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Authors:
(1) Ankur Nath, Department of Computer Science and Engineering, Texas A&M University;
(2) Alan Kuhnle, Department of Computer Science and Engineering, Texas A&M University. Table of Links Abstract & Introduction Related work Evaluation for Max-Cut Evaluation for SAT Summary and Outlook, References Supplementary Materials Abstract In recent years, combining neural networks with local search heuristics has become popular in the field of combinatorial optimization. Despite its considerable computational demands, this approach has exhibited promising outcomes with minimal manual engineering. However, we have identified three critical limitations in the empirical evaluation of these integration attempts. Firstly, instances with moderate complexity and weak baselines pose a challenge in accurately evaluating the effectiveness of learning-based approaches. Secondly, the absence of an ablation study makes it difficult to quantify and attribute improvements accurately to the deep learning architecture. Lastly, the generalization of learned heuristics across diverse distributions remains underexplored. In this study, we conduct a comprehensive investigation into these identified limitations. Surprisingly, we demonstrate that a simple learned heuristic based on Tabu Search surpasses state-of-the-art (SOTA) learned heuristics in terms of performance and generalizability. Our findings challenge prevailing assumptions and open up exciting avenues for future research and innovation in combinatorial optimization. 1 INTRODUCTION Designing effective heuristics or approximation algorithms for NP-hard combinatorial optimization (CO) problems is a challenging task, often requiring domain knowledge and extensive trial-and-error. Thus, the idea of automating this demanding and tedious design process through machine learning to learn algorithms that exploit the inherent structure of a problem has gained significant interest among researchers (Bello et al., 2016; Khalil et al., 2017; Bengio et al., 2021; Dong et al., 2021). Particularly, a considerable portion of these works (Khalil et al., 2017; Barrett et al., 2020; Yolcu and P´oczos, 2019) have concentrated on employing Graph Neural Networks (GNNs) for CO problems. Despite the computational demands, these GNN-based approaches have demonstrated competitive performance compared to SOTA heuristics tailored to specific problems. While these potential advancements offer great promise, some concerns remain. The superior performance of the learned heuristics can be attributed to the selection of specific instances and baselines. Specifically, if the baselines are weak, the learned heuristics can easily outperform them. Without hard instances and proper baseline selection, the learned heuristics can easily show comparable performance with the SOTA heuristics, and this can lead to an overestimation of the actual capabilities of the learned heuristics. Moreover, comparisons with SOTA heuristics are occasionally left out due to scalability challenges with deep learning architectures. A subset of learning-based approaches (Khalil et al., 2017; Yolcu and P´oczos, 2019; Barrett et al., 2020, 2022) incorporate the functionality or behavior of traditional heuristics, potentially offering improved or enhanced performance by integrating heuristics principles with machine learning components. If a comprehensive comparison with the integrated heuristics and an ablation study of the deep learning architecture are lacking, it becomes challenging to determine the specific contribution of the deep learning architecture. Consequently, if the integrated heuristics are robust, the learned heuristics can seamlessly outperform the baseline heuristics, while the deep learning architecture plays a little role. Another great achievement of learned heuristics (Khalil et al., 2017; Barrett et al., 2020; Toenshof et al., 2019), initially trained on smaller and specific instances from a particular distribution, exhibits impressive performance when tested on larger instances from different distributions. This achievement stands as a significant accomplishment, aligning with the core objective of employing learning-based approaches: to mitigate the necessity for extensive customization and domain-specific knowledge often required by heuristics. Although classical heuristics with hyperparameters may also encounter challenges if they are fine-tuned for a specific distribution, they may also generalize across different distributions. The primary inquiry revolves around whether the learned heuristics indeed demonstrate superior generalizability compared to classical heuristics. A thorough and insightful comparison of learned heuristics against classical heuristics provides valuable insights into the generalizability of learned heuristics. Learned heuristics often lack theoretical guarantees, making empirical evaluation the sole method to comprehend the strengths and limitations of proposed methodologies. We believe that several fundamental, yet pivotal questions remain unexplored in the empirical evaluation of these works. While these inquiries are pertinent to all types of learned heuristics, we ask and answer them by analyzing highly cited and recent peer-reviewed publications focused on learning local search heuristics. Our goal is not to provide exhaustive benchmarks for the CO problems discussed in our work but rather to assist future researchers in evaluating their research. Concretely, we ask and answer the following questions: 1. Can learned heuristics be over-parameterized? Absolutely. Replacing the GNN in ECO-DQN with linear regression and utilizing a subset of features from ECO-DQN (Barrett et al., 2020) that links to Tabu Search (Glover, 1990), we introduce a pruned version of ECO-DQN termed SoftTabu. Our study demonstrates that SoftTabu showcases superior performance and generalizability in comparison to ECO-DQN for the NPhard Maximum-Cut (Max-Cut) problem. 2. Can baseline bias be attributed to the superior performance of learned heuristics? Yes, we demonstrate that SoftTabu, a vanilla learned heuristic, can outperform S2V-DQN (Khalil et al., 2017) for the Max-Cut problem and GNNSAT (Yolcu and P´oczos, 2019) for Boolean Satisfiability (SAT). *3. Can learned heuristics demonstrate superior generalizability owing to instance selection bias?