Эффект Рингельмана -- эффект социальной лени
https://en.wikipedia.org/wiki/Ringelmann_effect
https://en.wikipedia.org/wiki/Social_loafing
Есть формула, которая апроксимирует средний вклад каждого участника. Работает на группе до 7 человек и только для физической активности. Есть вариант и для бОльших групп, и для knowledge workers (надо копать отдельно):
где С — средний индивидуальный вклад участников; К — количество участников группы.
Можно подумать, как с этой помощью моделировать эффективность команд/соц.групп.
Книжка по теме
https://www.amazon.com/Group-Dynamics-5th-Donelson-Forsyth-ebook/dp/B01AJ8YU2Q
Дискуссия со ссылками и спорами
https://www.facebook.com/teodorix/posts/10220400256226704
https://en.wikipedia.org/wiki/Ringelmann_effect
https://en.wikipedia.org/wiki/Social_loafing
Есть формула, которая апроксимирует средний вклад каждого участника. Работает на группе до 7 человек и только для физической активности. Есть вариант и для бОльших групп, и для knowledge workers (надо копать отдельно):
С = 100 - 7*(К-1)где С — средний индивидуальный вклад участников; К — количество участников группы.
Можно подумать, как с этой помощью моделировать эффективность команд/соц.групп.
Книжка по теме
https://www.amazon.com/Group-Dynamics-5th-Donelson-Forsyth-ebook/dp/B01AJ8YU2Q
Дискуссия со ссылками и спорами
https://www.facebook.com/teodorix/posts/10220400256226704
paper168.pdf
1.8 MB
Apple's CHI 2019 research paper on app usage in older adults
To characterize smartphone usage among older adults, we collected iPhone usage data from 84 healthy older adults over three months. We find that older adults use fewer apps, take longer to complete tasks, and send fewer messages. We use cognitive test results from these same older adults to then show that up to 79% of these differences can be explained by cognitive decline, and that we can predict cognitive test performance from smartphone usage with 83% ROCAUC. While older adults differ from younger adults in app usage behavior, the “cognitively young” older adults use smartphones much like their younger counterparts. Our study suggests that to better support all older adults, researchers and developers should consider the full spectrum of cognitive function.
To characterize smartphone usage among older adults, we collected iPhone usage data from 84 healthy older adults over three months. We find that older adults use fewer apps, take longer to complete tasks, and send fewer messages. We use cognitive test results from these same older adults to then show that up to 79% of these differences can be explained by cognitive decline, and that we can predict cognitive test performance from smartphone usage with 83% ROCAUC. While older adults differ from younger adults in app usage behavior, the “cognitively young” older adults use smartphones much like their younger counterparts. Our study suggests that to better support all older adults, researchers and developers should consider the full spectrum of cognitive function.
hamilton2017loyalty.pdf
760.1 KB
In this paper we operationalize loyalty as a user-community relation: users loyal to a community consistently prefer it over all others; loyal communities retain their loyal users over time. By exploring a large set of Reddit communities, we reveal that loyalty is manifested in remarkably consistent behaviors. Loyal users employ language that signals collective identity and engage with more esoteric, less popular content, indicating that they may play a curational role in surfacing new material. Loyal communities have denser user-user interaction networks and lower rates of triadic closure, suggesting that community-level loyalty is associated with more cohesive interactions and less fragmentation into subgroups. We exploit these general patterns to predict future rates of loyalty. Our results show that a user’s propensity to become loyal is apparent from their initial interactions with a community, suggesting that some users are intrinsically loyal from the very beginning.
User modeling is the subdivision of human–computer interaction which describes the process of building up and modifying a conceptual understanding of the user. The main goal of user modeling is customization and adaptation of systems to the user's specific needs. The system needs to "say the 'right' thing at the 'right' time in the 'right' way". To do so it needs an internal representation of the user. Another common purpose is modeling specific kinds of users, including modeling of their skills and declarative knowledge, for use in automatic software-tests. User-models can thus serve as a cheaper alternative to user testing.
Conference: https://www.um.org/
Conference: https://www.um.org/
www.um.org
User Modeling - User Modeling
UM Inc. is a society of researchers and practitioners who are interested in developing adaptive systems and personalizing users’ experience with systems.
