Bizzy

bizzy

Ron Shah, Bizly CEO Meetings dominated my life for 14 years as a venture capitalist. They were essential and often. Sometimes they were mundane, but mostly they were portals of inspiration and facilitated the deepest moments of personal connection. For a while, there was popular thinking that technology may reduce the need for meetings. In reality, the opposite happened: The need for meetings in business exploded. Video conference calls and text messages failed to replace the most essential elements of business. Today, meetings are often the prime indication of progress — someone pulling themselves away from their screen and traveling to see you in person equates to progress. In this respect, perhaps the meeting is the quintessential of all business activities. real Worklife is changing. Technology has enabled remote and mobile working, often around the clock and around the world. Employers are no longer nervous about hiring the expert they need who resides on the other end of the country. People are spending more time with their families, less time in traffic and capturing many more moments in between. And when this independence has been coupled with highly engaged in-person group work sessions, productivity has soared. Teams need these group sessions to be impactful and memorable. Desks and chairs just aren’t enough. Meetings must be potent experiences. Meetings must make a difference in the way your team members about themselves, their colleagues and the mission at hand. And employers need to provide the safest and most reliable ways to meet, while fulfilling their duty of care. feel This is why we created Bizly. Simply put, Bizly is a mobile app for instantly booking private meeting rooms at leading hotels. Yes, we optimize underutilized hotel inventory. But our heart and soul is much more than that. Bizly is the only platform that enterprises can confidently use and know that they will get a great meeting experience for their team. They will be treated nicely, have access to help when they need it, be able to order food & beverage and have all the technology and amenities they need right at their fingertips. Most importantly, if there is ever an emergency or problem, hospitality professionals are onsite 24/7 to take care of them. In a fast-moving world, with employees constantly working on-the-go, we give enterprises the confidence that there is reliable workspace and meeting rooms at hotel brands they trust. At Bizly, our mission is to deliver extraordinary meeting experiences for enterprises. We want teams and clients to remember those meetings, and bring them closer to each other and the mission at hand. In addition to helping enterprises, we are excited to bring the most cutting edge solution to the hospitality industry so they can serve this need in the most seamless, upscale and enjoyable way anyone has ever imagined. We help hotels both centralize and visualize their meeting room and workspace inventory, which is very difficult to book today through the manual process. With Bizly, hotels have a partner who understands how to deliver seamless and exceptional meeting experiences, with a focus on luxury and quality. Several of our hotel partners have already begun their converting bedrooms to workspaces and meeting rooms, powered by Bizly, and are ready for the hospitality service of the future. By Ron Shah, Bizly CEO Download the Bizly iOS app here (Android coming soon).

Breathing Life into Bizly

Authors:
(1) Shih-Tang Su, University of Michigan, Ann Arbor (shihtang@umich.edu);
(2) Vijay G. Subramanian, University of Michigan, Ann Arbor and (vgsubram@umich.edu);
(3) Grant Schoenebeck, University of Michigan, Ann Arbor (schoeneb@umich.edu). Table of Links Abstract and 1. Introduction 2. Problem Formulation 2.1 Model of Binary-Outcome Experiments in Two-Phase Trials 3 Binary-outcome Experiments in Two-phase Trials and 3.1 Experiments with screenings 3.2 Assumptions and induced strategies 3.3 Constraints given by phase-II experiments 3.4 Persuasion ratio and the optimal signaling structure 3.5 Comparison with classical Bayesian persuasion strategies 4 Binary-outcome Experiments in Multi-phase trials and 4.1 Model of binary-outcome experiments in multi-phase trials 4.2 Determined versus sender-designed experiments 4.3 Multi-phase model and classical Bayesian persuasion and References 4.3 Multi-phase model and classical Bayesian persuasion Note that the pruned tree will potentially be unbalanced. Lemma 7. Given an N-phase trial M with binary-outcome experiments, if there exists a pruned N-phase trial model P run(M) such that the following two conditions hold, then the sender’s expected utility is given by an equivalent single-phase Bayesian persuasion model. For every non-trivial determined experiment, its sibling is either a trivial or a sender-designed experiment.


There exists a least one sender-designed experiment in each (from root to leaf) experiment sequence of P run(M). References Au, P.H.: Dynamic information disclosure. The RAND Journal of Economics 46(4), 791–823 (2015)


Basu, D.: Statistical information and likelihood [with discussion]. Sankhy¯a: The Indian Journal of Statistics, Series A pp. 1–71 (1975)


Bergemann, D., Morris, S.: Information design: A unified perspective. Journal of Economic Literature 57(1), 44–95 (2019)


Bizzotto, J., R¨udiger, J., Vigier, A.: Dynamic persuasion with outside information. American Economic Journal: Microeconomics 13(1), 179–94 (2021)


Bizzotto, J., Vigier, A.: Can a better informed listener be easier to persuade? Economic Theory 72(3), 705–721 (2021)


Blackwell, D.: Equivalent comparisons of experiments. The annals of mathematical statistics pp. 265–272 (1953)


Dughmi, S., Kempe, D., Qiang, R.: Persuasion with limited communication. In: Proceedings of the 2016 ACM Conference on Economics and Computation. pp. 663–680 (2016)


Dughmi, S., Xu, H.: Algorithmic Bayesian persuasion. SIAM Journal on Computing 50(3), 68–97 (2019)


Ely, J.C.: Beeps. American Economic Review 107(1), 31–53 (2017)


Ely, J.C., Szydlowski, M.: Moving the goalposts. Journal of Political Economy 128(2), 468–506 (2020)


Farhadi, F., Teneketzis, D.: Dynamic information design: A simple problem on optimal sequential information disclosure. Available at SSRN 3554960 (2020)


Forges, F., Koessler, F.: Long persuasion games. Journal of Economic Theory 143(1), 1–35 (2008)


Gradwohl, R., Hahn, N., Hoefer, M., Smorodinsky, R.: Algorithms for persuasion with limited communication. In: Proceedings of the 2021 ACM-SIAM Symposium on Discrete Algorithms (SODA). pp. 637–652. SIAM (2021)


Honryo, T.: Dynamic persuasion. Journal of Economic Theory 178, 36–58 (2018)


Kamenica, E.: Bayesian persuasion and information design. Annual Review of Economics 11, 249 272 (2019)


Kamenica, E., Gentzkow, M.: Bayesian persuasion. American Economic Review 101(6), 2590–2615 (2011)


Kolotilin, A., Mylovanov, T., Zapechelnyuk, A., Li, M.: Persuasion of a privately informed receiver. Econometrica 85(6), 1949–1964 (2017)


Le Treust, M., Tomala, T.: Persuasion with limited communication capacity. Journal of Economic Theory 184, 104940 (2019)


Li, F., Norman, P.: On Bayesian persuasion with multiple senders. Economics Letters 170, 66–70 (2018)


Li, J., Zhou, J.: Blackwell’s informativeness ranking with uncertainty-averse preferences. Games and Economic Behavior 96, 18–29 (2016)


Meigs, E., Parise, F., Ozdaglar, A., Acemoglu, D.: Optimal dynamic information provision in traffic routing. arXiv preprint arXiv:2001.03232 (2020)


Nguyen, A., Tan, T.Y.: Bayesian persuasion with costly messages. Available at SSRN 3298275 (2019)