*Yes, we demonstrate that ECO-DQN shows poor generalization on harder instances and easily gets trapped in the search space. This paper is available on arxiv under CC 4.0 DEED license. Authors: (1) Ankur Nath, Department of Computer Science and Engineering, Texas A&M University; (2) Alan Kuhnle, Department of Computer Science and Engineering, Texas A&M University. Authors: Authors: (1) Ankur Nath, Department of Computer Science and Engineering, Texas A&M University; (2) Alan Kuhnle, Department of Computer Science and Engineering, Texas A&M University. Table of Links Abstract & Introduction Abstract & Introduction Related work Related work Evaluation for Max-Cut Evaluation for Max-Cut Evaluation for SAT Evaluation for SAT Summary and Outlook, References Summary and Outlook, References Supplementary Materials Supplementary Materials Abstract In recent years, combining neural networks with local search heuristics has become popular in the field of combinatorial optimization. Despite its considerable computational demands, this approach has exhibited promising outcomes with minimal manual engineering. However, we have identified three critical limitations in the empirical evaluation of these integration attempts. Firstly, instances with moderate complexity and weak baselines pose a challenge in accurately evaluating the effectiveness of learning-based approaches. Secondly, the absence of an ablation study makes it difficult to quantify and attribute improvements accurately to the deep learning architecture. Lastly, the generalization of learned heuristics across diverse distributions remains underexplored. In this study, we conduct a comprehensive investigation into these identified limitations. Surprisingly, we demonstrate that a simple learned heuristic based on Tabu Search surpasses state-of-the-art (SOTA) learned heuristics in terms of performance and generalizability. Our findings challenge prevailing assumptions and open up exciting avenues for future research and innovation in combinatorial optimization. 1 INTRODUCTION Designing effective heuristics or approximation algorithms for NP-hard combinatorial optimization (CO) problems is a challenging task, often requiring domain knowledge and extensive trial-and-error. Thus, the idea of automating this demanding and tedious design process through machine learning to learn algorithms that exploit the inherent structure of a problem has gained significant interest among researchers (Bello et al., 2016; Khalil et al., 2017; Bengio et al., 2021; Dong et al., 2021). Particularly, a considerable portion of these works (Khalil et al., 2017; Barrett et al., 2020; Yolcu and P´oczos, 2019) have concentrated on employing Graph Neural Networks (GNNs) for CO problems. Despite the computational demands, these GNN-based approaches have demonstrated competitive performance compared to SOTA heuristics tailored to specific problems. While these potential advancements offer great promise, some concerns remain. The superior performance of the learned heuristics can be attributed to the selection of specific instances and baselines. Specifically, if the baselines are weak, the learned heuristics can easily outperform them. Without hard instances and proper baseline selection, the learned heuristics can easily show comparable performance with the SOTA heuristics, and this can lead to an overestimation of the actual capabilities of the learned heuristics. Moreover, comparisons with SOTA heuristics are occasionally left out due to scalability challenges with deep learning architectures. A subset of learning-based approaches (Khalil et al., 2017; Yolcu and P´oczos, 2019; Barrett et al., 2020, 2022) incorporate the functionality or behavior of traditional heuristics, potentially offering improved or enhanced performance by integrating heuristics principles with machine learning components. If a comprehensive comparison with the integrated heuristics and an ablation study of the deep learning architecture are lacking, it becomes challenging to determine the specific contribution of the deep learning architecture. Consequently, if the integrated heuristics are robust, the learned heuristics can seamlessly outperform the baseline heuristics, while the deep learning architecture plays a little role. Another great achievement of learned heuristics (Khalil et al., 2017; Barrett et al., 2020; Toenshof et al., 2019), initially trained on smaller and specific instances from a particular distribution, exhibits impressive performance when tested on larger instances from different distributions. This achievement stands as a significant accomplishment, aligning with the core objective of employing learning-based approaches: to mitigate the necessity for extensive customization and domain-specific knowledge often required by heuristics. Although classical heuristics with hyperparameters may also encounter challenges if they are fine-tuned for a specific distribution, they may also generalize across different distributions. The primary inquiry revolves around whether the learned heuristics indeed demonstrate superior generalizability compared to classical heuristics. A thorough and insightful comparison of learned heuristics against classical heuristics provides valuable insights into the generalizability of learned heuristics. Learned heuristics often lack theoretical guarantees, making empirical evaluation the sole method to comprehend the strengths and limitations of proposed methodologies. We believe that several fundamental, yet pivotal questions remain unexplored in the empirical evaluation of these works. While these inquiries are pertinent to all types of learned heuristics, we ask and answer them by analyzing highly cited and recent peer-reviewed publications focused on learning local search heuristics. Our goal is not to provide exhaustive benchmarks for the CO problems discussed in our work but rather to assist future researchers in evaluating their research. Concretely, we ask and answer the following questions: 1. Can learned heuristics be over-parameterized? Absolutely. Replacing the GNN in ECO-DQN with linear regression and utilizing a subset of features from ECO-DQN (Barrett et al., 2020) that links to Tabu Search (Glover, 1990), we introduce a pruned version of ECO-DQN termed SoftTabu. Our study demonstrates that SoftTabu showcases superior performance and generalizability in comparison to ECO-DQN for the NPhard Maximum-Cut (Max-Cut) problem. 1. Can learned heuristics be over-parameterized? Absolutely. 2. Can baseline bias be attributed to the superior performance of learned heuristics? Yes, we demonstrate that SoftTabu, a vanilla learned heuristic, can outperform S2V-DQN (Khalil et al., 2017) for the Max-Cut problem and GNNSAT (Yolcu and P´oczos, 2019) for Boolean Satisfiability (SAT). 2. Can baseline bias be attributed to the superior performance of learned heuristics? *3. Can learned heuristics demonstrate superior generalizability owing to instance selection bias?*Yes, we demonstrate that ECO-DQN shows poor generalization on harder instances and easily gets trapped in the search space. This paper is available on arxiv under CC 4.0 DEED license. This paper is available on arxiv under CC 4.0 DEED license.

