AI

ai@lists.wikimedia.org

1 participants
265 discussions

Wikimania Submissions (Deadline today!)
by Aaron Halfaker 10 Apr '17

10 Apr '17

Hey folks, I've been working on a couple of submissions for Wikimania. I wanted to take the opportunity to call your attention to them and possibly to entice anyone out there to file their own. https://wikimania2017.wikimedia.org/wiki/Submissions/The_story_of_building_… https://wikimania2017.wikimedia.org/wiki/Submissions/The_Keilana_Effect:_Vi… -Aaron

1 0

WikiProject assessments vs. external reviewers vs. ORES
by Aaron Halfaker 06 Apr '17

06 Apr '17

Cross-posting from: https://en.wikipedia.org/wiki/Wikipedia:Village_pump_(miscellaneous)#WikiPr… Hey folks, I have been collaborating with some researchers who are publishing a dataset of externally reviewed Wikipedia articles (the sample was taken back in 2006). I'd like to take the opportunity to compare the prediction quality of ORES' article quality model with these external reviewers, but in order get a good picture of the situation, it would also be very helpful to get a set of Wikipedian assessments[1] for the same dataset. So, I have gathered all of the versions of externally reviewed articles in User:EpochFail/ORES_audit[2] and I'm asking for your help to gather assessments. There's 90 old revisions of articles that I need your help assessing. I don't think this will take long, but I need to borrow your judgement here to make sure I'm not biasing things. To help out, see User:EpochFail/ORES_audit[2]. The more we know about how ORES performs against important baselines, the better use of it we can make it to measure Wikipedia and direct wiki work. 1. https://en.wikipedia.org/wiki/Wikipedia:WikiProject_assessment 2. https://en.wikipedia.org/wiki/User:EpochFail/ORES_audit Thanks! -Aaron

1 0

Another round of name that thing
by Erik Bernhardson 05 Apr '17

05 Apr '17

We seem to have some consensus that for the upcoming learning to rank work we will build out a python library to handle the bulk of the backend data plumbing work. The library will primarily be code integrating with pyspark to do various pieces such as: # Sampling from the click logs to generate the set of queries + page's that will be labeled with click models # Distributing the work of running click models against those sampled data sets # Pushing queries we use for feature generation into kafka, and reading back the resulting feature vectors (the other end of this will run those generated queries against either the hot-spare elasticsearch cluster or the relforge cluster to get feature scores) # Merging feature vectors with labeled data, splitting into test/train/validate sets, and writing out files formatted for whichever training library we decide on (xgboost, lightgbm and ranklib are in the running currently) # Whatever plumbing is necessary to run the actual model training and do hyper parameter optimization # Converting the resulting models into a format suitable for use with the elasticsearch learn to rank plugin # Reporting on the quality of models vs some baseline The high level goal is that we would have relatively simple python scripts in our analytics repository that are called from oozie, those scripts would know the appropriate locations to load/store data and pass into this library for the bulk of the processing. There will also be some script, probably within the library, that combines many of these steps for feature engineering purposes to take some set of features and run the whole thing. So, what do we call this thing? Horrible first attempts: * ltr-pipeline * learn-to-rank-pipeline * bob * cirrussearch-ltr * ???

2 2

Fwd: [Commons-l] Programmatically categorizing media in the Commons with Machine Learning
by Pine W 03 Apr '17

03 Apr '17

Forwarding. Pine ---------- Forwarded message ---------- From: "Jordan Adler" <jmadler(a)google.com> Date: Aug 11, 2016 13:06 Subject: [Commons-l] Programmatically categorizing media in the Commons with Machine Learning To: "commons-l(a)wikimedia.org" <commons-l(a)lists.wikimedia.org> Cc: "Ray Sakai" <rsakai(a)reactive.co.jp>, "Ram Ramanathan" < ramramanathan(a)google.com>, "Kazunori Sato" <kazsato(a)google.com> Hey folks! A few months back a colleague of mine was looking for some unstructured images to analyze as part of a demo for the Google Cloud Vision API <https://cloud.google.com/blog/big-data/2016/05/explore-the-galaxy-of-images…>. Luckily, I knew just the place <https://commons.wikimedia.org/wiki/Category:Media_needing_categories>, and the resulting demo <http://vision-explorer.reactive.ai/>, built by Reactive Inc., is pretty awesome. It was shared on-stage by Jeff Dean during the keynote <https://www.youtube.com/watch?v=HgWHeT_OwHc&feature=youtu.be&t=2h1m19s> at GCP NEXT 2016. I wanted to quickly share the data from the programmatically identified images so it could be used to help categorize the media in the Commons. There's about 80,000 images worth of data: - map.txt <https://storage.googleapis.com/gcs-samples2-explorer/reprocess/map.txt> (5.9MB): A single text file mapping id to filename in a "id : filename" format, one per line - results.tar.gz <https://storage.googleapis.com/gcs-samples2-explorer/reprocess/results.tar.…> (29.6MB): a tgz'd directory of json files representing the output of the API <https://cloud.google.com/vision/reference/rest/v1/images/annotate#response-…>, in the format "${id}.jpg.json" We're making this data available under the CC0 license, and these links will likely be live for at least a few weeks. If you're interested in working with the Cloud Vision API to tag other images in the Commons, talk to the WMF Community Tech team. Thanks for your help! _______________________________________________ Commons-l mailing list Commons-l(a)lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/commons-l