Su, S.T., Subramanian, V., Schoenebeck, G.: Bayesian persuasion in sequential trials. arXiv preprint arXiv:2110.09594 (2021) This paper is available on arxiv under CC 4.0 license. Authors: (1) Shih-Tang Su, University of Michigan, Ann Arbor (shihtang@umich.edu); (2) Vijay G. Subramanian, University of Michigan, Ann Arbor and (vgsubram@umich.edu); (3) Grant Schoenebeck, University of Michigan, Ann Arbor (schoeneb@umich.edu). Authors: Authors: (1) Shih-Tang Su, University of Michigan, Ann Arbor (shihtang@umich.edu); (2) Vijay G. Subramanian, University of Michigan, Ann Arbor and (vgsubram@umich.edu); (3) Grant Schoenebeck, University of Michigan, Ann Arbor (schoeneb@umich.edu). Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Problem Formulation 2. Problem Formulation 2.1 Model of Binary-Outcome Experiments in Two-Phase Trials 2.1 Model of Binary-Outcome Experiments in Two-Phase Trials 3 Binary-outcome Experiments in Two-phase Trials and 3.1 Experiments with screenings 3 Binary-outcome Experiments in Two-phase Trials and 3.1 Experiments with screenings 3.2 Assumptions and induced strategies 3.2 Assumptions and induced strategies 3.3 Constraints given by phase-II experiments 3.3 Constraints given by phase-II experiments 3.4 Persuasion ratio and the optimal signaling structure 3.4 Persuasion ratio and the optimal signaling structure 3.5 Comparison with classical Bayesian persuasion strategies 3.5 Comparison with classical Bayesian persuasion strategies 4 Binary-outcome Experiments in Multi-phase trials and 4.1 Model of binary-outcome experiments in multi-phase trials 4 Binary-outcome Experiments in Multi-phase trials and 4.1 Model of binary-outcome experiments in multi-phase trials 4.2 Determined versus sender-designed experiments 4.2 Determined versus sender-designed experiments 4.3 Multi-phase model and classical Bayesian persuasion and References 4.3 Multi-phase model and classical Bayesian persuasion and References 4.3 Multi-phase model and classical Bayesian persuasion Note that the pruned tree will potentially be unbalanced. Lemma 7. Given an N-phase trial M with binary-outcome experiments, if there exists a pruned N-phase trial model P run(M) such that the following two conditions hold, then the sender’s expected utility is given by an equivalent single-phase Bayesian persuasion model. Lemma 7. Given an N-phase trial M with binary-outcome experiments, if there exists a pruned N-phase trial model P run(M) such that the following two conditions hold, then the sender’s expected utility is given by an equivalent single-phase Bayesian persuasion model. For every non-trivial determined experiment, its sibling is either a trivial or a sender-designed experiment. There exists a least one sender-designed experiment in each (from root to leaf) experiment sequence of P run(M). For every non-trivial determined experiment, its sibling is either a trivial or a sender-designed experiment. For every non-trivial determined experiment, its sibling is either a trivial or a sender-designed experiment. For every non-trivial determined experiment, its sibling is either a trivial or a sender-designed experiment. There exists a least one sender-designed experiment in each (from root to leaf) experiment sequence of P run(M). There exists a least one sender-designed experiment in each (from root to leaf) experiment sequence of P run(M). There exists a least one sender-designed experiment in each (from root to leaf) experiment sequence of P run(M). References Au, P.H.: Dynamic information disclosure. The RAND Journal of Economics 46(4), 791–823 (2015) Basu, D.: Statistical information and likelihood [with discussion]. Sankhy¯a: The Indian Journal of Statistics, Series A pp. 1–71 (1975) Bergemann, D., Morris, S.: Information design: A unified perspective. Journal of Economic Literature 57(1), 44–95 (2019) Bizzotto, J., R¨udiger, J., Vigier, A.: Dynamic persuasion with outside information. American Economic Journal: Microeconomics 13(1), 179–94 (2021) Bizzotto, J., Vigier, A.: Can a better informed listener be easier to persuade? Economic Theory 72(3), 705–721 (2021) Blackwell, D.: Equivalent comparisons of experiments. The annals of mathematical statistics pp. 265–272 (1953) Dughmi, S., Kempe, D., Qiang, R.: Persuasion with limited communication. In: Proceedings of the 2016 ACM Conference on Economics and Computation. pp. 663–680 (2016) Dughmi, S., Xu, H.: Algorithmic Bayesian persuasion. SIAM Journal on Computing 50(3), 68–97 (2019) Ely, J.C.: Beeps. American Economic Review 107(1), 31–53 (2017) Ely, J.C., Szydlowski, M.: Moving the goalposts. Journal of Political Economy 128(2), 468–506 (2020) Farhadi, F., Teneketzis, D.: Dynamic information design: A simple problem on optimal sequential information disclosure. Available at SSRN 3554960 (2020) Forges, F., Koessler, F.: Long persuasion games. Journal of Economic Theory 143(1), 1–35 (2008) Gradwohl, R., Hahn, N., Hoefer, M., Smorodinsky, R.: Algorithms for persuasion with limited communication. In: Proceedings of the 2021 ACM-SIAM Symposium on Discrete Algorithms (SODA). pp. 637–652. SIAM (2021) Honryo, T.: Dynamic persuasion. Journal of Economic Theory 178, 36–58 (2018) Kamenica, E.: Bayesian persuasion and information design. Annual Review of Economics 11, 249 272 (2019) Kamenica, E., Gentzkow, M.: Bayesian persuasion. American Economic Review 101(6), 2590–2615 (2011) Kolotilin, A., Mylovanov, T., Zapechelnyuk, A., Li, M.: Persuasion of a privately informed receiver. Econometrica 85(6), 1949–1964 (2017) Le Treust, M., Tomala, T.: Persuasion with limited communication capacity. Journal of Economic Theory 184, 104940 (2019) Li, F., Norman, P.: On Bayesian persuasion with multiple senders. Economics Letters 170, 66–70 (2018) Li, J., Zhou, J.: Blackwell’s informativeness ranking with uncertainty-averse preferences. Games and Economic Behavior 96, 18–29 (2016) Meigs, E., Parise, F., Ozdaglar, A., Acemoglu, D.: Optimal dynamic information provision in traffic routing. arXiv preprint arXiv:2001.03232 (2020) Nguyen, A., Tan, T.Y.: Bayesian persuasion with costly messages. Available at SSRN 3298275 (2019) Su, S.T., Subramanian, V., Schoenebeck, G.: Bayesian persuasion in sequential trials. arXiv preprint arXiv:2110.09594 (2021) Au, P.H.: Dynamic information disclosure. The RAND Journal of Economics 46(4), 791–823 (2015) Au, P.H.: Dynamic information disclosure. The RAND Journal of Economics 46(4), 791–823 (2015) Basu, D.: Statistical information and likelihood [with discussion]. Sankhy¯a: The Indian Journal of Statistics, Series A pp. 1–71 (1975) Basu, D.: Statistical information and likelihood [with discussion]. Sankhy¯a: The Indian Journal of Statistics, Series A pp. 1–71 (1975) Bergemann, D., Morris, S.: Information design: A unified perspective. Journal of Economic Literature 57(1), 44–95 (2019) Bergemann, D., Morris, S.: Information design: A unified perspective. Journal of Economic Literature 57(1), 44–95 (2019) Bizzotto, J., R¨udiger, J., Vigier, A.: Dynamic persuasion with outside information. American Economic Journal: Microeconomics 13(1), 179–94 (2021) Bizzotto, J., R¨udiger, J., Vigier, A.: Dynamic persuasion with outside information. American Economic Journal: Microeconomics 13(1), 179–94 (2021) Bizzotto, J., Vigier, A.: Can a better informed listener be easier to persuade? Economic Theory 72(3), 705–721 (2021) Bizzotto, J., Vigier, A.: Can a better informed listener be easier to persuade? Economic Theory 72(3), 705–721 (2021) Blackwell, D.: Equivalent comparisons of experiments. The annals of mathematical statistics pp. 265–272 (1953) Blackwell, D.: Equivalent comparisons of experiments. The annals of mathematical statistics pp. 265–272 (1953) Dughmi, S., Kempe, D., Qiang, R.: Persuasion with limited communication. In: Proceedings of the 2016 ACM Conference on Economics and Computation. pp. 663–680 (2016) Dughmi, S., Kempe, D., Qiang, R.: Persuasion with limited communication. In: Proceedings of the 2016 ACM Conference on Economics and Computation. pp. 663–680 (2016) Dughmi, S., Xu, H.: Algorithmic Bayesian persuasion. SIAM Journal on Computing 50(3), 68–97 (2019) Dughmi, S., Xu, H.: Algorithmic Bayesian persuasion. SIAM Journal on Computing 50(3), 68–97 (2019) Ely, J.C.: Beeps. American Economic Review 107(1), 31–53 (2017) Ely, J.C.: Beeps. American Economic Review 107(1), 31–53 (2017) Ely, J.C., Szydlowski, M.: Moving the goalposts. Journal of Political Economy 128(2), 468–506 (2020) Ely, J.C., Szydlowski, M.: Moving the goalposts. Journal of Political Economy 128(2), 468–506 (2020) Farhadi, F., Teneketzis, D.: Dynamic information design: A simple problem on optimal sequential information disclosure. Available at SSRN 3554960 (2020) Farhadi, F., Teneketzis, D.: Dynamic information design: A simple problem on optimal sequential information disclosure. Available at SSRN 3554960 (2020) Forges, F., Koessler, F.: Long persuasion games. Journal of Economic Theory 143(1), 1–35 (2008) Forges, F., Koessler, F.: Long persuasion games. Journal of Economic Theory 143(1), 1–35 (2008) Gradwohl, R., Hahn, N., Hoefer, M., Smorodinsky, R.: Algorithms for persuasion with limited communication. In: Proceedings of the 2021 ACM-SIAM Symposium on Discrete Algorithms (SODA). pp. 637–652. SIAM (2021) Gradwohl, R., Hahn, N., Hoefer, M., Smorodinsky, R.: Algorithms for persuasion with limited communication. In: Proceedings of the 2021 ACM-SIAM Symposium on Discrete Algorithms (SODA). pp. 637–652. SIAM (2021) Honryo, T.: Dynamic persuasion. Journal of Economic Theory 178, 36–58 (2018) Honryo, T.: Dynamic persuasion. Journal of Economic Theory 178, 36–58 (2018) Kamenica, E.: Bayesian persuasion and information design. Annual Review of Economics 11, 249 272 (2019) Kamenica, E.: Bayesian persuasion and information design. Annual Review of Economics 11, 249 272 (2019) Kamenica, E., Gentzkow, M.: Bayesian persuasion. American Economic Review 101(6), 2590–2615 (2011) Kamenica, E., Gentzkow, M.: Bayesian persuasion. American Economic Review 101(6), 2590–2615 (2011) Kolotilin, A., Mylovanov, T., Zapechelnyuk, A., Li, M.: Persuasion of a privately informed receiver. Econometrica 85(6), 1949–1964 (2017) Kolotilin, A., Mylovanov, T., Zapechelnyuk, A., Li, M.: Persuasion of a privately informed receiver. Econometrica 85(6), 1949–1964 (2017) Le Treust, M., Tomala, T.: Persuasion with limited communication capacity. Journal of Economic Theory 184, 104940 (2019) Le Treust, M., Tomala, T.: Persuasion with limited communication capacity. Journal of Economic Theory 184, 104940 (2019) Li, F., Norman, P.: On Bayesian persuasion with multiple senders. Economics Letters 170, 66–70 (2018) Li, F., Norman, P.: On Bayesian persuasion with multiple senders. Economics Letters 170, 66–70 (2018) Li, J., Zhou, J.: Blackwell’s informativeness ranking with uncertainty-averse preferences. Games and Economic Behavior 96, 18–29 (2016) Li, J., Zhou, J.: Blackwell’s informativeness ranking with uncertainty-averse preferences. Games and Economic Behavior 96, 18–29 (2016) Meigs, E., Parise, F., Ozdaglar, A., Acemoglu, D.: Optimal dynamic information provision in traffic routing. arXiv preprint arXiv:2001.03232 (2020) Meigs, E., Parise, F., Ozdaglar, A., Acemoglu, D.: Optimal dynamic information provision in traffic routing. arXiv preprint arXiv:2001.03232 (2020) Nguyen, A., Tan, T.Y.: Bayesian persuasion with costly messages. Available at SSRN 3298275 (2019) Nguyen, A., Tan, T.Y.: Bayesian persuasion with costly messages. Available at SSRN 3298275 (2019) Su, S.T., Subramanian, V., Schoenebeck, G.: Bayesian persuasion in sequential trials. arXiv preprint arXiv:2110.09594 (2021) Su, S.T., Subramanian, V., Schoenebeck, G.: Bayesian persuasion in sequential trials. arXiv preprint arXiv:2110.09594 (2021) This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Generalizing Signaling Strategies in Multi-phase Trials

You Should Give That Talk or Write That Blog

আপনি যে টক দিতে হবে বা ব্লগ লিখতে হবে

Sie sollten diesen Vortrag halten oder diesen Blog schreiben

Deberías dar esa charla o escribir ese blog

Vous devriez donner cette conférence ou écrire ce blog

आपको वह भाषण देना चाहिए या वह ब्लॉग लिखना चाहिए

あなたはその講演をしたり、そのブログを書いたりするべきです

당신은 그 이야기를 해야 합니다. 아니면 그 블로그를 써야 합니다.

