflows contain potential repetitions for analysis and synthesis, including user inputs (e.g. pen strokes on a pressure sensing tablet), model outputs (e.g. extrusions on an object surface), and camera viewpoints. We evaluate our method via user feedbacks and authored models., Comment: 10 pages

IntroductionThe evolution of our society depended on information from the very beginning. Different technologies developed in order to store, to retrieve, to transmit, or to process information described this evolution. However, in the November 1958 issue, Harvard Business Review published the article Management in the 1980's, by Harold J. Leavitt and Thomas L. Whisler. In that article, the authors had highlighted that "Over the last decade a new technology has begun to take hold in American business, one so new that its significance is still difficult to evaluate. While many aspects of this technology are uncertain, it seems clear that it will move into the managerial scene rapidly, with definite and far-reaching impact on managerial organization. The new technology does not yet have a single established name. We shall call it information technology." That article is available today online, at the web address: https://hbr.org/1958/11/management-in-the1980s. Nowadays, about information technology, Wikipedia, the free encyclopaedia, is warning us "Not to be confused with Informatics." Google search engine offers us autocomplete options for the keywords information technology as information technology definition, or information technology vs computer science or information technology and communications or information technology outsourcing. If the users edit the keywords information technology communication, others options available are networks, plan or definition, and if the search phrase is information technology communications, other options are manager job description, plan, or manager. Searching for Romanian keywords tehnologia informaţiei, the autocomplete options are "si a comunicarii", "si a comunicatiilor", "referat". When we start a new search using the names Leavitt and Whisler, one Google's autocomplete option is "leavitt whisler 19581'Using BizNar for searching "information technology," we get a search summary, a structured, or a visual result, and we can refine the search depending on topics, authors, publications, source, dates, document format or document type.Using WolframAlpha for searching "information technology," we get a structured result assuming "information technology" is a general topic, or we can get different results using the phrase as an occupation or a word instead.When Leavitt and Whisler named the new technology, they prognosticated that information technology should move the boundary between planning and performance upward, and much more of the work will be programed, i.e., covered by sets of operating rules governing the day-to-day decisions that are made. At the same time, the authors considered that a radical reorganization of middle-management levels should occur, with certain classes of middle-management jobs moving downward in status and compensation (because they will require less autonomy and skill), while other classes move upward into the top-management group [1]. The same authors highlight the idea that on a broader social scale one can conceive of large problems outside the firm that affect many institutions ancillary to industry. In this context, they ask how we educate people for routinized middle-management jobs, especially if the path from those jobs up to top management gets much rockier, and to what extent do schools start training new kinds of specialists?In 1964, [2] emphasized that in operational and practical fact, the medium (defined as any extension of ourselves) is the message. The author explained that the personal and social consequences of any medium result from the new scale that is included into our affairs by each extension of ourselves, or by any new technology. In addition, he gives two examples: the restructuring of human work and association which were shaped by the technique of fragmentation, which it was the essence of machine technology, while the essence of automation technology is the opposite, it is integral and decentralist in its patterning of human relationships. …