Post #139
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Channel Public Channel
CO Collective Intelligence
@co_intelligence
Collective intelligence (CI) is shared or group intelligence that emerges from the collaboration, collective efforts, and competition of many individuals and appears in consensus decision making.
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Machine learning helps researchers understand human psychology and mental illness by identifying patterns in individuals’ word-use that can be predictive of certain conditions.
https://www.technologyreview.com/s/608322/the-emerging-science-of-computational-psychiatry/
MIT Technology Review The Emerging Science of Computational Psychiatry Psychiatry, the study and prevention of mental disorders, is currently undergoing a quiet revolution. For decades, even centuries, this discipline has been based largely on subjective observation. Large-scale studies have been hampered by the difficulty of objectively… https://www.technologyreview.com/s/608322/the-emerging-science-of-computational-psychiatry/
Post #128
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Post #127
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*Personalized Re-ranking for Recommendation*
Re-ranking в рекомендательных системах зависит от данных пользователя, его предпочтений и намерений.
Для пользователя, который чувствителен к цене, взаимодействие между ценой должно быть более важным в модели повторного re-ranking.
Обычно ранжирование в рекомендательной системе учитывает только особенности пары пользователь-элемент. Pairwise и listwise learning to rank пытаются решить эту проблему, используя в качестве входных данных пару элементов или список элементов. Они сосредоточены только на оптимизации функции потерь, чтобы лучше использовать метки, например, данные о кликах. Они явно не моделировали взаимные влияния между элементами в пространстве признаков.
Исследователи из Alibaba и Kwai предложили использовать Transofrmer, хорошо знакомый по машинному переводу. Их аргументы следующие: в структуре Transformer используется self-attention, в котором любые два элемента могут взаимодействовать друг с другом напрямую без ухудшения encoding distance.
Между тем, Transformer более эффективен, чем RNN из-за возможности распараллеливания. Transformer позволяет моделировать взаимодействия для любого из двух предметов за O (1).
Исследователи представили персонализированную матрицу PV для изучения функции encoding, специфичной для пользователя, которая способна моделировать персонализированные взаимные влияния между парой предметов. Поэтому функцию потерь можно сформулировать следующим образом.
Авторы используют предварительно обученную нейронную сеть для создания персонализированных вложений пользователя, которые затем используются в качестве дополнительных функций для модели PRM. Предварительно обученная нейронная сеть извлекается из click-through logs . Дополнительная информация пользователя включает пол, возраст и информацию о покупках.
Evaluation Metrics
1. Precision@k
2. MAP@k
Для онлайн A/B test, использовали PV (pageView), IPV(itemProudctClick), CTR(click-through-rates) и GMV в качестве метрик.
https://arxiv.org/abs/1904.06813
arXiv.org Personalized Re-ranking for Recommendation Ranking is a core task in recommender systems, which aims at providing an ordered list of items to users. Typically, a ranking function is learned from the labeled dataset to optimize the global... Re-ranking в рекомендательных системах зависит от данных пользователя, его предпочтений и намерений.
Для пользователя, который чувствителен к цене, взаимодействие между ценой должно быть более важным в модели повторного re-ranking.
Обычно ранжирование в рекомендательной системе учитывает только особенности пары пользователь-элемент. Pairwise и listwise learning to rank пытаются решить эту проблему, используя в качестве входных данных пару элементов или список элементов. Они сосредоточены только на оптимизации функции потерь, чтобы лучше использовать метки, например, данные о кликах. Они явно не моделировали взаимные влияния между элементами в пространстве признаков.
Исследователи из Alibaba и Kwai предложили использовать Transofrmer, хорошо знакомый по машинному переводу. Их аргументы следующие: в структуре Transformer используется self-attention, в котором любые два элемента могут взаимодействовать друг с другом напрямую без ухудшения encoding distance.
Между тем, Transformer более эффективен, чем RNN из-за возможности распараллеливания. Transformer позволяет моделировать взаимодействия для любого из двух предметов за O (1).
Исследователи представили персонализированную матрицу PV для изучения функции encoding, специфичной для пользователя, которая способна моделировать персонализированные взаимные влияния между парой предметов. Поэтому функцию потерь можно сформулировать следующим образом.
Авторы используют предварительно обученную нейронную сеть для создания персонализированных вложений пользователя, которые затем используются в качестве дополнительных функций для модели PRM. Предварительно обученная нейронная сеть извлекается из click-through logs . Дополнительная информация пользователя включает пол, возраст и информацию о покупках.
Evaluation Metrics
1. Precision@k
2. MAP@k
Для онлайн A/B test, использовали PV (pageView), IPV(itemProudctClick), CTR(click-through-rates) и GMV в качестве метрик.
https://arxiv.org/abs/1904.06813
Post #126
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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/
Post #125
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Post #124
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Post #123
50
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
Post #122
39
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
Post #121
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https://youtu.be/5J5L1uCtX_Q
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… https://en.wikipedia.org/wiki/Public_participation_geographic_information_system
https://www.qullab.com/research
https://imprecity.ru/analytics
Post #120
34
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/