Você deveria dar aquela palestra ou escrever aquele blog

Вам следует выступить с этой речью или написать этот блог

O Konuşmayı Yapmalısın ya da O Blogu Yazmalısın

Bạn nên nói chuyện hoặc viết blog đó

你应该发表演讲或写博客

During the COVID-19 pandemic, dating apps experienced a surge in users. Match Group, which owns over 45 dating brands including Tinder, Hinge, and OKCupid, reported an increase in subscribers and downloads in its second quarter 2020 earnings report, surpassing pre-pandemic levels. However, many dating platforms now struggle to grow sales, attract new users, and convince younger generations to pay for subscriptions. According to The New York Times, industry giants Match Group and Bumble have collectively lost over $40 billion in market value since 2021. Investors are now seeking innovation, prompting major companies to bring in leaders willing to experiment with new features, such as artificial intelligence, and strategies that would attract new users, especially the younger ones. AI in the Service of Love One of the most intriguing trends in the dating app market is the integration of artificial intelligence. Speaking at the Bloomberg Tech Summit in San Francisco, Bumble founder Whitney Wolfe Herd highlighted how AI could enhance the search for connections. Herd proposed using AI assistants, or AI dating concierges, to reach out to potential partners, thereby expanding the pool of people each user can communicate with and increasing the chances of finding a perfect match. “There is a world where your dating concierge could go and date for you with other dating concierges ... and then you don’t have to talk to 600 people,” Herd explained during her interview. Startups are exploring how AI can improve matchmaking. Some apps use neural networks trained to identify the exact type of people a user is attracted to, making the dating experience more enjoyable. Liesel Sharabi, an associate professor at Arizona State University, noted that AI could help reduce "dating burnout" by making matches more accurate. “Looking at AI, there are all kinds of possibilities for helping to make the decision fatigue a little bit less pronounced when you're just having to swipe through so many profiles, and also helping to make the matchmaking process better by just improving upon some of the algorithms that they've been using," Sharabi said. There is even an AI platform called Meeno that people can consult about their relationships. Meeno was founded by Renate Nyborg, a former CEO of Tinder. By creating Meeno, she aimed to address the increased level of loneliness among young people that occurred after the COVID-19 pandemic. Meeno was designed to become a chatting partner and an unbiased advisor to people who feel lonely. One of the users, Andrew, shared his experience of using Meeno with Time Magazine. According to Andrew, Meeno didn’t suggest anything groundbreaking, but just the process of chatting with the app and reading advice on the screen that confirmed his own instincts gave him the confidence he needed to act. There is another platform where AI can give relationship advice. On YouMatch, the AI is trained to analyze people’s personality types and consider their behavior on the app to offer personalized advice on how they should navigate their romantic lives and deal with different situations. Beyond Romance: Facilitating Social Connections The Chinese dating app market is booming, largely due to features that promote social connections rather than just romance. New “social discovery” features help users find friends and acquaintances with similar interests and hobbies. Apps like Bumble offer BFF mode for finding friends and Bizz mode for networking, while Tinder's Friends of Friends feature helps users find potential friends. For many, dating apps have become virtual sanctuaries—a modern twist on the "third place,” a concept urbanists describe as a community space between work and home, The New York Times reports. These platforms allow users to explore hobbies, discuss popular topics, and meet new friends, helping to combat loneliness. Shift Toward Serious Dating In early 2023, Tinder released its international “Green Flags Study,” surveying 8,000 heterosexual men and women aged 18-34 across the U.S., U.K., Australia, and Canada. The study found that 53% of men and 68% of women are seeking a romantic relationship, indicating that a majority of singles using dating apps are looking for something serious. Hinge data shows that Gen Z singles are more clear and honest about their relationship intentions and expectations, with over half of users seeking a long-term, serious relationship with a deeper connection. This trend has led to a decline in “hookup culture”, which peaked with the rise of Tinder, and a growth in apps focused on serious relationships. To help users build deeper relationships, some startups rely on scientific findings and methodology. For example, YouMatch algorithms conduct each user's personality analysis based on the DISC (Dominance-Influence-Steadiness-Conscientiousness) methodology developed by American psychologist and Harvard graduate William Marston. After determining their personality type and assessing their values and goals, the app matches people with a higher level of personal compatibility, giving them more potential to build a common future. Prioritizing Users' Wellbeing and Safety Young singles are increasingly prioritizing mental health and well-being. According to a Hinge survey, 97% of users prefer dating someone who looks after their mental health and 86% are more likely to agree to a second date if their partner is attending therapy. Nearly 60% of users reported taking time to focus on themselves before entering a relationship. To support users' well-being, dating apps share content on mental health and dating ethics. Bumble, for instance, publishes articles on avoiding ghosting and managing dating burnout. Safety features are also a priority; Tinder's "Share My Date” feature allows users to share their date plans with friends and family with one click, just to be on the safe side. Overall, promoting safe and respectful communication has become a central trend among both major and emerging dating apps. During the COVID-19 pandemic, dating apps experienced a surge in users. Match Group, which owns over 45 dating brands including Tinder, Hinge, and OKCupid, reported an increase in subscribers and downloads in its second quarter 2020 earnings report , surpassing pre-pandemic levels. report report However, many dating platforms now struggle to grow sales, attract new users, and convince younger generations to pay for subscriptions. According to The New York Times, industry giants Match Group and Bumble have collectively lost over $40 billion in market value since 2021. lost over $40 billion lost over $40 billion Investors are now seeking innovation, prompting major companies to bring in leaders willing to experiment with new features, such as artificial intelligence, and strategies that would attract new users, especially the younger ones. AI in the Service of Love AI in the Service of Love One of the most intriguing trends in the dating app market is the integration of artificial intelligence. Speaking at the Bloomberg Tech Summit in San Francisco, Bumble founder Whitney Wolfe Herd highlighted how AI could enhance the search for connections. Herd proposed using AI assistants, or AI dating concierges, to reach out to potential partners, thereby expanding the pool of people each user can communicate with and increasing the chances of finding a perfect match. highlighted how AI could enhance highlighted how AI could enhance “There is a world where your dating concierge could go and date for you with other dating concierges ... and then you don’t have to talk to 600 people,” Herd explained during her interview. Startups are exploring how AI can improve matchmaking. Some apps use neural networks trained to identify the exact type of people a user is attracted to, making the dating experience more enjoyable. Liesel Sharabi, an associate professor at Arizona State University, noted that AI could help reduce "dating burnout" by making matches more accurate. “Looking at AI, there are all kinds of possibilities for helping to make the decision fatigue a little bit less pronounced when you're just having to swipe through so many profiles, and also helping to make the matchmaking process better by just improving upon some of the algorithms that they've been using," Sharabi said . Sharabi said Sharabi said There is even an AI platform called Meeno that people can consult about their relationships. Meeno was founded by Renate Nyborg, a former CEO of Tinder. By creating Meeno, she aimed to address the increased level of loneliness among young people that occurred after the COVID-19 pandemic. Meeno was designed to become a chatting partner and an unbiased advisor to people who feel lonely. One of the users, Andrew, shared his experience of using Meeno with Time Magazine . According to Andrew, Meeno didn’t suggest anything groundbreaking, but just the process of chatting with the app and reading advice on the screen that confirmed his own instincts gave him the confidence he needed to act. experience of using Meeno with Time Magazine There is another platform where AI can give relationship advice. On YouMatch, the AI is trained to analyze people’s personality types and consider their behavior on the app to offer personalized advice on how they should navigate their romantic lives and deal with different situations. Beyond Romance: Facilitating Social Connections Beyond Romance: Facilitating Social Connections The Chinese dating app market is booming , largely due to features that promote social connections rather than just romance. New “social discovery” features help users find friends and acquaintances with similar interests and hobbies. Apps like Bumble offer BFF mode for finding friends and Bizz mode for networking, while Tinder's Friends of Friends feature helps users find potential friends. Chinese dating app market is booming Chinese dating app market is booming For many, dating apps have become virtual sanctuaries—a modern twist on the "third place,” a concept urbanists describe as a community space between work and home, The New York Times reports . These platforms allow users to explore hobbies, discuss popular topics, and meet new friends, helping to combat loneliness. reports reports Shift Toward Serious Dating In early 2023, Tinder released its international “ Green Flags Study ,” surveying 8,000 heterosexual men and women aged 18-34 across the U.S., U.K., Australia, and Canada. The study found that 53% of men and 68% of women are seeking a romantic relationship, indicating that a majority of singles using dating apps are looking for something serious. Green Flags Study Green Flags Study Hinge data shows that Gen Z singles are more clear and honest about their relationship intentions and expectations, with over half of users seeking a long-term, serious relationship with a deeper connection. This trend has led to a decline in “hookup culture”, which peaked with the rise of Tinder, and a growth in apps focused on serious relationships. apps focused on serious relationships. apps focused on serious relationships. To help users build deeper relationships, some startups rely on scientific findings and methodology. For example, YouMatch algorithms conduct each user's personality analysis based on the DISC (Dominance-Influence-Steadiness-Conscientiousness) methodology developed by American psychologist and Harvard graduate William Marston. After determining their personality type and assessing their values and goals, the app matches people with a higher level of personal compatibility, giving them more potential to build a common future. Prioritizing Users' Wellbeing and Safety Prioritizing Users' Wellbeing and Safety Young singles are increasingly prioritizing mental health and well-being. According to a Hinge survey , 97% of users prefer dating someone who looks after their mental health and 86% are more likely to agree to a second date if their partner is attending therapy. Nearly 60% of users reported taking time to focus on themselves before entering a relationship. survey To support users' well-being, dating apps share content on mental health and dating ethics. Bumble, for instance, publishes articles on avoiding ghosting and managing dating burnout . Safety features are also a priority; Tinder's "Share My Date” feature allows users to share their date plans with friends and family with one click, just to be on the safe side. avoiding ghosting avoiding ghosting dating burnout dating burnout Overall, promoting safe and respectful communication has become a central trend among both major and emerging dating apps.

AI and the Rise of Meaningful Connections: Current Dating App Market Trends

Nvidia has caught lightning in a bottle, and boy, is it making the company filthy rich. Investors eagerly awaiting the company's quarterly results were not disappointed after Nvidia said its sales had more than doubled and its profit was up sevenfold, propelling its valuation to record highs. Nvidia generated $26.04 billion in Q1 revenue, up 262% year over year, while net income of $14.88 billion was a jump of 628% from the $2.04 billion the company had posted in the same quarter last year. The company also said it was increasing its cash dividend by 150% to $0.10 per share. "The next industrial revolution has begun," declared Nvidia founder and Chief Executive Jensen Huang. Indeed, it has. Next quarter, Nvidia plans to generate even more in sales: $28 billion, plus or minus 2%. The kicker? The forecast was above what analysts thought the company would be able to generate: $26.66 billion. Nvidia is basing its revenue forecast on the success of the recently announced Blackwell class of AI chips, which are due to begin shipments this quarter. The company already believes that demand will outstrip supply. "We are poised for our next wave of growth. The Blackwell platform is in full production," Huang said. With so much positive news, it's no wonder the company's stock has hit a record high and crossed the $1,000 a share threshold. At this point, Nvidia's stock is so influential that it helped the technology-driven Nasdaq Composite Index to reach a record high! But that's not all; with yet another jump in the company's stock price, Nvidia is slowly inching towards a market capitalization of $3 trillion — only months after it became a $2 trillion company. The only other company valued at above $3 trillion is Microsoft, but the Windows maker did not achieve that milestone as quickly as Nvidia is. "This still doesn't feel like bubble territory," Derren Nathan, head of equity analysis at Hargreaves Lansdown, told Reuters. There is so much interest in Nvidia stock that the company has decided to make it easier for investors to own a piece of the pie. In its earnings announcement, Nvidia said it would be breaking down its shares at a ratio of 10-for-1 to make stock ownership "more accessible to employees and investors." What a great time to be Nvidia. Sponsored Content: Cash is king 👑 Not long ago, investors were chasing rocketship startups in hopes of a huge payday. Boy, how times have changed. Those overvalued, buzzy startups just aren't as attractive anymore. You know what's cool? Undervalued companies that earn real profit.WebStreet finds these companies, acquires them, improves them, and sells them. (And lets you invest) They buy overlooked businesses already earning money
They improve operations, so they make even more money
They bundle everything into a single, powerful fund that you can invest in. It's like micro private equity on autopilot. Unlike a lot of startups, WebStreet is doing great. Are you an accredited investor looking for passive income and want to invest in businesses providing real value? Join WebStreet’s Waitlist Here. Former OpenAI Employee Spills the Beans on Sam Altman Ouster 🤯 Sam Altman concealed the existence of ChatGPT from OpenAI's board. That's coming from former board member Helen Toner, who recently said in a podcast that Altman had a history of withholding information and lying to its members. The Microsoft-backed startup, which kickstarted the generative AI boom in 2022, went through its share of drama after the release of ChatGPT. Apparently, the OpenAI board only found out about the chatbot's existence after it became public on X, formerly Twitter. "After years of these kinds of things, all four of us who fired him came to the conclusion that we just couldn't believe things that Sam was telling us," Toner said. Worryingly, Sam also apparently gave the board inaccurate information on the safety processes for AI. All of this boiled over into the board deciding to kick Sam out, only for him to return just a few days later with Microsoft's blessing and kicking out the same people who fired him. "We are disappointed that Miss Toner continues to revisit these issues," OpenAI board chair Bret Taylor told the host of the podcast where Toner appeared. Meanwhile, on the safety front, OpenAI said it had formed a committee that would deliver recommendations on "critical safety and security decisions for OpenAI projects and operations." Altman will serve on the three-member board. For their first task, the OpenAI Safety and Security Committee will "evaluate and further develop OpenAI’s processes and safeguards over the next 90 days," the startup said in a statement. Despite the serious allegations, neither OpenAI nor Altman seem to be going anywhere. In fact, the company continues to partner with industry heavyweights such as News Corp to train its popular chatbot while attracting the likes of PwC to resell the enterprise version of ChatGPT. That is some serious backing. In Other News.. 📰 BlackRock Takes the Crown for the Largest Spot Bitcoin ETF From Grayscale — via CoinDesk
AI models have favorite numbers because they think they’re people — via TechCrunch
Prisoners are using VR to prepare them for life after release — via CNN
US court to hear challenges to potential TikTok ban in September — via Reuters
Sam Altman is starting to look a lot like Mark Zuckerberg — via Axios
China’s $47.5 billion chip fund will likely focus on AI amid U.S. export curbs — via CNBC And that's a wrap! Don't forget to share this newsletter with your family and friends! See y'all next week. PEACE! ☮️ — Sheharyar Khan, Editor, Business Tech @ HackerNoon *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. Tech, What the Heck!? is a once-weekly newsletter written by HackerNoon editors that combine HackerNoon's proprietary data with news-worthy tech stories from around the internet. Humorous and insightful, the newsletter recaps trending events that are shaping the world of tech. Subscribe here. Nvidia has caught lightning in a bottle, and boy, is it making the company filthy rich. Nvidia Nvidia Investors eagerly awaiting the company's quarterly results were not disappointed after Nvidia said its sales had more than doubled and its profit was up sevenfold , propelling its valuation to record highs. quarterly results quarterly results sevenfold Nvidia generated $26.04 billion in Q1 revenue, up 262% year over year, while net income of $14.88 billion was a jump of 628% from the $2.04 billion the company had posted in the same quarter last year. The company also said it was increasing its cash dividend by 150% to $0.10 per share. also "The next industrial revolution has begun," declared Nvidia founder and Chief Executive Jensen Huang. Indeed, it has. Next quarter, Nvidia plans to generate even more in sales: $28 billion, plus or minus 2%. The kicker? The forecast was above what analysts thought the company would be able to generate: $26.66 billion . more $26.66 billion $26.66 billion Nvidia is basing its revenue forecast on the success of the recently announced Blackwell class of AI chips, which are due to begin shipments this quarter. The company already believes that demand will outstrip supply. recently announced recently announced "We are poised for our next wave of growth. The Blackwell platform is in full production," Huang said. With so much positive news, it's no wonder the company's stock has hit a record high and crossed the $1,000 a share threshold. At this point, Nvidia's stock is so influential that it helped the technology-driven Nasdaq Composite Index to reach a record high! record high record high so influential so influential But that's not all; with yet another jump in the company's stock price, Nvidia is slowly inching towards a market capitalization of $3 trillion — only months after it became a $2 trillion company. inching towards inching towards $2 trillion $2 trillion The only other company valued at above $3 trillion is Microsoft , but the Windows maker did not achieve that milestone as quickly as Nvidia is. Microsoft Microsoft "This still doesn't feel like bubble territory," Derren Nathan, head of equity analysis at Hargreaves Lansdown, told Reuters. told told There is so much interest in Nvidia stock that the company has decided to make it easier for investors to own a piece of the pie. In its earnings announcement, Nvidia said it would be breaking down its shares at a ratio of 10-for-1 to make stock ownership "more accessible to employees and investors." What a great time to be Nvidia. Sponsored Content: Sponsored Content: Cash is king 👑 Cash is king 👑 Not long ago, investors were chasing rocketship startups in hopes of a huge payday. Boy, how times have changed. Those overvalued, buzzy startups just aren't as attractive anymore. You know what's cool? Undervalued companies that earn real profit.WebStreet finds these companies, acquires them, improves them, and sells them. (And lets you invest) They buy overlooked businesses already earning money They improve operations, so they make even more money They bundle everything into a single, powerful fund that you can invest in. It's like micro private equity on autopilot. They buy overlooked businesses already earning money They improve operations, so they make even more money They bundle everything into a single, powerful fund that you can invest in. It's like micro private equity on autopilot. Unlike a lot of startups, WebStreet is doing great. Are you an accredited investor looking for passive income and want to invest in businesses providing real value? Join WebStreet’s Waitlist Here . Join WebStreet’s Waitlist Here Former OpenAI Employee Spills the Beans on Sam Altman Ouster 🤯 Former OpenAI Employee Spills the Beans on Sam Altman Ouster 🤯 Sam Altman concealed the existence of ChatGPT from OpenAI's board. That's coming from former board member Helen Toner, who recently said in a podcast that Altman had a history of withholding information and lying to its members. recently said in a podcast recently said in a podcast The Microsoft -backed startup, which kickstarted the generative AI boom in 2022, went through its share of drama after the release of ChatGPT. Apparently, the OpenAI board only found out about the chatbot's existence after it became public on X, formerly Twitter. Microsoft Microsoft "After years of these kinds of things, all four of us who fired him came to the conclusion that we just couldn't believe things that Sam was telling us," Toner said. Worryingly, Sam also apparently gave the board inaccurate information on the safety processes for AI. All of this boiled over into the board deciding to kick Sam out, only for him to return just a few days later with Microsoft's blessing and kicking out the same people who fired him. kicking out kicking out "We are disappointed that Miss Toner continues to revisit these issues," OpenAI board chair Bret Taylor told the host of the podcast where Toner appeared. told told Meanwhile, on the safety front, OpenAI said it had formed a committee that would deliver recommendations on "critical safety and security decisions for OpenAI projects and operations." Altman will serve on the three-member board. For their first task, the OpenAI Safety and Security Committee will "evaluate and further develop OpenAI’s processes and safeguards over the next 90 days," the startup said in a statement . statement statement Despite the serious allegations, neither OpenAI nor Altman seem to be going anywhere. In fact, the company continues to partner with industry heavyweights such as News Corp to train its popular chatbot while attracting the likes of PwC to resell the enterprise version of ChatGPT. News Corp News Corp resell resell That is some serious backing. In Other News.. 📰 In Other News.. 📰 BlackRock Takes the Crown for the Largest Spot Bitcoin ETF From Grayscale — via CoinDesk AI models have favorite numbers because they think they’re people — via TechCrunch Prisoners are using VR to prepare them for life after release — via CNN US court to hear challenges to potential TikTok ban in September — via Reuters Sam Altman is starting to look a lot like Mark Zuckerberg — via Axios China’s $47.5 billion chip fund will likely focus on AI amid U.S. export curbs — via CNBC BlackRock Takes the Crown for the Largest Spot Bitcoin ETF From Grayscale — via CoinDesk CoinDesk CoinDesk AI models have favorite numbers because they think they’re people — via TechCrunch TechCrunch TechCrunch Prisoners are using VR to prepare them for life after release — via CNN CNN CNN US court to hear challenges to potential TikTok ban in September — via Reuters Reuters Reuters Sam Altman is starting to look a lot like Mark Zuckerberg — via Axios Axios Axios China’s $47.5 billion chip fund will likely focus on AI amid U.S. export curbs — via CNBC CNBC CNBC And that's a wrap! Don't forget to share this newsletter with your family and friends! See y'all next week. PEACE! ☮️ — Sheharyar Khan , Editor, Business Tech @ HackerNoon Sheharyar Khan Sheharyar Khan *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. Tech Company Rankings Tech Company Rankings Tech, What the Heck!? is a once-weekly newsletter written by HackerNoon editors that combine HackerNoon's proprietary data with news-worthy tech stories from around the internet. Humorous and insightful, the newsletter recaps trending events that are shaping the world of tech. Subscribe here. Tech, What the Heck!? is a once-weekly newsletter written by HackerNoon editors that combine HackerNoon's proprietary data with news-worthy tech stories from around the internet. Humorous and insightful, the newsletter recaps trending events that are shaping the world of tech. Subscribe here. Tech, What the Heck!? is a once-weekly newsletter written by HackerNoon editors that combine HackerNoon's proprietary data with news-worthy tech stories from around the internet. Humorous and insightful, the newsletter recaps trending events that are shaping the world of tech. Subscribe here. Tech, What the Heck!? is a once-weekly newsletter written by HackerNoon editors that combine HackerNoon's proprietary data with news-worthy tech stories from around the internet. Humorous and insightful, the newsletter recaps trending events that are shaping the world of tech. Subscribe here . here here