Unveiling the Limits of Learned Local Search Heuristics: Are You the Mightiest of the Meek?

শেখা স্থানীয় অনুসন্ধান হিউরিস্টিকসের সীমা উন্মোচন করা: আপনি কি নম্রদের মধ্যে সবচেয়ে শক্তিশালী?

Die Grenzen erlernter Heuristiken für die lokale Suche aufdecken: Sind Sie der Mächtigste unter den Sanftmütigen?

Revelando los límites de la heurística de búsqueda local aprendida: ¿eres el más poderoso entre los mansos?

Dévoiler les limites des heuristiques de recherche locale apprises : êtes-vous le plus puissant des doux ?

सीखी गई स्थानीय खोज हेयुरिस्टिक्स की सीमाओं का अनावरण: क्या आप नम्र लोगों में सबसे शक्तिशाली हैं?

学習したローカル検索ヒューリスティックスの限界を明らかにする: あなたは最も強い弱者ですか?

학습된 지역 검색 휴리스틱의 한계 공개: 당신은 가장 온유한 사람입니까?

Revelando os limites das heurísticas de pesquisa local aprendidas: você é o mais poderoso dos mansos?

Раскрытие границ изученной эвристики локального поиска: вы самый могущественный из кротких?

Öğrenilmiş Yerel Arama Buluşsal Yöntemlerinin Sınırlarını Ortaya Çıkarıyoruz: Uysalların En Güçlüsü Siz misiniz?

Tiết lộ các giới hạn của phương pháp phỏng đoán tìm kiếm địa phương đã học: Bạn có phải là người mạnh mẽ nhất trong số những người nhu mì không?