Inferring User Interests in Microblogging Social Networks: A Survey
Guangyuan Piao, John G. Breslin
https://arxiv.org/abs/1712.07691
Guangyuan Piao, John G. Breslin
https://arxiv.org/abs/1712.07691
arXiv.org
Inferring User Interests in Microblogging Social Networks: A Survey
With the growing popularity of microblogging services such as Twitter in
recent years, an increasing number of users are using these services in their
daily lives. The huge volume of information...
recent years, an increasing number of users are using these services in their
daily lives. The huge volume of information...
A bunch of smaller things joining together to form a giant that can function as more than the sum of its parts — is called emergence. We can visualize it as a tower.
...
A human isn’t simply a perfect survival creature—it’s also just the right element of a perfect survival tribe. Examining the traits of a perfect survival tribe can help us see the specs for human nature, not only illuminating who we are, but why we’re that way.
https://waitbutwhy.com/2019/08/giants.html
...
A human isn’t simply a perfect survival creature—it’s also just the right element of a perfect survival tribe. Examining the traits of a perfect survival tribe can help us see the specs for human nature, not only illuminating who we are, but why we’re that way.
https://waitbutwhy.com/2019/08/giants.html
Forwarded from TechSparks
Микрософту упрямо не везёт с ботами. Была у них печально знаменитая Таи, которая реально училась у собеседников в Твиттере — и ее немедленно обучили плохому и очень плохому.
А теперь тамошние исследователи написали бота-комментатора (да ещё и выложили код на Гитхабе). Бот читает новости и пишет типичный комментарий среднего обитателя соцсеточек, который в каждой бочке затычка. Цель заявлена благая: размещая сгенерированные комментарии, побудить живых людей вступать в дискуссию (а для этого некоторые из них и саму новость прочтут). То есть боты должны поднять интерес к новостям и вовлечённость читателей.
Но неудивительно, что борцы с фейками увидели не столь благостные сценарии использования алгоритма ;) И встревожились. А ещё неудивительно, что обе разработки — изначально для Китая: всё-таки деанонимизированный и контролируемый интернет живет по своим правилам ;)
https://www.vice.com/en_ca/article/d3a4mk/microsoft-used-machine-learning-to-make-a-bot-that-comments-on-news-articles-for-some-reason
А теперь тамошние исследователи написали бота-комментатора (да ещё и выложили код на Гитхабе). Бот читает новости и пишет типичный комментарий среднего обитателя соцсеточек, который в каждой бочке затычка. Цель заявлена благая: размещая сгенерированные комментарии, побудить живых людей вступать в дискуссию (а для этого некоторые из них и саму новость прочтут). То есть боты должны поднять интерес к новостям и вовлечённость читателей.
Но неудивительно, что борцы с фейками увидели не столь благостные сценарии использования алгоритма ;) И встревожились. А ещё неудивительно, что обе разработки — изначально для Китая: всё-таки деанонимизированный и контролируемый интернет живет по своим правилам ;)
https://www.vice.com/en_ca/article/d3a4mk/microsoft-used-machine-learning-to-make-a-bot-that-comments-on-news-articles-for-some-reason
Vice
Microsoft Used Machine Learning to Make a Bot That Comments on News Articles For Some Reason
The algorithm automatically reads and digests new articles, and posts comments alongside humans.
About UMUAI - The Journal of Personalization Research
User Modeling and User-Adapted Interaction (UMUAI) provides an interdisciplinary forum for the dissemination of novel original research results about interactive computer systems that can be adapted or adapt themselves to their current users, and on the role of user models in the adaptation process.
http://www.umuai.org/
User Modeling and User-Adapted Interaction (UMUAI) provides an interdisciplinary forum for the dissemination of novel original research results about interactive computer systems that can be adapted or adapt themselves to their current users, and on the role of user models in the adaptation process.
http://www.umuai.org/
https://youtu.be/5J5L1uCtX_Q
https://en.wikipedia.org/wiki/Public_participation_geographic_information_system
https://www.qullab.com/research
https://imprecity.ru/analytics
https://en.wikipedia.org/wiki/Public_participation_geographic_information_system
https://www.qullab.com/research
https://imprecity.ru/analytics
YouTube
Эмоции города: анализ качества городской среды с помощью PPGIS – Александра Ненько
В выступлении будут представлены результаты анализа эмоций горожан Санкт-Петербурга, полученных с помощью системы Imprecity, позволяющей зарегистрированным пользователям оставлять смайлики и комментарии об общественных пространствах. На основе собранных…
Last week, I saw a lot of social media discussion about a paper using deep learning to generate artificial comments on news articles. I’m not sure why anyone thinks this is a good idea. At best, it adds noise to the media environment. At worst, it’s a tool for con artists and propagandists.