Nvidia Just Became a Whole Lot More Valuable

এনভিডিয়া সবেমাত্র অনেক বেশি মূল্যবান হয়ে উঠেছে

Nvidia ist gerade um einiges wertvoller geworden

Nvidia acaba de volverse mucho más valiosa

Nvidia est devenu beaucoup plus précieux

एनवीडिया अब और भी अधिक मूल्यवान हो गया है

Nvidiaの価値がさらに高まった

Nvidia는 훨씬 더 가치 있게 되었습니다.

Nvidia acaba de se tornar muito mais valiosa

Nvidia стала намного более ценной

Nvidia Artık Çok Daha Değerli Hale Geldi

Nvidia vừa trở nên có giá trị hơn rất nhiều

Nvidia 的价值大幅提升

This paper is available on arxiv under CC 4.0 license. Authors: (1) Pietro Ghezzi, Brighton & Sussex Medical School, Falmer, Brighton, UK; (2) Peter G Bannister, Brighton & Sussex Medical School, Falmer, Brighton, UK; (3) Gonzalo Casino, Communication Department, Pompeu Fabra University, Barcelona, Spain and Iberoamerican Cochrane Center, Barcelona, Spain; (4) Alessia Catalani, Department of Biomolecular Sciences, University of Urbino Carlo Bo, Urbino, PU, Italy; (5) Michel Goldman, Institute for Interdisciplinary Innovation in healthcare (I3h), Université libre de Bruxelles; (6) Jessica Morley, Oxford Internet Institute, University of Oxford, Oxford, UK; (7) Marie Neunez, Institute for Interdisciplinary Innovation in healthcare (I3h), Université libre de Bruxelles; (8) Andreu Prados, Communication Department, Pompeu Fabra University, Barcelona, Spain, Iberoamerican Cochrane Center, Barcelona, Spain, Department of Biomolecular Sciences, University of Urbino Carlo Bo, Urbino, PU, Italy, Institute for Interdisciplinary Innovation in healthcare (I3h), Université libre de Bruxelles, Oxford Internet Institute, University of Oxford, Oxford, UK, and Blanquerna School of Health Sciences, Universitat Ramon Llull, Barcelona, Spain; (9) Mariarosaria Taddeo, Oxford Internet Institute, University of Oxford, Oxford, UK, Blanquerna School of Health Sciences, Universitat Ramon Llull, Barcelona, Spain, and The Alan Turing Institute, London, UK; (10) Tania Vanzolini, Department of Biomolecular Sciences, University of Urbino Carlo Bo, Urbino, PU, Italy; (11) Luciano Floridi, Oxford Internet Institute, University of Oxford, Oxford, UK, Blanquerna School of Health Sciences, Universitat Ramon Llull, Barcelona, Spain, and The Alan Turing Institute, London, UK. Table of Links Abstract and Introduction Methods Web search and content analysis Results Discussion Conclusion and References Table 1. Example of classification of webpages Table 2. Percentage of vaccine-negative webpages in different SERPs Conclusion Our analysis shows that while it may well be technically possible to design a search engine that manages to balance privacy-preservation with the promotion of high IQ material, this is currently not the case. The current relationship between privacy-preserving design features of search engines and the IQ of the results they return is inverse (although not proportionally). In instances where this can have a negative impact on public health, as in the example we have provided of the promotion of anti-vaccine misinformation, not intervening to alter the design of the algorithm even if this means sacrificing some degree of user privacy - can lead to severe harm for a large population of user and is, therefore, unethical. Designing a search engine that is privacy savvy and avoids issues with filter bubbles that can result from user-tracking may be a good thing, like designing a car with an engine that does not pollute and is inexpensive to run, and designers should seek to balance the different aspects of search engine design highlighted in Figure 3. However, if the brakes in an environmentally-designed car do not work, the car is unsafe and this negates the positive ethical decisions made by the designers. In a car this is a highly unlikely design outcome, as a car has to undergo several rounds of testing by regulatory agencies before being allowed on the market. This is not yet the case for search engines, which are only regulated from the perspective of data protection – which is primarily interpreted as data security rather than data privacy. Our study suggests that this is necessary but insufficient, and instead mechanisms should be developed to test search engines from the perspective of IQ, (particularly for YMYL webpages) before they can be deemed trustworthy providers of public health information. References 1. World Health Organization. Ten threats to global health in 2019. 2019. https://www.who.int/emergencies/ten-threats-to-global-health-in-2019 (archived at https://web.archive.org/web/20190627111040/https://www.who.int/emergencies/ten-threats-toglobal-health-in-2019) (accessed 27/06/2019 2. Wakefield AJ, Murch SH, Anthony A, et al. Ileal-lymphoid-nodular hyperplasia, non-specific colitis, and pervasive developmental disorder in children. Lancet 1998; 351(9103): 637-41. 3. Stahl JP, Cohen R, Denis F, et al. The impact of the web and social networks on vaccination. New challenges and opportunities offered to fight against vaccine hesitancy. Med Mal Infect 2016; 46(3): 117-22. 4. Martin GP, Sutton E, Willars J, Dixon-Woods M. Frameworks for change in healthcare organisations: a formative evaluation of the NHS Change Model. Health Serv Manage Res 2013; 26(2-3): 65-75. 5. Illari P, Floridi L. Information quality, data and philosophy. The Philosophy of Information Quality: Springer; 2014: 5-23. 6. Al-Jefri M, Evans R, Uchyigit G, Ghezzi P. What is health information quality? Ethical dimension and perception by users. Frontiers in medicine 2018; 5. 7. Norman CD, Skinner HA. eHEALS: The eHealth Literacy Scale. J Med Internet Res 2006; 8(4): e27. 8. Chen X, Hay JL, Waters EA, et al. Health Literacy and Use and Trust in Health Information. J Health Commun 2018; 23(8): 724-34. 9. Paige SR, Krieger JL, Stellefson ML. The Influence of eHealth Literacy on Perceived Trust in Online Health Communication Channels and Sources. J Health Commun 2017; 22(1): 53-65. 10. Kim J, Park HA. Development of a health information technology acceptance model using consumers' health behavior intention. J Med Internet Res 2012; 14(5): e133. 11. Borah P, Xiao X. The Importance of 'Likes': The Interplay of Message Framing, Source, and Social Endorsement on Credibility Perceptions of Health Information on Facebook. J Health Commun 2018; 23(4): 399-411. 12. Huo C, Zhang M, Ma F. Factors influencing people’s health knowledge adoption in social media: the mediating effect of trust and the moderating effect of health threat. Library Hi Tech 2018; 36(1): 129-51. 13. Chiou L, Tucker C. Search engines and data retention: Implications for privacy and antitrust: National Bureau of Economic Research, 2017. 14. Esteve A. The business of personal data: Google, Facebook, and privacy issues in the EU and the USA. International Data Privacy Law 2017; 7(1): 36-47. 15. Floridi L, Taddeo M. What is data ethics? : The Royal Society; 2016. 16. Moskvitch K. Amazon’s Alexa deal with the NHS can help patients but at what cost? Wired, 2019. (accessed 8/11/2019). 17. Arif N, Al-Jefri M, Bizzi IH, et al. Fake News or Weak Science? Visibility and Characterization of Antivaccine Webpages Returned by Google in Different Languages and Countries. Front Immunol 2018; 9: 1215. 18. Maki A, Evans R, Ghezzi P. Bad news. Analysis of the quality of information on influenza prevention returned by Google in English and Italian. Frontiers in Immunology 2015; 6:616. doi: 10.3389/fimmu.2015.00616. 19. Fleiss JL, Levin B, Paik MC. Statistical methods for rates and proportions. Hoboken, NJ, USA: John Wiley & Sons; 2013. 20. Economist T. Seek and you shall find. Google rewards reputable reporting, not left-wing politics. The Economist 2019; 431(9146): 85. 21. Taddeo M, Floridi L. The Debate on the Moral Responsibilities of Online Service Providers. Science and engineering ethics 2015: 1-29. 22. Mittelstadt BD, Allo P, Taddeo M, Wachter S, Floridi L. The ethics of algorithms: Mapping the debate. Big Data & Society 2016; 3(2): 2053951716679679. 23. Association for Computing Machinery. ACM Code of Ethics and Professional Conduct. 2018. https://www.acm.org/code-of-ethics (archived at https://web.archive.org/web/20190627114921/https://www.acm.org/code-of-ethics) (accessed 27/06/2019 2019). 24. Tufekci Z. Algorithmic harms beyond Facebook and Google: Emergent challenges of computational agency. Colo Tech LJ 2015; 13: 203. 25. Hasnas J. Teaching business ethics: The principles approach. Journal of Business Ethics Education 2013; 10: 275-304. 26. Macdonald C, Gavura S. Alternative Medicine and the Ethics Of Commerce. Bioethics 2016; 30(2): 77-84. 27. Taddeo M, Floridi L. The Debate on the Moral Responsibilities of Online Service Providers. Sci Eng Ethics 2016; 22(6): 1575-603. 28. Chigwedere P, Seage GR, 3rd, Gruskin S, Lee TH, Essex M. Estimating the lost benefits of antiretroviral drug use in South Africa. J Acquir Immune Defic Syndr 2008; 49(4): 410-5. 29. Ouyang Y. Student's death highlights gaps in China's health regulations. Lancet Oncol 2016; 17(6): 709. 30. Browne M, Thomson P, Rockloff MJ, Pennycook G. Going against the Herd: Psychological and Cultural Factors Underlying the 'Vaccination Confidence Gap'. PLoS One 2015; 10(9): e0132562. 31. Bean SJ. Emerging and continuing trends in vaccine opposition website content. Vaccine 2011; 29(10): 1874-80.