2 1

The Revision Scoring weekly update
by Aaron Halfaker 16 Mar '17

16 Mar '17

Hey folks! This is the 32 - 41st weekly update from the revision scoring team that we have sent to this mailing list. We've been busy, but our reporting fell behind. So here I am getting us caught up! This is going to be a long one. Bear with me. One major thing we've done in the past few weeks is drafted and presented a proposal to increase the resourcing for the ORES project in the 2017 Fiscal Year. Currently, we're just one fully funded staff member (halfak) and partially funded contractor (Amir1) working with a bunch of volunteers. We're proposing to staff the team with fulltime engineers, a liaison and a tech writer. See a full draft of our proposal and pitch deck here: https://meta.wikimedia.org/wiki/Wikimedia_Foundation_Scoring_Platform_team *New development:* We've expanded support for our "editquality" models to more wikis and improved the performance of some of the models. - We scaled up the number of observations for Indonesian Wikipedia to 100k[1] - We added language support for Romanian[2] and built the basic "reverted" model[3] - We trained and tested "damaging" and "goodfaith" models for Czech Wikipedia[4] - We implemented some params in our training utilites to control memory usage[5] - We deployed all of the above to Wikimedia Labs[6]. A production deployment is coming soon. Prompted by the 2016 community wishlist[7], we've implemented a "draftquality" model for evaluating new page creations. - We researched deletion reasons on English Wikipedia[8] and created a labeled dataset using the deletion log. - We engineered a set of features to predict the quality of new articles[9] and built a model[10] - We generated a set of datasets[11,12,13] to make it easier for volunteers and external researchers to help us audit the performance of the model. - We deployed the model on WMFLabs[14] and announced it's presence to a few interested patrollers in English Wikipedia - We've started the process of deploying the model in production[15,16] We completed a project exploring the use of advance natural-language processing strategies to extract new signal about vandalism, article quality and problematic new articles. Regretfully, memory issues prevent us from trivially putting this into production[17], so we're looking into alternative strategies[18]. - We implemented a strategy for extracting sentence from Wikitext[19] - We built sentence banks for personal attacks[20, vandalism[21], spam[22], and Featured Articles[23]. - We built PCFG-based models[24] and analyzed their ability to differentiate[25] We've been working with the Collaboration Team[26] on their Edit Review Improvments project[27] - We defined and implemented a set of new precision-based test statistics that will inform thresholds used in their new user interface[28] - But we also decided to continue to report recall-based test statistics as well[29] Based on advice from engineers on the Collaboration Team, we've begun the process of converting Wiki labels[30] to a stand-alone tool in labs. - We generalize the gadget interface so that it can handle all langauges/wikis[31] - We implemented a means to auto-configure wikis based on the dbname[32,33] and that allowed us to simplify configuration[34] - We also implemented some performance improvements with minification, bundling[35] *Labeling:* In the past few weeks, we've set up labeling campaigns for a few wikis. - We deployed an edit types campaign for Catalan Wikipedia[36] - We deployed an edit quality campagin for Chinese[37] and Romanian[38] Wikipedias - We deployed a new type of campaign for English Wikipedia -- "discussion quality" asks editors to label talk posts as "toxic" or not[39] *Maintenance and robustness:* We've solved a large set of problems with logging issues, compatibility with wikibase, and we've made minor improvements to performance. - We addressed a few bugs in the ORES Review Tool[40,44] - We quieted some errors from our logging in ORES[41,45] - We updated our code to work with a wikibase schema change[42] - We fixed a language fallback pattern in Wiki labels[43] - We set up monitoring on ORES database disk sizes[46] - We fixed some issues with scap, phabricator's diffusion and other supporting systems so that we can continue deploying to beta labs[47] - We split our assets repo so that we can let our WMFLabs deploy get ahead of the Production deployment[48] - ORES can now minify its JSON responses[49] - We identified a bug in flask-assets and worked around it in our local installation of Wiki labels[50] *Communications and outreach:* We had a big presence at the Wikimedia Developer summit, we've drafted a resourcing proposal, and we've made some announcements about upcoming plans for the ORES Review tool. - We facilitated the "Artificial Intelligence to build and navigate content" track[51] - We ran a session for building an AI wishlist[52] and captured notes about more than 20 new AI proposals on a new tag in phabricator[53] - We also ran a session discussion the ethics and dangers of advanced algorithms mediating our processes[54] - We helped facilitate a session about where to surface current AIs in Wikimedia Projects[55] - We held a discussion with Legal about licensing labeled data that comes out of Wiki labels[56] and updated the interface to state the CC0 license clearly[57] - We worked with the Reading Infrastructure team to analyze the consumption of "oresscores" through the MediaWiki API[58] - We drafted a pitch for increasing the resources for our team[59] - We worked with the Collaboration team to announce that they'll experimenting with a new RecentChanged filtering