揭示学习本地搜索启发式方法的局限性：你是最强大的吗？

This article was , an independent, nonprofit organization that works side by side with consumers for truth, transparency and fairness in the marketplace. . copublished with Consumer Reports Learn more here By now most internet users know their online activity is constantly tracked. No one should be shocked to see ads for items they previously searched for, or to be asked if their data can be shared with an unknown number of “partners.” But what is the scale of this surveillance? Judging from data collected by Facebook and by non-profit consumer watchdog Consumer Reports, it’s massive, and examining the data may leave you with more questions than answers. newly described in a unique study Using a panel of 709 volunteers who shared archives of their Facebook data, Consumer Reports found that a total of 186,892 companies sent data about them to the social network. On average, each participant in the study had their data sent to Facebook by 2,230 companies. That number varied significantly, with some panelists’ data listing over 7,000 companies providing their data. The Markup helped Consumer Reports recruit participants for the study. Participants downloaded an archive of the last three years of their data from their Facebook settings, then provided it to Consumer Reports. By collecting data this way, the study was able to examine a form of tracking that is normally hidden: so-called “server-to-server” tracking, in which personal data goes from a company’s servers to Meta’s servers. Another form of tracking, in which Meta tracking pixels are placed on company websites, is visible to users’ browsers. Because the data came from a self-selected group of users, and because the results were not demographically adjusted, the study does not “make any claims about how representative this sample is of the U.S. population as a whole,” Consumer Reports noted. Participants were also likely more privacy conscious and technically inclined than typical users and more likely to be members of Consumers Reports. Despite its limitations, the study offers a rare look, using data directly from Meta, on how personal information is collected and aggregated online. Meta spokesperson Emil Vazquez defended the company’s practices. “We offer a number of transparency tools to help people understand the information that businesses choose to share with us, and manage how it’s used,” wrote Vazquez in an emailed statement to The Markup. While Meta does provide transparency tools like the one that enabled the study, Consumer Reports identified problems with them, including that the identity of many data providers is unclear from the names disclosed to users and that companies that provide services to advertisers are often allowed to ignore opt-out requests. One company appeared in 96% of participants’ data: the San Francisco-based data broker LiveRamp. But the companies sharing your online activity to Facebook aren’t just little-known data brokers. Retailers like Home Depot, Walmart and Macy’s all were in the top 100 most frequently-seen companies in the study. Credit reporting and consumer data companies such as Experian and TransUnion’s Neustar also made the list, as did Amazon, Etsy and PayPal. LiveRamp did not respond to a request for comment. What Exactly Does This Data Contain? The data examined by Consumer Reports in this study comes from two types of collection: events and custom audiences. Both categories include information about what people do outside of Meta’s platforms. Custom audiences allow advertisers to upload customer lists to Meta, often including identifiers like email addresses and mobile advertising IDs. These customers, and so-called “lookalike audiences” made up of similar people, can then be targeted with ads on Meta’s platforms. The other category of data collection, “events,” describes interactions that the user had with a brand, which can occur outside of Meta’s apps and in the real world. Events can include visiting a page on a company’s website, leveling up in a game, visiting a physical store, or purchasing a product. These signals originate from Meta software code included in many mobile apps, their tracking pixel, which is included on many websites, and from server-to-server tracking, where a company’s server passes data to a Meta server. The Markup has written extensively about the and how it has been used to surveil people as they , , , , and . Meta Pixel dial suicide hotlines buy their groceries take the SATs file their taxes book appointments with their doctors Website owners can configure the pixel to track user website interactions such as searches or filling out a form, sending each action to Meta, even if the user doesn’t have an account on Facebook. Although research tools like The Markup’s “Pixel Hunt” can detect the Meta pixel or SDK tracking, there is no way for a consumer to monitor the traffic between a company’s server and Meta’s. This Consumer Reports study looks at server-to-server data along with the rest. How Can I See My Data? Facebook users can browse through the list of companies that have sent their data to Facebook by going to: https://accountscenter.facebook.com/info_and_permissions From this menu, users can either download or access their information. To see the companies outside of Meta that have been sharing your information, select “Your activity off Meta technologies,” then “Recent activity.” You might be prompted to re-enter your password at this point. This view will show you the number of recent connections between various third-party businesses you visited and Meta, and examples of the activity that was shared. You can choose to prevent future sharing by the company by selecting “Disconnect.” To see detailed information about these interactions, . request a copy of your data But after jumping through the many hoops required to actually get your hands on this data, you may still be left with lingering questions. For example, in addition to easily recognizable company names, more than 7,000 companies in Consumer Reports’ study were named using unreadable gibberish, or just numbers that don’t mean anything to most users. Even those companies with readable names often did not include links to the company website. Some company names were ambiguous, such as “Viking,” which could be a number of different businesses. “This type of tracking which occurs entirely outside of the user’s view is just so far outside of what people expect when they use the internet,” Caitriona Fitzgerald, Deputy Director of the Electronic Privacy Information Center, told The Markup in an interview. Fitzgerald said that while users are likely aware that Meta knows what they are doing while they are on Facebook and Instagram, “they don’t expect Meta to know what stores they walk into or what news articles they’re reading or every site they visit online.” In the report, Consumer Reports calls for a number of policy proposals covering data collection practices, some of which could be part of a national digital privacy law, something that the organization has long advocated for. Among the recommendations specifically aimed at Meta’s technology and the advertisers who use it: Require companies to adopt “data minimization” strategies which call for the collection of the absolute minimum amount of data needed to provide the service being offered. Expand the powers of “authorized agents” to act on behalf of consumers to act on their rights. is a mobile app by Consumer Reports that allows users to quickly opt-out of and delete data from a long list of companies and data brokers on behalf of users; the app takes advantage of state privacy laws’ authorized agent provisions. Permission Slip Increase ad transparency by creating ad archives that allow the public to see all ads that have been served to users on a platform, following the lead of the EU's . Digital Services Act Existing Meta transparency tools should improve the quality and readability of the data that is made available so consumers can actually act on the information they review. Fitzgerald echoed these recommendations, saying the problem lies with the fact that the burden is on the consumer to take action to prevent this data collection. Even a “global opt-out” mechanism allowing users to avoid having their data shared is not enough because “that still requires the user to take an action to protect their privacy. A lot of people are not going to have the time or knowledge to do that,” said Fitzgerald. Vazquez, Meta’s spokesperson, said the company would “continue to invest in data minimization technologies to keep up with evolving expectations. As we cover in our terms, businesses are responsible for getting permission to share people’s information with companies like ours.” For now, the lack of a federal privacy law leaves consumers in most states with few options. “I think people should be encouraging their elected officials to pass privacy laws that require businesses to change some of these business practices to stop this ubiquitous tracking of our every click and every movement,” said Fitzgerald. Credits: , , , , , Jon Keegan Carlo Cadenas Maria Puertas Gabriel Hongsdusit Ryan Tate Michael Reilly Also published here Photo by on Amanda Dalbjörn Unsplash

If You're a Facebook User, Thousands of Companies Are Watching You

আপনি যদি একজন ফেসবুক ব্যবহারকারী হন, হাজার হাজার কোম্পানি আপনাকে দেখছে

Wenn Sie ein Facebook-Nutzer sind, werden Sie von Tausenden Unternehmen beobachtet

Si eres usuario de Facebook, miles de empresas te están observando

Si vous êtes un utilisateur de Facebook, des milliers d'entreprises vous surveillent

यदि आप फेसबुक उपयोगकर्ता हैं, तो हजारों कंपनियां आप पर नजर रख रही हैं

あなたが Facebook ユーザーなら、何千もの企業があなたに注目しています

당신이 Facebook 사용자라면 수천 개의 회사가 당신을 지켜보고 있습니다.