Online Information of Vaccines: Conclusion and References

AI/ML is a wildfire trend. It’s being integrated into just about every application you can think of. Compared to other technical innovations over the past years—like blockchain—infrastructure and tooling were being built (and still are) in parallel with discovering a meaningful application. AI/ML, on the other hand, has seemingly jumped straight to real-world application, leaving tooling and infrastructure largely lacking in comparison to adoption. We’ve built a lot of tools for developing and tuning models, but moving them into production is relatively new, and the few MLOps tools that do exist were built before AI/ML became all the rage. At some point, we have to stop and ask ourselves if we’re building long-term solutions or just bootstrapping our way to production. In my opinion, we’re standing at a crossroads—specifically, a crossroads of DevOps and MLOps. Like DevOps 10 years ago, MLOps is buzzy, and tech companies are racing to incorporate these practices into their workflows. However, the more I dig into it, the more I come to believe that MLOps is a momentary trend. I find it hard to believe that the future lies in further segmentation of XOps practices but in a return to a unified approach under the well-established (and adopted) practices of DevOps. The model handoff conundrum A major stumbling block in the current AI/ML workflow is the model handoff process. Moving a model from a development stage (typically in a Jupyter Notebook) to an ML tool, then onto a development server, and finally to a production environment like Kubernetes is…let’s say, challenging at best. Each of these transitions requires repackaging the model to suit the target environment. This is not just a pain for the team (usually DevOps) that owns everything to the right of training (see diagram below) but introduces significant risk and delay into the deployment cycle. If that’s not enough, the diversity of packaging mechanisms without a standardized procedure amplifies these issues, turning what we’ve come to enjoy as a straightforward process into a real “pain in the ops.” So we decided to do something about it. We had a crazy idea–Why not package models into a format that we’ve already standardized on? Back in 2013, a little company called Docker made it really easy to start using containers to package up applications. A big key to their success was the OCI ( ), an industry-wide initiative to have standards around how we package up our applications. Because of OCI standards, we have hundreds (maybe thousands?) of tools that can be combined to manage and deploy applications. So why aren’t we using this to package Notebooks and AI models as well? It would make deploying, sharing, and managing our models easier for everyone involved. you can learn about that here From Notebook to OCI-standard file type As I said earlier, we set out to do something about how we’re packaging up our models, and what we did was build Kit. Kit is that packages your model, datasets, code, and configuration so data scientists and developers can use their preferred tools while collaborating effortlessly. an open source MLOps project Our goal is to standardize the packaging, reproduction, deployment, and tracking of AI/ML models. We want models to be managed with the same agility as application code. Kit streamlines the deployment process and ensures models can be executed universally with minimal friction. E.g., when you hand a model to the DevOps team, it’s already good to go, meaning they can deploy it to prod! Your tools have been standardized, and best of all, you didn’t have to learn how to write a Docker or Kubernetes file. The Kit CLI packages up all of the assets/resources included in your Notebook and turns them into a ModelKit, to make handoffs between ML engineers using Jupyter Notebooks and application teams easy, letting both teams work with the tools they already use and love. . Checkout a video of the Kit CLI here KitOps' philosophy is centered around improving collaboration without requiring an overhaul of existing processes. By building on industry standards, we facilitate cross-team engagement beyond merely data scientists in the model development process. The compatibility of ModelKits and Kitfiles with current tools eliminates the need for significant modifications to established deployment pipelines or endpoints. Unmatched model traceability and reproducibility KitOps's approach to model traceability and reproducibility is different from the common practice of simply using a Docker file. To be clear, there’s nothing wrong with using Docker files, however, including your hyperparameters and data sets in a container are not a practical approach. Kit provides a unified package for everything you need to reproduce, integrate with, or test the model throughout its lifecycle in a modular package that gives users the flexibility to extract specific components of a ModelKit, such as the dataset or the model itself, or to retrieve the entire package. Furthermore, the storage of ModelKits within an organization’s registry facilitates a detailed record of state changes, crucial for auditing purposes. And, since ModelKits are OCI-compliant, you can use the registries that you’ve already adopted (Docker Hub, Amazon ECR, Artifactory, Quay, etc). In addition, ModelKits are ideally suited for secure bill-of-materials (SBOM) initiatives, addressing the growing concerns regarding security and compliance in AI/ML model deployment. Something that is becoming increasingly important. The inevitability of DevOps integration We believe that KitOps signifies a critical moment for AI/MLOps and DevOps. By addressing the issues plaguing model deployment and collaboration, KitOps paves the way for an era where the delineation between MLOps and DevOps becomes increasingly indistinct. To be clear, I’m not saying that the existing MLOps practices are not valuable; rather, I’m advocating that the foundational practices and tools being implemented in our DevOps workflows are flexible enough to incorporate the requisites of AI/ML deployment. We're still early in the development process and are really interested in collecting community feedback. You can learn more about KitOps at https://kitops.ml And check out the source code here: https://github.com/jozu-ai/kitops Also published . here

KitOps: Bridging the Gap Between AI/ML and DevOps with Standardized Packaging

This paper is available on arxiv under CC 4.0 license. Authors: (1) Samie Mostafavi, ssmos, KTH Royal Institute of Technology; (2) Vishnu Narayanan Moothedath, vnmo, KTH Royal Institute of Technology; (3) Stefan Ronngren, steron, KTH Royal Institute of Technology; (4) Neelabhro Roy, §nroy, KTH Royal Institute of Technology; (5) Gourav Prateek Sharma, gpsharma, KTH Royal Institute of Technology; (6) Sangwon Seo, sangwona, KTH Royal Institute of Technology; (7) Manuel Olgu´ın Munoz, manual@olguinmunoz.xyz, KTH Royal Institute of Technology; (8) James Gross, jamesgr, KTH Royal Institute of Technology. Table of Links Abstract & Introduction Testbed Design and Architecture Experimental Testbed Validation Supported Experimentation Acknowledgements & References V. ACKNOWLEDGEMENTS This research has been partially funded by (1) the VINNOVA Competence Center for Trustworthy Edge Computing Systems and Applications (TECoSA) at KTH Royal Institute of Technology; and (2) the Swedish Foundation for Strategic Research (SSF), through grant number ITM17–0246. REFERENCES [1] D. Raychaudhuri, I. Seskar, G. Zussman, T. Korakis, D. Kilper, T. Chen, J. Kolodziejski, M. Sherman, Z. Kostic, X. Gu, H. Krishnaswamy, S. Maheshwari, P. Skrimponis, and C. Gutterman, Challenge: COSMOS: A City-Scale Programmable Testbed for Experimentation with Advanced Wireless. New York, NY, USA: Association for Computing Machinery, 2020. [2] J. Yu, T. Chen, C. Gutterman, S. Zhu, G. Zussman, I. Seskar, and D. Kilper, “Cosmos: Optical architecture and prototyping,” in 2019 Optical Fiber Communications Conference and Exhibition (OFC), pp. 1– 3, 2019. [3] J. Breen, A. Buffmire, J. Duerig, K. Dutt, E. Eide, M. Hibler, D. Johnson, S. K. Kasera, E. Lewis, D. Maas, A. Orange, N. Patwari, D. Reading, R. Ricci, D. Schurig, L. B. Stoller, J. Van der Merwe, K. Webb, and G. Wong, “POWDER: Platform for open wireless data-driven experimental research,” in Proceedings of the 14th International Workshop on Wireless Network Testbeds, Experimental Evaluation and Characterization (WiNTECH), Sept. 2020. [4] H. Zhang, Y. Guan, A. Kamal, D. Qiao, M. Zheng, A. Arora, O. Boyraz, B. Cox, T. Daniels, M. Darr, et al., “Ara: A wireless living lab vision for smart and connected rural communities,” in Proceedings of the 15th ACM Workshop on Wireless Network Testbeds, Experimental evaluation & CHaracterization, pp. 9–16, 2022. [5] K. Keahey, J. Anderson, Z. Zhen, P. Riteau, P. Ruth, D. Stanzione, M. Cevik, J. Colleran, H. S. Gunawi, C. Hammock, J. Mambretti, A. Barnes, F. Halbach, A. Rocha, and J. Stubbs, “Lessons learned from the Chameleon Testbed,” in Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20), USENIX Association, July 2020. [6] K. Keahey, J. Anderson, M. Sherman, C. Hammock, Z. Zhen, J. Tillotson, T. Bargo, L. Long, T. Ul Islam, S. Babu, H. Zhang, and F. Halbach, “Chi-in-a-box: Reducing operational costs of research testbeds,” in Practice and Experience in Advanced Research Computing, PEARC ’22, (New York, NY, USA), Association for Computing Machinery, 2022. [7] S. George, T. Eiszler, R. Iyengar, H. Turki, Z. Feng, J. Wang, P. Pillai, and M. Satyanarayanan, “Openrtist: End-to-end benchmarking for edge computing,” IEEE Pervasive Computing, vol. 19, no. 4, pp. 10–18, 2020. [8] X.-M. Zhang, Q.-L. Han, X. Ge, D. Ding, L. Ding, D. Yue, and C. Peng, “Networked control systems: a survey of trends and techniques,” IEEE/CAA Journal of Automatica Sinica, vol. 7, no. 1, pp. 1–17, 2020. [9] M. Olgu´ın Munoz, ˜ An Emulation-Based Performance Evaluation Methodology for Edge Computing and Latency Sensitive Applications. PhD thesis, KTH Royal Institute of Technology, 2023. [10] M. Olgu´ın Munoz, N. Roy, and J. Gross, “CLEAVE: Scalable and edge- ˜ native benchmarking of networked control systems,” in Proceedings of the 5th International Workshop on Edge Systems, Analytics and Networking, EdgeSys ’22, (New York, NY, USA), pp. 37–42, Association for Computing Machinery, 2022. [11] K. Ha, Z. Chen, W. Hu, W. Richter, P. Pillai, and M. Satyanarayanan, “Towards wearable cognitive assistance,” in Proc. of the 12th Annual International Conference on Mobile Systems, Applications, and Services, p. 68–81, Association for Computing Machinery, 2014. [12] B. Lu, Y. Li, X. Wu, and Z. Yang, “A review of recent advances in wind turbine condition monitoring and fault diagnosis,” in IEEE Power Electronics and Machines in Wind Applications, pp. 1–7, 2009. [13] T.-H. Oh, R. Jaroensri, C. Kim, M. Elgharib, F. Durand, W. T. Freeman, and W. Matusik, “Learning-based video motion magnification,” in Proceedings of the European Conference on Computer Vision (ECCV), pp. 633–648, 2018. [14] C. Liu, A. Torralba, W. T. Freeman, F. Durand, and E. H. Adelson, “Motion magnification,” ACM transactions on graphics (TOG), vol. 24, no. 3, pp. 519–526, 2005. [15] G. P. Sharma, D. Patel, J. Sachs, M. De Andrade, J. Farkas, J. Harmatos, B. Varga, H.-P. Bernhard, R. Muzaffar, M. K. Atiq, et al., “Towards Deterministic Communications in 6G Networks: State of the Art, Open Challenges and the Way Forward,” arXiv preprint arXiv:2304.01299, 2023. [16] S. Mostafavi, G. P. Sharma, and J. Gross, “Data-Driven Latency Probability Prediction for Wireless Networks: Focusing on Tail Probabilities,” arXiv preprint arXiv:2307.10648, 2023. [17] M. Skocaj, F. Conserva, N. S. Grande, A. Orsi, D. Micheli, G. Ghinamo, S. Bizzarri, and R. Verdone, “Data-driven predictive latency for 5g: A theoretical and experimental analysis using network measurements,” arXiv preprint arXiv:2307.02329, 2023. [18] V. N. Moothedath, J. P. Champati, and J. Gross, “Online algorithms for hierarchical inference in deep learning applications at the edge,” 2023. [19] G. Al-Atat, A. Fresa, A. P. Behera, V. N. Moothedath, J. Gross, and J. P. Champati, “The case for hierarchical deep learning inference at the network edge,” in Proceedings of the 1st International Workshop on Networked AI Systems, NetAISys ’23, (New York, NY, USA), Association for Computing Machinery, 2023. [20] B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y. Arcas, “Communication-Efficient Learning of Deep Networks from Decentralized Data,” in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, vol. 54 of Proceedings of Machine Learning Research, pp. 1273–1282, PMLR, 20–22 Apr 2017. [21] H. Gao, A. Xu, and H. Huang, “On the Convergence of CommunicationEfficient Local SGD for Federated Learning,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 7510–7518, May 2021. [22] J. Perazzone, S. Wang, M. Ji, and K. S. Chan, “Communication-efficient device scheduling for federated learning using stochastic optimization,” in IEEE INFOCOM 2022 - IEEE Conference on Computer Communications, pp. 1449–1458, 2022.