strategy in the ORES Review Tool[60,61] 1. https://phabricator.wikimedia.org/T147107 -- Scale up the number of observations for idwiki to 100k 2. https://phabricator.wikimedia.org/T152482 -- Add language support for Romanian 3. https://phabricator.wikimedia.org/T156504 -- Build reverted model for Romanian Wikipedia 4. https://phabricator.wikimedia.org/T156492 -- Train and test damaging/goodfaith models for Czech Wikipedia 5. https://phabricator.wikimedia.org/T156645 -- Add '--workers' param to cv_train utility 6. https://phabricator.wikimedia.org/T154856 -- Clean up dependencies and deploy newest ORES & Models in labs 7. https://meta.wikimedia.org/wiki/2016_Community_Wishlist_Survey/Categories/M… 8. https://meta.wikimedia.org/wiki/Research:Automated_classification_of_draft_… 9. https://phabricator.wikimedia.org/T148580 -- Build feature set for draft quality model 10. https://phabricator.wikimedia.org/T148038 -- [Epic] Build draft quality model (spam, vandalism, attack, or OK) 11. https://phabricator.wikimedia.org/T148581 -- Extract features for deleted page (draft quality model) 12. https://phabricator.wikimedia.org/T156642 -- Generate scored dataset for 2016-08 - 2017-01 13. https://phabricator.wikimedia.org/T156643 -- Generate extracted features for 2016-08 - 2017-01 14. https://phabricator.wikimedia.org/T155576 -- Deploy draftquality models to WMFLabs 15. https://phabricator.wikimedia.org/T156835 -- Create package stuff for draftquality 16. https://phabricator.wikimedia.org/T157049 -- Create new repo: research-ores-draftquality 17. https://phabricator.wikimedia.org/T148867#2816566 -- Memory footprint is enormous! 18. https://phabricator.wikimedia.org/T155111 -- [Spike] Investigate use of Apertium LTtoolbox API in labs/production 19. https://phabricator.wikimedia.org/T148867 -- Implement sentences datascources 20. https://phabricator.wikimedia.org/T148035 -- Sentence bank for personal attacks 21. https://phabricator.wikimedia.org/T148034 -- Sentence bank for vandalism 22. https://phabricator.wikimedia.org/T148032 -- Sentence bank for spam 23. https://phabricator.wikimedia.org/T148033 -- Sentence bank for Featured Articles 24. https://phabricator.wikimedia.org/T148037 -- Generate PCFG sentence models 25. https://phabricator.wikimedia.org/T151819 -- Analyze differentiation of FA, Spam, Vandalism, and Attack models/sentences. 26. https://www.mediawiki.org/wiki/Collaboration 27. https://www.mediawiki.org/wiki/Edit_Review_Improvements 28. https://phabricator.wikimedia.org/T151970 -- Implement new precision-based test stats for editquality models 29. https://phabricator.wikimedia.org/T156644 -- Restore recall-threshold-based metrics for editquality models. 30. https://meta.wikimedia.org/wiki/Wiki_labels 31. https://phabricator.wikimedia.org/T151120 -- Generalize standalone gadget interface 32. https://phabricator.wikimedia.org/T154433 -- Auto config wikilabels using dbnames 33. https://phabricator.wikimedia.org/T155439 -- Use module loader to load JS/CSS from wikis 34. https://phabricator.wikimedia.org/T154693 -- Remove host from wikilabels config -- infer from request 35. https://phabricator.wikimedia.org/T154122 -- Minification and bundling for wikilabels assets 36. https://phabricator.wikimedia.org/T152965 -- Deploy cawiki edit types campaign 37. https://phabricator.wikimedia.org/T152561 -- Deploy zhwiki edit quality campaign 38. https://phabricator.wikimedia.org/T156357 -- Deploy edit quality campaign for Romanian Wikipedia 39. https://phabricator.wikimedia.org/T156303 -- Deploy "Discussion quality" campaign in wikilabels 40. https://phabricator.wikimedia.org/T152542 -- Undefined method ORES\Hooks::getDamagingThreshold() 41. https://phabricator.wikimedia.org/T146681 -- Quiet TimeoutError in celery logging 42. https://phabricator.wikimedia.org/T154168 -- Quantity changes broke ORES 43. https://phabricator.wikimedia.org/T154897 -- Chinese translations are not being loaded 44. https://phabricator.wikimedia.org/T155500 -- Fatal exception of type "DBQueryError" on sorting ORES contributions 45. https://phabricator.wikimedia.org/T157078 -- ores logspam: Model contains an error 46. https://phabricator.wikimedia.org/T155482 -- Set up monitoring for ORES redis database 47. https://phabricator.wikimedia.org/T157135 -- Fix broken beta-labs deploy 48. https://phabricator.wikimedia.org/T154436 -- Split wheels repo into Prod/WMFLabs branches and maintain independence 49. https://phabricator.wikimedia.org/T155931 -- Minify json responses 50. https://phabricator.wikimedia.org/T154865 -- assets url return empty string 51. https://phabricator.wikimedia.org/T147708 -- Artificial Intelligence to build and navigate content 52. https://phabricator.wikimedia.org/T147710 -- What should an AI do you for you? Building an AI Wishlist. 53. https://phabricator.wikimedia.org/tag/artificial-intelligence/ 54. https://phabricator.wikimedia.org/T147929 -- Algorithmic dangers and transparency -- Best practices 55. https://phabricator.wikimedia.org/T148690 -- Where to surface AI in Wikimedia Projects 56. https://phabricator.wikimedia.org/T145024 -- Licensing of labeled data 57. https://phabricator.wikimedia.org/T156052 -- Add notice of CC0 status of Wikilabels data to UI & Docs 58. https://phabricator.wikimedia.org/T156273 -- Identify baseline api.php Action API consumption 59. https://phabricator.wikimedia.org/T157470 -- Draft proposal/pitch for ORES resourcing 60. https://phabricator.wikimedia.org/T150855 -- Gather assets for post about ORES review tool including ERI filters 61. https://phabricator.wikimedia.org/T150858 -- Post about ORES review tool including ERI filters Sincerely, Aaron from the Revision Scoring Scoring Platform team