A few years ago, an acquaintance pulled me aside at a conference to tell me he was building a similar fake comment generator. His project worried me, and I privately discussed it with a few AI colleagues, but none of us knew what to do about it. It was only this year, with the staged release of OpenAI’s GPT-2 language model, that the question went mainstream.
Do we avoid publicizing AI threats to try to slow their spread, as I did after hearing about my acquaintance’s project? Keeping secret the details of biological and nuclear weapon designs has been a major force slowing their proliferation. Alternatively, should we publicize them to encourage defenses, as I’m doing in this letter?
Efforts like the OECD’s Principles on AI, which state that “AI should benefit people and the planet,” give useful high-level guidance. But we need to develop guidelines to ethical behavior in practical situations, along with concrete mechanisms to encourage and empower such behavior.
We should look to other disciplines for inspiration, though these ideas will have to be adapted to AI. For example, in computer security, researchers are expected to report vulnerabilities to software vendors confidentially and give them time to issue a patch. But AI actors are global, so it’s less clear how to report specific AI threats.
Or consider healthcare. Doctors have a duty to care for their patients, and also enjoy legal protections so long as they are working to discharge this duty. In AI, what is the duty of an engineer, and how can we make sure engineers are empowered to act in society’s best interest?
To this day, I don’t know if I did the right thing years ago, when I did not publicize the threat of AI fake commentary. If ethical use of AI is important to you, I hope you will discuss worrisome uses of AI with trusted colleagues so we can help each other find the best path forward. Together, we can think through concrete mechanisms to increase the odds that this powerful technology will reach its highest potential.
https://info.deeplearning.ai/the-batch-tesla-acquires-deepscale-france-backs-face-recognition-robots-learn-in-virtual-reality-acquirers-snag-ai-startups
info.deeplearning.ai
The Batch: Tesla Acquires DeepScale, France Backs Face Recognition, Robots Learn in Virtual Reality, Acquirers Snag AI Startups
User Modeling in Human-Computer Interaction
A fundamental objective of human-computer interaction research is to make systems more usable, more useful, and to provide users with experiences fitting their specific background knowledge and objectives. The challenge in an information-rich world is not only to make information available to people at any time, at any place, and in any form, but specifically to say the “right” thing at the “right” time in the “right” way. Designers of collaborative human- computer systems face the formidable task of writing software for millions of users (at design time) while making it work as if it were designed for each individual user (only known at use time).
User modeling research has attempted to address these issues. In this article, I will first review the objectives, progress, and unfulfilled hopes that have occurred over the last ten years, and illustrate them with some interesting computational environments and their underlying conceptual frameworks. A special emphasis is given to high-functionality applications and the impact of user modeling to make them more usable, useful, and learnable. Finally, an assessment of the current state of the art followed by some future challenges is given.
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.43.6025&rep=rep1&type=pdf
A fundamental objective of human-computer interaction research is to make systems more usable, more useful, and to provide users with experiences fitting their specific background knowledge and objectives. The challenge in an information-rich world is not only to make information available to people at any time, at any place, and in any form, but specifically to say the “right” thing at the “right” time in the “right” way. Designers of collaborative human- computer systems face the formidable task of writing software for millions of users (at design time) while making it work as if it were designed for each individual user (only known at use time).
User modeling research has attempted to address these issues. In this article, I will first review the objectives, progress, and unfulfilled hopes that have occurred over the last ten years, and illustrate them with some interesting computational environments and their underlying conceptual frameworks. A special emphasis is given to high-functionality applications and the impact of user modeling to make them more usable, useful, and learnable. Finally, an assessment of the current state of the art followed by some future challenges is given.
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.43.6025&rep=rep1&type=pdf
In short, the goal of this course is to introduce students to ways of thinking about how Artificial Intelligence will and has impacted humans, and how we can design interactive intelligent systems that are usable and beneficial to humans, and respect human values. As students in this course, you will build a number of different interactive technologies powered by AI, gain practical experience with what impacts their usability for humans, understand the various places that humans exist in the data pipeline that drives machine learning, and learn to think both optimistically and critically of what AI systems can do and how they can and should be integrated into society.
TODO: download slides http://www.humanaiclass.org/schedule/
TODO: download slides http://www.humanaiclass.org/schedule/