ExPECA: An Experimental Platform for Trustworthy: Acknowledgements & References

This paper is available on arxiv under CC BY-SA 4.0 DEED license. Authors: (1) Mathias-Felipe de-Lima-Santos, Faculty of Humanities, University of Amsterdam, Institute of Science and Technology, Federal University of São Paulo; (2) Wilson Ceron, Institute of Science and Technology, Federal University of São Paulo. Table of Links Abstract & Introduction Data and Methods Results Discussion and Conclusions Funding and References Funding This project was partially funded by the University of Amsterdam’s RPA Human(e) AI and by the European Union’s Horizon 2020 research and innovation programs No 951911 (AI4Media). References (1) Sunstein, C. R. #Republic: Divided Democracy in the Age of Social Media; Princeton University Press: Princeton, New Jersey, 2017. (2) Garimella, K.; De Francisci Morales, G.; Gionis, A.; Mathioudakis, M. Political Discourse on Social Media. In Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW ’18; ACM Press: New York, New York, USA, 2018; Vol. 2, pp 913–922. https://doi.org/10.1145/3178876.3186139. (3) Del Vicario, M.; Bessi, A.; Zollo, F.; Petroni, F.; Scala, A.; Caldarelli, G.; Stanley, H. E.; Quattrociocchi, W. The Spreading of Misinformation Online. Proc. Natl. Acad. Sci. 2016, 113 (3), 554–559. https://doi.org/10.1073/pnas.1517441113. (4) Garrett, R. K. Echo Chambers Online?: Politically Motivated Selective Exposure among Internet News Users. J. Comput. Commun. 2009, 14 (2), 265–285. https://doi.org/10.1111/j.1083- 6101.2009.01440.x. (5) Nickerson, R. S. Confirmation Bias: A Ubiquitous Phenomenon in Many Guises. Rev. Gen. Psychol. 1998, 2 (2), 175–220. https://doi.org/10.1037/1089-2680.2.2.175. (6) Biehl, J. A Political Economy of Pharmaceuticals. Will to Live 2021, 18 (1), 10–13. https://doi.org/10.2307/j.ctv1wmz48h.5. (7) Allcott, H.; Gentzkow, M. Social Media and Fake News in the 2016 Election. J. Econ. Perspect. 2017, 31 (2), 211–236. https://doi.org/10.1257/jep.31.2.211. (8) Bennett, W. L.; Livingston, S. The Disinformation Order: Disruptive Communication and the Decline of Democratic Institutions. Eur. J. Commun. 2018, 33 (2), 122–139. https://doi.org/10.1177/0267323118760317. (9) Giglietto, F.; Righetti, N.; Rossi, L.; Marino, G. It Takes a Village to Manipulate the Media: Coordinated Link Sharing Behavior during 2018 and 2019 Italian Elections. Inf. Commun. Soc. 2020, 23 (6), 867–891. https://doi.org/10.1080/1369118X.2020.1739732. (10) Farkas, J.; Schou, J. Fake News as a Floating Signifier: Hegemony, Antagonism and the Politics of Falsehood. Javnost 2018, 25 (3), 298–314. https://doi.org/10.1080/13183222.2018.1463047. (11) Wardle, C. The Need for Smarter Definitions and Practical, Timely Empirical Research on Information Disorder. Digit. Journal. 2018, 6 (8), 951–963. https://doi.org/10.1080/21670811.2018.1502047. (12) Abramowitz, S.; McKune, S. L.; Fallah, M.; Monger, J.; Tehoungue, K.; Omidian, P. A. The Opposite of Denial: Social Learning at the Onset of the Ebola Emergency in Liberia. J. Health Commun. 2017, 22 (sup1), 59–65. https://doi.org/10.1080/10810730.2016.1209599. (13) Guidry, J. P. D.; Jin, Y.; Orr, C. A.; Messner, M.; Meganck, S. Ebola on Instagram and Twitter: How Health Organizations Address the Health Crisis in Their Social Media Engagement. Public Relat. Rev. 2017, 43 (3), 477–486. https://doi.org/10.1016/j.pubrev.2017.04.009. (14) Guidry, J. P. D.; Meganck, S. L.; Perrin, P. B.; Messner, M.; Lovari, A.; Carlyle, K. E. #Ebola: Tweeting and Pinning an Epidemic. Atl. J. Commun. 2021, 29 (2), 79–92. https://doi.org/10.1080/15456870.2019.1707202. (15) Smallman, S. Whom Do You Trust? Doubt and Conspiracy Theories in the 2009 Influenza Pandemic. J. Int. Glob. Stud. 2015, 6 (2), 1–24. (16) Wagner-Egger, P.; Bangerter, A.; Gilles, I.; Green, E.; Rigaud, D.; Krings, F.; Staerklé, C.; Clémence, A. Lay Perceptions of Collectives at the Outbreak of the H1n1 Epidemic: Heroes, Villains and Victims. Public Underst. Sci. 2011, 20 (4), 461–476. https://doi.org/10.1177/0963662510393605. (17) Arif, N.; Al-Jefri, M.; Bizzi, I. H.; Perano, G. B.; Goldman, M.; Haq, I.; Chua, K. L.; Mengozzi, M.; Neunez, M.; Smith, H.; Ghezzi, P. Fake News or Weak Science? Visibility and Characterization of Antivaccine Webpages Returned by Google in Different Languages and Countries. Front. Immunol. 2018, 9 (JUN), 1215. https://doi.org/10.3389/fimmu.2018.01215. (18) Dubé, E.; Vivion, M.; MacDonald, N. E. Vaccine Hesitancy, Vaccine Refusal and the AntiVaccine Movement: Influence, Impact and Implications. Expert Rev. Vaccines 2014, 14 (1), 99– 117. https://doi.org/10.1586/14760584.2015.964212. (19) West, J. D.; Bergstrom, C. T. Misinformation in and about Science. Proc. Natl. Acad. Sci. U. S. A. 2021, 118 (15), e1912444117. https://doi.org/10.1073/pnas.1912444117. (20) Hornsey, M. J.; Harris, E. A.; Fielding, K. S. The Psychological Roots of Anti-Vaccination Attitudes: A 24-Nation Investigation. Heal. Psychol. 2018, 37 (4), 307–315. https://doi.org/10.1037/hea0000586. (21) Ceron, W.; De-Lima-Santos, M.-F.; Quiles, M. G. Fake News Agenda in the Era of COVID-19: Identifying Trends through Fact-Checking Content. Online Soc. Networks Media 2021, 21, 100116. https://doi.org/10.1016/j.osnem.2020.100116. (22) Ceron, W.; Sanseverino, G. G.; De-Lima-Santos, M.-F.; Quiles, M. G. COVID-19 Fake News Diffusion across Latin America. Soc. Netw. Anal. Min. 2021, 11 (1), 47. https://doi.org/10.1007/s13278-021-00753-z. (23) Chadwick, A.; Kaiser, J.; Vaccari, C.; Freeman, D.; Lambe, S.; Loe, B. S.; Vanderslott, S.; Lewandowsky, S.; Conroy, M.; Ross, A. R. N.; Innocenti, S.; Pollard, A. J.; Waite, F.; Larkin, M.; Rosebrock, L.; Jenner, L.; McShane, H.; Giubilini, A.; Petit, A.; Yu, L. M. Online Social Endorsement and Covid-19 Vaccine Hesitancy in the United Kingdom. Soc. Media Soc. 2021, 7 (2), 205630512110088. https://doi.org/10.1177/20563051211008817. (24) Monari, A. C. P.; Sacramento, I. A “Vacina Chinesa de João Doria”: A Influência Da Disputa Política-Ideológica Na Desinformação Sobre a Vacinação Contra a Covid-19. Rev. Mídia e Cotid. 2021, 15 (3), 125–143. (25) Bode, L.; Vraga, E. The Swiss Cheese Model for Mitigating Online Misinformation. Bull. At. Sci. 2021, 77 (3), 129–133. https://doi.org/10.1080/00963402.2021.1912170. (26) Gera, S.; Sinha, A. C-ANN: A Deep Leaning Model for Detecting Black-Marketed Colluders in Twitter Social Network. Neural Comput. Appl. 2022, 34 (18), 15113–15127. https://doi.org/10.1007/s00521-021-06756-3. (27) Gleicher, N. Coordinated inauthentic behavior explained https://about.fb.com/news/2018/12/inside-feed-coordinated-inauthenticbehavior/%0Ahttps://newsroom.fb.com/news/2018/12/inside-feed-coordinated-inauthenticbehavior/ (accessed Oct 11, 2021). (28) Broniatowski, D. A. Towards Statistical Foundations for Detecting Coordinated Inauthentic Behavior on Facebook; Washington, DC, 2021. (29) Freelon, D.; Wells, C. Disinformation as Political Communication. Polit. Commun. 2020, 37 (2), 145–156. https://doi.org/10.1080/10584609.2020.1723755. (30) Keller, F. B.; Schoch, D.; Stier, S.; Yang, J. H. Political Astroturfing on Twitter: How to Coordinate a Disinformation Campaign. Polit. Commun. 2020, 37 (2), 256–280. https://doi.org/10.1080/10584609.2019.1661888. (31) Yang, K. C.; Pierri, F.; Hui, P. M.; Axelrod, D.; Torres-Lugo, C.; Bryden, J.; Menczer, F. The COVID-19 Infodemic: Twitter versus Facebook. Big Data Soc. 2021, 8 (1). https://doi.org/10.1177/20539517211013861. (32) Kim, Y.; Song, D.; Lee, Y. J. #Antivaccination on Instagram: A Computational Analysis of Hashtag Activism through Photos and Public Responses. Int. J. Environ. Res. Public Health 2020, 17 (20), 1–20. . https://doi.org/10.3390/ijerph17207550 (33) Möller, J. Filter Bubbles and Digital Echo Chambers. In The Routledge Companion to Media
Disinformation and Populism; Tumber, H., Waisbord, S., Eds.; Routledge: London, 2021; pp 92–
100. . https://doi.org/10.4324/9781003004431-10 (34) Dahlgren, P. The Internet, Public Spheres, and Political Communication: Dispersion and
Deliberation. Polit. Commun. 2005, 22 (2), 147–162. . https://doi.org/10.1080/10584600590933160 (35) Belair-Gagnon, V.; Nelson, J. L.; Lewis, S. C. Audience Engagement, Reciprocity, and the
Pursuit of Community Connectedness in Public Media Journalism. Journal. Pract. 2019, 13 (5),
558–575. .
(36) Benkler, Y. The Wealth of Networks; Yale University Press: New York, 2006. https://doi.org/10.1080/17512786.2018.1542975 (37) McPherson, M.; Smith-Lovin, L.; Cook, J. M. Birds of a Feather: Homophily in Social
Networks. Annu. Rev. Sociol. 2001, 27 (1), 415–444. . https://doi.org/10.1146/annurev.soc.27.1.415 (38) Terren, L.; Borge, R. Echo Chambers on Social Media: A Systematic Review of the Literature.
Rev. Commun. Res. 2021, 9 (May), 1–39. . https://doi.org/10.12840/ISSN.2255-4165.028 (39) Gt Walker, P.; Whittaker, C.; Watson, O.; Baguelin, M.; Ainslie, K. E. C.; Bhatia, S.; Bhatt, S.;
Boonyasiri, A.; Boyd, O.; Cattarino, L.; Cucunubá, Z.; Cuomo-Dannenburg, G.; Dighe, A.;
Donnelly, C. A.; Dorigatti, I.; Van Elsland, S.; Fitzjohn, R.; Flaxman, S.; Fu, H.; Gaythorpe, K.;
Geidelberg, L.; Grassly, N.; Green, W.; Hamlet, A.; Hauck, K.; Haw, D.; Hayes, S.; Hinsley, W.;
Imai, N.; Jorgensen, D.; Knock, E.; Laydon, D.; Mishra, S.; Nedjati-Gilani, G.; Okell, L. C.; Riley,
S.; Thompson, H.; Unwin, J.; Verity, R.; Vollmer, M.; Walters, C.; Wang, W.; Wang, Y.;
Winskill, P.; Xi, X.; Ferguson, N. M.; Ghani, A. C. The Global Impact of COVID-19 and Strategies
for Mitigation and Suppression; 2020. (40) Bruns, A. Echo Chambers? Filter Bubbles? The Misleading Metaphors That Obscure the Real