Se você é um usuário do Facebook, milhares de empresas estão observando você

Если вы пользователь Facebook, за вами наблюдают тысячи компаний

Facebook Kullanıcısıysanız Binlerce Şirket Sizi İzliyor

Nếu bạn là người dùng Facebook, hàng nghìn công ty đang theo dõi bạn

如果您是 Facebook 用户，成千上万的公司正在关注您

The Way We Live Now by Anthony Trollope is part of the HackerNoon Books Series. You can jump to any chapter in this book . RUBY RUGGLES OBEYS HER GRANDFATHER here RUBY RUGGLES OBEYS HER GRANDFATHER. The next day there was great surprise at Sheep's Acre farm, which communicated itself to the towns of Bungay and Beccles, and even affected the ordinary quiet life of Carbury Manor. Ruby Ruggles had gone away, and at about twelve o'clock in the day the old farmer became aware of the fact. She had started early, at about seven in the morning; but Ruggles himself had been out long before that, and had not condescended to ask for her when he returned to the house for his breakfast. There had been a bad scene up in the bedroom overnight, after John Crumb had left the farm. The old man in his anger had tried to expel the girl; but she had hung on to the bed-post and would not go; and he had been frightened, when the maid came up crying and screaming murder. "You'll be out o' this to-morrow as sure as my name's Dannel Ruggles," said the farmer panting for breath. But for the gin which he had taken he would hardly have struck her;—but he had struck her, and pulled her by the hair, and knocked her about;—and in the morning she took him at his word and was away. About twelve he heard from the servant girl that she had gone. She had packed a box and had started up the road carrying the box herself. "Grandfather says I'm to go, and I'm gone," she had said to the girl. At the first cottage she had got a boy to carry her box into Beccles, and to Beccles she had walked. For an hour or two Ruggles sat, quiet, within the house, telling himself that she might do as she pleased with herself,—that he was well rid of her, and that from henceforth he would trouble himself no more about her. But by degrees there came upon him a feeling half of compassion and half of fear, with perhaps some mixture of love, instigating him to make search for her. She had been the same to him as a child, and what would people say of him if he allowed her to depart from him after this fashion? Then he remembered his violence the night before, and the fact that the servant girl had heard if she had not seen it. He could not drop his responsibility in regard to Ruby, even if he would. So, as a first step, he sent in a message to John Crumb, at Bungay, to tell him that Ruby Ruggles had gone off with a box to Beccles. John Crumb went open-mouthed with the news to Joe Mixet, and all Bungay soon knew that Ruby Ruggles had run away. After sending his message to Crumb the old man still sat thinking, and at last made up his mind that he would go to his landlord. He held a part of his farm under Roger Carbury, and Roger Carbury would tell him what he ought to do. A great trouble had come upon him. He would fain have been quiet, but his conscience and his heart and his terrors all were at work together,—and he found that he could not eat his dinner. So he had out his cart and horse and drove himself off to Carbury Hall. It was past four when he started, and he found the squire seated on the terrace after an early dinner, and with him was Father Barham, the priest. The old man was shown at once round into the garden, and was not long in telling his story. There had been words between him and his granddaughter about her lover. Her lover had been accepted and had come to the farm to claim his bride. Ruby had behaved very badly. The old man made the most of Ruby's bad behaviour, and of course as little as possible of his own violence. But he did explain that there had been threats used when Ruby refused to take the man, and that Ruby had, this day, taken herself off. "I always thought it was settled they were to be man and wife," said Roger. "It was settled, squoire;—and he war to have five hun'erd pound down;—money as I'd saved myself. Drat the jade." "Didn't she like him, Daniel?" "She liked him well enough till she'd seed somebody else." Then old Daniel paused, and shook his head, and was evidently the owner of a secret. The squire got up and walked round the garden with him,—and then the secret was told. The farmer was of opinion that there was something between the girl and Sir Felix. Sir Felix some weeks since had been seen near the farm and on the same occasion Ruby had been observed at some little distance from the house with her best clothes on. "He's been so little here, Daniel," said the squire. "It goes as tinder and a spark o' fire, that does," said the farmer. "Girls like Ruby don't want no time to be wooed by one such as that, though they'll fall-lall with a man like John Crumb for years." "I suppose she's gone to London." "Don't know nothing of where she's gone, squoire;—only she have gone some'eres. May be it's Lowestoffe. There's lots of quality at Lowestoffe a' washing theyselves in the sea." Then they returned to the priest, who might be supposed to be cognisant of the guiles of the world and competent to give advice on such an occasion as this. "If she was one of our people," said Father Barham, "we should have her back quick enough." "Would ye now?" said Ruggles, wishing at the moment that he and all his family had been brought up as Roman Catholics. "I don't see how you would have more chance of catching her than we have," said Carbury. "She'd catch herself. Wherever she might be she'd go to the priest, and he wouldn't leave her till he'd seen her put on the way back to her friends." "With a flea in her lug," suggested the farmer. "Your people never go to a clergyman in their distress. It's the last thing they'd think of. Any one might more probably be regarded as a friend than the parson. But with us the poor know where to look for sympathy." "She ain't that poor, neither," said the grandfather. "She had money with her?" "I don't know just what she had; but she ain't been brought up poor. And I