Talk:Crowdsourcing
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Collaborative Mapping
Added collaborative mapping in "see also". Do not know, if a seperate small section for "collaborative mapping" will be appropriate here. Mapping products like
Crowdsourcing in machine learning
Hello everyone,
We would like to suggest changes to this article. The crowdsourcing approach is successfully applied in machine learning as well. Researchers and practitioners in machine learning use crowdsourcing techniques to collect training data sets for ML models of various data science applications. In contrast to traditional in-house editorial-based data collection methods that are usually slow and expensive, crowdsourcing helps them to get data sets in a very short period of time and in a fairly inexpensive way.
In particular, to train self-driving cars to recognize objects on roads, engineers collect labeled images with people, road signs, traffic lights, and other objects on the streets. Voice assistants are trained with audio recorded and labeled by thousands of crowd workers with different voices and accents. For developing a search engine ranking model, engineers ask crowd workers to assess if search engine results match user queries. Among other applications that are trained on labeled data sets are translators, optical character recognition programs, chatbots, navigators, taxi mobile apps, etc.
Data labeling tasks can be designed and programmed into an open crowdsourcing platform. These platforms provide on-demand access to a large crowd of workers and some tools that help to control the quality of responses. Another common way is to use internal tools, such as internal crowdsourcing platforms, or ready-made solutions.
References:
1. Alonso O. (2019). The Practice of Crowdsourcing. Ed. by G. Marchionini. Synthesis Lectures on Information Concepts, Retrieval, and Services. Morgan & Claypool Publishers. https://www.google.ru/books/edition/The_Practice_of_Crowdsourcing/rq2aDwAAQBAJ?hl=ru&gbpv=0&bsq=Omar%20Alonso
2. Alexey Drutsa, Viktoriya Farafonova, Valentina Fedorova, Olga Megorskaya, Evfrosiniya Zerminova, and Olga Zhilinskaya. 2019. Practice of Efficient Data Collection via Crowdsourcing at Large-Scale. Tutorial at the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '19). https://arxiv.org/pdf/1912.04444.pdf
3. Nikita Pavlichenko, Ivan Stelmakh, Dmitry Ustalov. 2020. CrowdSpeech and Vox DIY: Benchmark Dataset for Crowdsourced Audio Transcription. NeurIPS 2021 Datasets and Benchmarks Track (Round 1). https://openreview.net/forum?id=3_hgF1NAXU7 — Preceding unsigned comment added by Crowdfollowsme (talk • contribs) 11:38, 6 October 2021 (UTC)
--Crowdfollowsme (talk) 12:01, 6 October 2021 (UTC)
- Well, there were multiple bold attempts to add such section recently, e.g. https://en.wikipedia.org/w/index.php?title=Crowdsourcing&oldid=1038076350, which were reverted as they looked as a very opionated claims regarding ML (e.g. the crowdsouring approach being applied by the majority of products based on ML), written as a reason to cite Yandex/Toloko platform, to which you are affiliated, and some selected online courses from Internet. At the same time there is an existing sub-section "Microwork" which I believe describe the same stuff, citing Amazon's MTurk. I'd say, you better write about Yandex/Toloko in there, expanding the section, but avoiding claims that this is the only/best way to do ML. Birdofpreyru (talk) 20:21, 6 October 2021 (UTC)
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