Problem. In Hate Speech and Polarization in Participatory Society; Pérez-Escolar, M., NogueraVivo, J. M., Eds.; Routledge: London, 2021; pp 33–48. 4. https://doi.org/10.4324/9781003109891- (41) Flaxman, S.; Goel, S.; Rao, J. M. Filter Bubbles, Echo Chambers, and Online News
Consumption. Public Opin. Q. 2016, 80 (Specialissue1), 298–320. . https://doi.org/10.1093/poq/nfw006 (42) Bakshy, E.; Messing, S.; Adamic, L. A. Exposure to Ideologically Diverse News and Opinion
on Facebook. Science (80-. ). 2015, 348 (6239), 1130–1132. . https://doi.org/10.1126/science.aaa1160 (43) Bright, J. Explaining the Emergence of Political Fragmentation on Social Media: The Role of
Ideology and Extremism. J. Comput. Commun. 2018, 23 (1), 17–33. . https://doi.org/10.1093/JCMC/ZMX002 (44) Eady, G.; Nagler, J.; Guess, A.; Zilinsky, J.; Tucker, J. A. How Many People Live in Political
Bubbles on Social Media? Evidence From Linked Survey and Twitter Data. SAGE Open 2019,
9 (1). . https://doi.org/10.1177/2158244019832705 (45) Garimella, K.; Morales, G. D. F.; Gionis, A.; Mathioudakis, M. Quantifying Controversy on
Social Media. ACM Trans. Soc. Comput. 2018, 1 (1), 1–27. . https://doi.org/10.1145/3140565 (46) Cinelli, M.; De Francisci Morales, G.; Galeazzi, A.; Quattrociocchi, W.; Starnini, M. The Echo
Chamber Effect on Social Media. Proc. Natl. Acad. Sci. 2021, 118 (9), e2023301118. . https://doi.org/10.1073/pnas.2023301118 (47) Arguedas, A. R.; Robertson, C. T.; Fletcher, R.; Nielsen, R. K. Echo Chambers, Filter Bubbles, and
Polarisation: A Literature Review; Oxford, 2022. (48) Bucher, T.; Helmond, A. The Affordances of Social Media Platforms. In The SAGE Handbook of
Social Media; Burgess, J., Marwick, A., Poell, T., Eds.; SAGE Publications Ltd: 1 Oliver’s Yard,
55 City Road London EC1Y 1SP, 2018; pp 233–253. . https://doi.org/10.4135/9781473984066.n14 (49) Barberá, P. How Social Media Reduces Mass Political Polarization. Evidence from Germany, Spain, and the U.S; 2015. (50) van der Linden, S. Misinformation: Susceptibility, Spread, and Interventions to Immunize the Public. Nat. Med. 2022, 28 (3), 460–467. . https://doi.org/10.1038/s41591-022-01713-6 (51) Righetti, N. Four Years of Fake News: A Quantitative Analysis of the Scientific Literature. First Monday 2021. https://doi.org/10.5210/fm.v26i7.11645. (52) Tandoc, E. C.; Lim, Z. W.; Ling, R. Defining “Fake News”: A Typology of Scholarly Definitions. Digit. Journal. 2018, 6 (2), 137–153. https://doi.org/10.1080/21670811.2017.1360143. (53) Chang, K.-C.; Menczer, F. How many bots are on Twitter? The question is tough to answer — and misses the point https://www.niemanlab.org/2022/05/how-many-bots-are-on-twitterthe-question-is-tough-to-answer-and-misses-the-point/ (accessed Sep 1, 2022). (54) Giglietto, F.; Righetti, N.; Marino, G. Understanding Coordinated and Inauthentic Link Sharing Behavior on Facebook in the Run-up to 2018 General Election and 2019 European Election in Italy; SocArXiv, 2019. https://doi.org/10.31235/osf.io/3jteh. (55) Giglietto, F.; Iannelli, L.; Rossi, L.; Valeriani, A.; Righetti, N.; Carabini, F.; Marino, G.; Usai, S.; Zurovac, E. Mapping Italian News Media Political Coverage in the Lead-Up of 2018 General Election. SSRN Electron. J. 2018. https://doi.org/10.2139/ssrn.3179930. (56) Giglietto, F.; Righetti, N.; Rossi, L.; Marino, G. Coordinated Link Sharing Behavior as a Signal to Surface Sources of Problematic Information on Facebook. ACM Int. Conf. Proceeding Ser. 2020, 20, 85–91. https://doi.org/10.1145/3400806.3400817. (57) Varol, O.; Ferrara, E.; Davis, C. A.; Menczer, F.; Flammini, A. Online Human-Bot Interactions: Detection, Estimation, and Characterization. Proc. 11th Int. Conf. Web Soc. Media, ICWSM 2017 2017, 280–289. (58) Khaund, T.; Kirdemir, B.; Agarwal, N.; Liu, H.; Morstatter, F. Social Bots and Their Coordination During Online Campaigns: A Survey. IEEE Trans. Comput. Soc. Syst. 2022, 9 (2), 530–545. https://doi.org/10.1109/TCSS.2021.3103515. (59) Dang, S.; Paul, K.; Chmielewski, D. Do spam bots really comprise under 5% of Twitter users? Elon Musk wants to know | Reuters https://www.reuters.com/technology/do-spam-botsreally-comprise-under-5-twitter-users-elon-musk-wants-know-2022-05-13/ (accessed Sep 1, 2022). (60) Luceri, L.; Deb, A.; Giordano, S.; Ferrara, E. Evolution of Bot and Human Behavior during Elections. First Monday 2019, 24 (9). https://doi.org/10.5210/fm.v24i9.10213. (61) Bugra Torusdag, M.; Kutlu, M.; Selcuk, A. A. Are We Secure from Bots? Investigating Vulnerabilities of Botometer. 5th Int. Conf. Comput. Sci. Eng. UBMK 2020 2020, 343–348. https://doi.org/10.1109/UBMK50275.2020.9219433. (62) Balestrucci, A. How Many Bots Are You Following? In CEUR Workshop Proceedings; Ancona; Italy, 2020; pp 47–59. (63) Bello, B. S.; Heckel, R.; Minku, L. Reverse Engineering the Behaviour of Twitter Bots. 2018 5th Int. Conf. Soc. Networks Anal. Manag. Secur. SNAMS 2018 2018, 27–34. https://doi.org/10.1109/SNAMS.2018.8554675. (64) Akyon, F. C.; Esat Kalfaoglu, M. Instagram Fake and Automated Account Detection. In Proceedings - 2019 Innovations in Intelligent Systems and Applications Conference, ASYU 2019; IEEE, 2019; pp 1–7. https://doi.org/10.1109/ASYU48272.2019.8946437. (65) Cresci, S. A Decade of Social Bot Detection. Commun. ACM 2020, 63 (10), 72–83. https://doi.org/10.1145/3409116. (66) Al-Khateeb, S.; Agarwal, N. Examining Botnet Behaviors for Propaganda Dissemination: A Case Study of ISIL’s Beheading Videos-Based Propaganda. In Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015; IEEE, 2016; pp 51–57. https://doi.org/10.1109/ICDMW.2015.41. (67) Islam, A. K. M. N.; Laato, S.; Talukder, S.; Sutinen, E. Misinformation Sharing and Social Media Fatigue during COVID-19: An Affordance and Cognitive Load Perspective. Technol. Forecast. Soc. Change 2020, 159, 120201. https://doi.org/10.1016/j.techfore.2020.120201. (68) Törnberg, P. Echo Chambers and Viral Misinformation: Modeling Fake News as Complex Contagion. PLoS One 2018, 13 (9), 1–21. https://doi.org/10.1371/journal.pone.0203958. (69) IFCN. International Fact-Checking Network – Poynter https://www.poynter.org/ifcn/ (accessed Jul 27, 2020). (70) Bruns, A.; Harrington, S.; Hurcombe, E. ‘Corona? 5G? Or Both?’: The Dynamics of COVID-19/5G Conspiracy Theories on Facebook. Media Int. Aust. 2020, 177 (1), 12–29. https://doi.org/10.1177/1329878X20946113. (71) de-Lima-Santos, M.-F.; Kooli, A. Instagrammable Data: Using Visuals to Showcase More Than Numbers on AJ Labs Instagram Page. Int. J. Commun. 2022, 16, 2821–2842. https://doi.org/1932–8036/20220005. (72) Reuters. Facebook removes dozens of vaccine misinformation “superspreaders” https://www.reuters.com/technology/facebook-removes-dozens-vaccine-misinformationsuperspreaders-2021-08-18/ (accessed Oct 11, 2021). (73) Bastian, M.; Heymann, S.; Jacomy, M. Gephi: An Open Source Software for Exploring and Manipulating Networks. Icwsm 2009. (74) Blondel, V. D.; Guillaume, J. L.; Lambiotte, R.; Lefebvre, E. Fast Unfolding of Communities in Large Networks. J. Stat. Mech. Theory Exp. 2008, 2008 (10), 10008. https://doi.org/10.1088/1742- 5468/2008/10/P10008. (75) Fortunato, S. Community Detection in Graphs. Phys. Rep. 2010, 486 (3–5), 75–174. https://doi.org/10.1016/J.PHYSREP.2009.11.002. (76) Howard, P. N.; Woolley, S.; Calo, R. Algorithms, Bots, and Political Communication in the US 2016 Election: The Challenge of Automated Political Communication for Election Law and Administration. J. Inf. Technol. Polit. 2018, 15 (2), 81–93. https://doi.org/10.1080/19331681.2018.1448735. (77) Theocharis, Y.; Jungherr, A. Computational Social Science and the Study of Political Communication. Polit. Commun. 2021, 38 (1–2), 1–22. https://doi.org/10.1080/10584609.2020.1833121. (78) Girvan, M.; Newman, M. E. J. Community Structure in Social and Biological Networks. Proc. Natl. Acad. Sci. U. S. A. 2002, 99 (12), 7821–7826. https://doi.org/10.1073/pnas.122653799. (79) Watts, D. J.; Strogatz, S. H. Collective Dynamics of ’small-World9 Networks. Nature 1998, 393 (6684), 440–442. https://doi.org/10.1038/30918. (80) Walter, N.; Salovich, N. A. Unchecked vs. Uncheckable: How Opinion-Based Claims Can Impede Corrections of Misinformation. Mass Commun. Soc. 2021, 15205436.2020.1864406. https://doi.org/10.1080/15205436.2020.1864406. (81) Mozilla Foundation. INSIDE THE SHADOWY WORLD OF DISINFORMATION FOR HIRE IN KENYA; 2021. (82) Culliford, E. Facebook cracks down on German anti-COVID restrictions group over “social harm” https://www.reuters.com/technology/facebook-shuts-down-network-linked-germananti-covid-group-launches-rules-social-2021-09-16/ (accessed Oct 10, 2021). (83) Angus, D.; Bruns, A.; Hurcombe, E.; Harrington, S.; Glazunova, S.; Montaña-Niño, S. X.; Obeid, A.; Coulibaly, S.; Copland, S.; Graham, T.; Wright, S.; Dehghan, E. ‘Fake News’ and Other Problematic Information: Studying Dissemination and Discourse Patterns. AoIR Selected Papers of Internet Research. 2021. https://doi.org/10.5210/spir.v2021i0.12089 (84) Dehghan, E.; Glazunova, S.. ‘Fake news’ discourses. Journal of Language and Politics. 2021. 20(5), 741–760. https://doi.org/10.1075/jlp.21032.deh (85) de-Lima-Santos, M.-F.; Ceron, W. Coordinated Amplification, Coordinated Inauthentic Behavior, Orchestrated Campaigns? A Systematic Literature Review of Coordinated Inauthentic Content on Online Social Networks. In T. Filibeli & M. Ö. Özbek (Eds.), Mapping Lies in the Global Media Sphere (1st ed.). 2024.Taylor & Francis. (86) Porreca, A.; Scozzari, F.; Di Nicola, M. Using text mining and sentiment analysis to analyse YouTube Italian videos concerning vaccination. BMC Public Health. 2020. 20(1), 1–9. https://doi.org/10.1186/s12889-020-8342-4 (87) Wardle, C. Misunderstanding Misinformation. Issues in Science and Technology. 2023. 29(3), 38– 40. https://doi.org/10.58875/zaud1691