don't think as our Ruby'd go of herself to any clergyman. It never was her way." "It never is the way with a Protestant," said the priest. "We'll say no more about that for the present," said Roger, who was waxing wroth with the priest. That a man should be fond of his own religion is right; but Roger Carbury was beginning to think that Father Barham was too fond of his religion. "What had we better do? I suppose we shall hear something of her at the railway. There are not so many people leaving Beccles but that she may be remembered." So the waggonette was ordered, and they all prepared to go off to the station together. But before they started John Crumb rode up to the door. He had gone at once to the farm on hearing of Ruby's departure, and had followed the farmer from thence to Carbury. Now he found the squire and the priest and the old man standing around as the horses were being put to the carriage. "Ye ain't a' found her, Mr. Ruggles, ha' ye?" he asked as he wiped the sweat from his brow. "Noa;—we ain't a' found no one yet." "If it was as she was to come to harm, Mr. Carbury, I'd never forgive myself,—never," said Crumb. "As far as I can understand it is no doing of yours, my friend," said the squire. "In one way, it ain't; and in one way it is. I was over there last night a bothering of her. She'd a' come round may be, if she'd a' been left alone. She wouldn't a' been off now, only for our going over to Sheep's Acre. But,—oh!" "What is it, Mr. Crumb?" "He's a coosin o' yours, squoire; and long as I've known Suffolk, I've never known nothing but good o' you and yourn. But if your baronite has been and done this! Oh, Mr. Carbury! If I was to wring his neck round, you wouldn't say as how I was wrong; would ye, now?" Roger could hardly answer the question. On general grounds the wringing of Sir Felix's neck, let the immediate cause for such a performance have been what it might, would have seemed to him to be a good deed. The world would be better, according to his thinking, with Sir Felix out of it than in it. But still the young man was his cousin and a Carbury, and to such a one as John Crumb he was bound to defend any member of his family as far as he might be defensible. "They says as how he was groping about Sheep's Acre when he was last here, a hiding himself and skulking behind hedges. Drat 'em all. They've gals enough of their own,—them fellows. Why can't they let a fellow alone? I'll do him a mischief, Master Roger; I wull;—if he's had a hand in this." Poor John Crumb! When he had his mistress to win he could find no words for himself; but was obliged to take an eloquent baker with him to talk for him. Now in his anger he could talk freely enough. "But you must first learn that Sir Felix has had anything to do with this, Mr. Crumb." "In coorse; in coorse. That's right. That's right. Must l'arn as he did it, afore I does it. But when I have l'arned!"— And John Crumb clenched his fist as though a very short lesson would suffice for him upon this occasion. They all went to the Beccles Station, and from thence to the Beccles post office,—so that Beccles soon knew as much about it as Bungay. At the railway station Ruby was distinctly remembered. She had taken a second-class ticket by the morning train for London, and had gone off without any appearance of secrecy. She had been decently dressed, with a hat and cloak, and her luggage had been such as she might have been expected to carry, had all her friends known that she was going. So much was made clear at the railway station, but nothing more could be learned there. Then a message was sent by telegraph to the station in London, and they all waited, loitering about the post office, for a reply. One of the porters in London remembered seeing such a girl as was described, but the man who was supposed to have carried her box for her to a cab had gone away for the day. It was believed that she had left the station in a four-wheel cab. "I'll be arter her. I'll be arter her at once," said John Crumb. But there was no train till night, and Roger Carbury was doubtful whether his going would do any good. It was evidently fixed on Crumb's mind that the first step towards finding Ruby would be the breaking of every bone in the body of Sir Felix Carbury. Now it was not at all apparent to the squire that his cousin had had anything to do with this affair. It had been made quite clear to him that the old man had quarrelled with his granddaughter and had threatened to turn her out of his house, not because she had misbehaved with Sir Felix, but on account of her refusing to marry John Crumb. John Crumb had gone over to the farm expecting to arrange it all, and up to that time there had been no fear about Felix Carbury. Nor was it possible that there should have been communication between Ruby and Felix since the quarrel at the farm. Even if the old man were right in supposing that Ruby and the baronet had been acquainted,—and such acquaintance could not but be prejudicial to the girl,—not on that account would the baronet be responsible for her abduction. John Crumb was thirsting for blood and was not very capable in his present mood of arguing the matter out coolly, and Roger, little as he loved his cousin, was not desirous that all Suffolk should know that Sir Felix Carbury had been thrashed within an inch of his life by John Crumb of Bungay. "I'll tell you what I'll do," said he, putting his hand kindly on the old man's shoulder. "I'll go up myself by the first train to-morrow. I can trace her better than Mr. Crumb can do, and you will both trust me." "There's not one in the two counties I'd trust so soon," said the old man. "But you'll let us know the very truth," said John Crumb. Roger Carbury made him an indiscreet promise that he would let him know the truth. So the matter was settled, and the grandfather and lover returned together to Bungay. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Anthony Trollope (2004). The Way We Live Now. Urbana, Illinois: Project Gutenberg. Retrieved October 2022 https://www.gutenberg.org/cache/epub/5231/pg5231-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