Disinformation Echo-Chambers on Facebook: Funding and References

of Sam Altman’s forced ousting by OpenAI Board got even weirder and stranger over the weekend. If you are not in tech, I envy you. Your tech friends just binge watched 3 seasons of Succession and got left on a cliff hanger! But anyway, here are the sequence of events since Sam’s forced ousting on Friday 12:59 pm Monday Nov 20, 2023: The absolutely bizzare story : Greg Brockman, OpenAI COO/Chairman of the Board/Co-founder, , in solidarity with Sam. He mentioned knowing of the news mere minutes before it went out. Late Friday Nov 17 quit : several high ranking talents @ openAI quit. Turns out, Greg & Sam are pretty popular leaders. Then, there was a coordinated effort from many OpenAI employees to signal to the public who would all quit if Greg & Sam are not back, by quoting Sam’s tweet “I love the OpenAI team so much” with a heart emoji. Notably, to-be-interim CEO Mira Murati was part of that effort. Saturday Nov 18 : Sam got back in the OpenAI office for a negotiation with Board re/ his (and Greg’s) potential return. Condition would have been to find a new board, and all current board members to resign. Microsoft CEO Satya Nadella led the negotiation between Sam & board. A 5pm deadline was set and missed. Late Saturday Nov 18 :  Sam again got back in the office for a negotiation with Board re/ his (and Greg’s) potential return. Sam mentioned that this time, it would be his last. Another 5pm deadline was set and missed. Sunday Nov 19 Groundhog’s Day Monday Nov 20: a “bring sam back or we’ll quit” letter was leaked. Out of 770+ employees, 667 (and counting) have signed on to the letter, as of 10am MT Monday. Notably, it got cut off at the word “Iliya Sutskever”, the Chief Scientist Officer, one of 6 board members, the one whose vote was instrumental in ousting Sam. : Former CEO of Twitch, Emmett Shear, accepted job offer to be CEO of OpenAI! Midnight Sunday Nov 19/ Monday Nov 20 : Satya Nadella announced that Sam & Greg would, get this, JOIN MICROSOFT. More info . ok Monday Nov 20 here ….And we are half way over yet with Monday. “Concluding” Thoughts, aka WTF? I’m sure the story will continue to develop, hence the “concluding” is in quotation marks. But here are some of my thoughts on this absolutely insane story. And this time - I would like you, the readers - to help answer/shed some lights on some of them. : excuse my language, but who the fuck came up with this unnecessarily complicated and bizarre idea? And more importantly, why? . The former cares not at all about profit, while the latter is driven by it. The OpenAI Board consists of literally no one who has actual skin in the game. None of them holds OpenAI shares. In fact, the Quora CEO, has a conflict of interest in the form of his own ! Microsoft, who owns 49% of OpenAI shares, has no Board seat. Go figure. The Nonprofit Board controlling a For-profit Company The incentive of a nonprofit board is highly at odds with the incentive of a for-profit board alternative AI model, called Poe . I was following just fine on the OpenAI story (it was crazy, insane, glitch in the universe, etc, but it made sense….until he apologized). There are 6 Board members of OpenAI: CEO/founder Sam Altman, COO/cofounder Greg Brockman, CSO Ilya Suskever, Quora CEO Adam D’Angelo, Tech Entrepreneur Tasha McCauley, and Georgia Tech Director of Grants Helen Toner. In order to oust the CEO, there has to be a majority, aka, 4 votes. We know that Sam & Greg are on the same side. That leaves Ilya Suskever being the instrumental vote in forcing Sam’s ousting. Some people even call it a coup between the AI accelerationist vs. the AI safetyist. AND NOW ILYA’S APOLOGIZING? And telling the board he would… resign? If he saw something so atrocious from their “deliberate review” process that shows Sam not being “candid” with Board and render board lack “confidence” in Sam’s ability to run the company… WHAT IS IT? And why is it not important now? like, wtf? I don’t know much about Ilya’s motivation or what he knows that we don’t, but given I think his vote could have been a hallucination corrected by reinforcement learning from human feedback: The absolute bizarreness that is Ilya Sutskever’s U-turn what lya  Sutskever said in a March of this year interview with HackerNoon ” “We call it Reinforcement Learning from Human Feedback. We can say that in the pre-training process, you want to learn everything about the world. With reinforcement learning from human feedback, we care about the outputs. We say, anytime the output is inappropriate, don't do this again. Every time the output does not make sense, don't do this again.And it learns quickly to produce good outputs. But it's the level of the outputs, which is not the case during the language model pre-training process. Now on the point of hallucinations, it has a propensity of making stuff up from time to time, and that's something that also greatly limits their usefulness.But I'm quite hopeful that by simply improving this subsequent reinforcement learning from human feedback step, we can teach it to not hallucinate. Now you could say is it really going to learn? My answer is, let's find out. Microsoft tanked by 1% from the news of Sam’s ousting on Friday, which came out before market closes. As the CEO, Satya Nadella has fiduciary duty to fulfill to his shareholders (unlike, idk, the OpenAI board), he’s been instrumental in all these back-and-forth negotiations between the ideologues and the operators of OpenAI. All I can say right now is it sure as hell looks like Microsoft literally just acqui-hired OpenAI (Sam, Greg, and what looks like 80% of a company that was recently valued $80 Bilion) for $10 Billion dollars. What a move. Is the winner, ultimately, Microsoft? Nobody knows. Literally. They all have been sending extremely mixed signals to the public. In one breath threatening board to resign and leave with Sam and in the same breath saying they are “more united” than ever. Apparently, according to the Verge, the hiring of Sam & Greg and “colleagues” (read: almost all of OpenAI) is not even final yet. And the new CEO Emmett Shear is having a hard time gathering paperwork and having employees follow his order. What will happen to OpenAI next? : AI might be the most important technology of this decade, and the decades to come. But the story unfolded this past weekend just shows us how incredibly flawed the people under peril of whose it’s being controlled are. Savvy tech + flawed humans = pretty disastrous combo, imho. Whether or not you feel hopeful or doomed about the future, going forward, very much depends on your predisposition and existing temperament to begin with. If Ilya the scientist can make a u-turn, i guess so can the future of humanity. The danger of leaving AI’s fate in a few PS: So sorry that this story was extremely twitter-screenshot heavy. It’s not my fault that tech rank-and-files still latch on to that platform like moths despite everything. So I will leave you with this screenshot, from , instead. Until next time! threads

Chronological Feed: Sam Altman Fired by OpenAI Board & Hired By Microsoft CEO Satya Nadella (maybe)

কালানুক্রমিক ফিড: স্যাম অল্টম্যানকে ওপেনএআই বোর্ড দ্বারা বরখাস্ত করা হয়েছে এবং মাইক্রোসফ্ট সিইও সত্য নাদেলা (সম্ভবত) দ্বারা নিয়োগ করা হয়েছে

Chronologischer Feed: Sam Altman vom OpenAI-Vorstand entlassen und von Microsoft-CEO Satya Nadella eingestellt (vielleicht)

Feed cronológico: Sam Altman despedido por la junta de OpenAI y contratado por el director ejecutivo de Microsoft, Satya Nadella (tal vez)

Flux chronologique : Sam Altman licencié par le conseil d'administration d'OpenAI et embauché par le PDG de Microsoft, Satya Nadella (peut-être)

कालानुक्रमिक फ़ीड: सैम ऑल्टमैन को ओपनएआई बोर्ड द्वारा निकाल दिया गया और माइक्रोसॉफ्ट के सीईओ सत्या नडेला द्वारा नियुक्त किया गया (शायद)

時系列フィード: サム・アルトマン氏がOpenAI取締役会に解雇され、マイクロソフトCEOのサティア・ナデラ氏に雇われた（たぶん）

연대표: Sam Altman이 OpenAI 이사회에서 해고되고 Microsoft CEO Satya Nadella가 고용함(아마도)

Feed cronológico: Sam Altman demitido pelo conselho da OpenAI e contratado pelo CEO da Microsoft, Satya Nadella (talvez)

Хронологическая лента: Сэм Альтман уволен советом директоров OpenAI и нанят генеральным директором Microsoft Сатьей Наделлой (возможно)

Kronolojik Akış: Sam Altman OpenAI Yönetim Kurulu Tarafından Kovuldu ve Microsoft CEO'su Satya Nadella Tarafından İşe Alındı (belki)

Nguồn cấp dữ liệu theo thời gian: Sam Altman Bị Hội đồng OpenAI sa thải và được Giám đốc điều hành Microsoft Satya Nadella thuê (có thể)

按时间顺序排列：萨姆·奥尔特曼 (Sam Altman) 被 OpenAI 董事会解雇，并被微软首席执行官萨蒂亚·纳德拉 (Satya Nadella) 聘用（可能）

Stories, News & Mentions about BIZZY

Bizzy’s Stories on HackerNoon