RUBY RUGGLES OBEYS HER GRANDFATHER

The advantages of digital transformation in education are self-evident – particularly when one regards the disruption that the pandemic has wrought in recent times. Digitization has enabled students to continue their endeavours mostly unhindered whilst also allowing access to an increased number of resources such as . It is easy, on this basis, to present the increased adoption of technology in education as an exclusively neutral or positive development. artificial intelligence (AI) tools However, reservations might be harbored about this rapid transformation. may be described as the tendency to gravitate towards technology as the ultimate answer to all individual or societal problems. Although it is obvious that humanity at large has been accepting of the rapid advancement of technology in recent years, it may not be without its disadvantages. Techno-solutionism One of the primary issues with solutionism and its role in society is its reductionism of philosophical concepts. This establishes a fundamental problem with digitization – the convenience of providing instant solutions instead of enabling an exploration of obstacles and alternate paths to addressing them. The principal aim of learning is the examination and analysis of abstract concepts and ideas. One of the oft-cited mantras associated with education alludes to it being not “the filling of a pail, but the lighting of a fire”. Whilst this declaration may have become trite through overuse, it has coherence when considering it in the face of solutionism. It is obvious, if not expected, that technology is used in answer to problems facing individuals in everyday life. This means that a resolution is sought and found quickly, rather than reflecting on a particular problem. It may seem prudent that time is not wasted when answers are so accessible. Arguably, however, this meditation can engage the brain and contribute to the development of higher-order cognition skills such as critical thinking and problem-solving. Proactively spending time thinking through and evaluating possible resolutions to a singular issue can evolve the brain’s ability to advocate and make decisions. The same cannot be said for reactively being furnished with the solution by an automaton. This has implications for how developing minds acquire information and solve problems. In a culture devoid of technology, the optimal method of resolving complex issues is to deliberate about the process. Where technology is accessible, it is likely that the method will be guided by the correct answer already being provided by a digital agent. Therefore, there is less critical thought by the user. Currently, it is difficult to assess how large a detriment this is. After all, it has been the norm throughout the passage of time for the human brain to evolve and discard qualities that are no longer useful. A century ago, the skills perceived as being important included good memory and penmanship. With the advent of computers, these skills have now largely become obsolete. Instead, the ability to effectively process information is now more useful and valuable to the individual. In this way, technology has assisted society by automating a task and extricating the human mind to focus on more complex undertakings. Could it be possible that by delegating critical thinking to machines the human brain could further progress? It is impossible to know for sure, however, it is highly unlikely. The ability to thoughtfully evaluate and interpret alternatives empowers individuals to contribute efficiently to their chosen vocation. Globally, the development of higher-order cognition skills during adolescence is a dominant instructional goal by educators and psychologists, and with just cause. It is essential, therefore, that the adoption of digitization in education does not detract from any emphasis placed on cultivating critical thinking in adolescence. Awareness of the importance of self-efficacy and the negative effect passive learning can have on it must be figured out in any discourse. With the development of sophisticated generative AI tools such as there is a danger that eventually tasks such as written assignments will be assigned to machines instead of being the product of the acumen of students. This would impact an individual’s ability to adequately structure material for consumption, as well as their capacity to assess what information is relevant to include. GPT-4 The indicators of critical thinking could include the skill to successfully adjudicate the credibility of sources, to advance and defend a rational hypothesis, and to formulate pertinent clarifying questions. These abilities are not present in generative modeling as they currently exist and are among those most at risk of being depleted in humans should reasoning be deferred wholly to machines. The only way that the information produced by AI can be verified is through human inspection. Thus, consciousness of the limits of AI is fundamental. If written assignments are designated to automatons, there is a greater compulsion on educators to promote and test critical thinking skills instead of the tasks that machines already excel at such as memorization and formatting. There is a necessity to incorporate AI tools in education, and this necessity is driven by the level of interaction that students will have with generative AI in daily life. If the inclusion of AI cannot be avoided, is there a possibility that it could, instead, be embraced? Certainly, there seems to be cause for hope. It has been extensively documented over time that instruction in the classroom suffers from a uniformity of approach that is not appropriate or optimal for every student. With the advent of educational AI, there is a prospect of personalizing the learning experience to assist students more ably in comprehending complex concepts and processes. In addition, training machines to better understand the requirements of students, in turn, can provide information to teachers and educators on how to instruct more effectively. Thus, it is imperative that the teacher as the agent of change in the classroom espouses the positive aspects of AI and profoundly integrates them within the instruction. As AI becomes more complex, its functionality becomes more accessible, and education can afford to benefit from its assimilation.

Critical Thinking to AI: Are you a Friend or Foe?

এআই-এর প্রতি সমালোচনামূলক চিন্তাভাবনা: আপনি কি বন্ধু নাকি শত্রু?

Kritisches Denken zur KI: Sind Sie ein Freund oder ein Feind?

Pensamiento crítico para la IA: ¿eres amigo o enemigo?

Pensée critique envers l'IA : êtes-vous un ami ou un ennemi ?

एआई के लिए गंभीर सोच: क्या आप दोस्त हैं या दुश्मन?

AI に対する批判的思考: あなたは敵ですか、それとも味方ですか?

AI에 대한 비판적 사고: 당신은 친구입니까, 적입니까?

Pensamento crítico para IA: você é amigo ou inimigo?

Yapay Zeka için Eleştirel Düşünme: Dost musunuz, Düşman mısınız?

Tư duy phê phán đối với AI: Bạn là bạn hay thù?

人工智能的批判性思维：你是朋友还是敌人？

All Wars End (or Not)

Voyage on Goat Island

The Curious Case of the Callers

Let’s be honest — is there anyone who has started their own business not wanting to be the next Steve Jobs, Larry Page or Sara Blakely? Surely, you don’t need to copy your role model to gain success, but you can still learn from them. explored the characteristics of more than 30,000 business owners and categorized them into several groups according to their priorities and main qualities. The Founder Institute I took a closer look at the research and made some adjustments of my own. Knowing which type of business owner I am, helped me in presenting myself and positioning my personal brand. By knowing our leadership style and following it, we can send a clear and distinct message to the world. 1. Hustler In the world, hustlers are achievers who are chasing their dreams tirelessly and never giving up. They usually start small and get to the top not due to the capital, but due to hard work. They are self-confident and are never afraid of difficulties. business They treat disappointment just like another step in their professional journey. A hustler is not afraid of rejections and seems to be able to sell literally anything to anyone. A great example of this type of business owner would be Sophia Amoruso, founder of Nasty Gal, who started with a small store and grew her project into a multimillion-dollar business. She is well known for her belief in the power of hard work and determination, and nonstandard methods, like digging into a dumpster searching for vintage clothes to sell on eBay. eBay 2. Visionary Visionaries strive for innovations, creativity, and new approaches. These people are inspired by a big idea and know how to inspire others by it. Such a leader knows how to motivate their team and gives them a lot of freedom and space for creativity. A typical visionary would be , a co-founder of , who went all the way from opening a DVD rental shop to changing the whole picture of online streaming. As a leader, he is known for rejecting micromanagement and giving his team maximum independence. For example, Netflix employees can have unlimited vacation time, as long as they deliver good results. Reed Hastings Netflix 3. Strategist Strategists are very data-driven. They research, calculate everything, think through each step, and build long-term plans. These are not people who would just go by instincts or risk everything. Strategists leave themselves no room for making mistakes. They sometimes may dig too deep into data and facts, and usually need a lot of time to make a decision, but it will be as informed as possible. This type of entrepreneurship is brightly represented by Melanie Perkins, a founder of Canva, who pursues data-driven decision-making to compete with Adobe and Microsoft successfully. 4. Machine The Machines are the most reliable and sustainable people ever. They just get things done, quickly and effectively. These are natural problem-solvers who work hard and are ready to put in as much effort as needed to provide quality. The Machines often rely heavily on automation and tech solutions to increase efficiency. Along with many strong points, Machines typically have a weak spot — fear of delegating. This approach is typically called “solo entrepreneurship”, as business owners who follow it, prefer to do everything on their own and need to schedule every minute to follow the track. This is an entrepreneurship style brightly represented by Tim Ferris, entrepreneur, podcaster, and author, famous for his self-help book “The 4-Hour Work Week”, which he came up with, while working 14 hours a day. 5. Prodigy People often look at the Prodigies with a question: “How are they even doing it?”. These entrepreneurs just seem to be gifted with a high IQ and a natural instinct which help them earn their millions with seemingly no effort paid. We can easily think of tech geniuses who would be perfect examples of the Prodigies, like Mark Zuckerberg. He became world-famous due to his high intelligence, innovative ideas (like turning Harvard students’ platform into a network billions of people could use), and instinctual approach to decision-making. Which One Are You? Do any of these resonate with you? If you’re struggling with choosing one — you’re right. Our personal leadership style is usually a combination of different approaches with one prevailing. But we can still benefit from getting inspiration from our role models and examining their ways of managing business. Myself, I find the Visionary’s leadership model most appealing and always keep a picture of how to follow it in mind. I feel that pinpointing my predominant type of leadership made me one step closer to my ultimate business goal.

5 Types of Business Owners: Which One are You?

5 ধরনের ব্যবসার মালিক: আপনি কোনটি?

5 Arten von Unternehmern: Welcher sind Sie?

5 tipos de empresarios: ¿cuál eres tú?

5 types d'entrepreneurs : lequel êtes-vous ?

5 प्रकार के व्यवसाय स्वामी: आप कौन से हैं?

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