1 1

Application to Represent AI in my region
by Owhorji Shadrach 17 Feb '17

17 Feb '17

Dear sir /ma, I am Shadrach Promise Owhorji, from ikwerre local government area of Rivers State of the federal Republic of Nigeria. I am an adult male citizen, aged 29 years old. I am a human rights activist, Educationist and also a political scientist by profession. Having seen the good work of the AI, and having glance through the past and present records of the AI, have just decided to be part of the Community that affects people's lives positively. I really wish to be giving the opportunity and audience to push and defend the course of the Amnesty International in my region. Having stipulated the above, my request is therefore your approval to start up the campaign of AI here, perhaps engage myself with training and retraining of the Amnesty supporters around the world. I also call on your office to provide me the required documents in order to to have issues with representation here in Nigeria. I will be glad if my request will be granted. I anticipate your cooperation. Sincerely, Comrade, Shadrach Promise Owhorji......

2 1

Artificial Intelligence and the Law (Panel discussion from 2016)
by Aaron Halfaker 15 Feb '17

15 Feb '17

I just found this talk by the Wikimedia Legal interns from July of 2016. See https://www.youtube.com/watch?v=2Y_4HCtxvtw The Wikimedia legal interns will be hosting a panel titled "Artificial > Intelligence and the Law." The discussion (lasting about an hour) will > focus on the intersection of emerging technologies, like driverless cars, > web crawlers, and lethal autonomous weapons, and the legal issues they > raise, including in the areas of employment and liability. The panel > speakers will explore the legal challenges presented by emerging > technologies and artificial intelligence and how the legal industry has > responded to those challenges. -Aaron

1 0

Re: [AI] AI Digest, Vol 15, Issue 5
by Owhorji Shadrach 10 Feb '17

10 Feb '17

Good evening sir, Please I don't understand this information, Could you please explain to me more about this message. Thank you, Sincerely, Shadrach.... On Feb 10, 2017 1:00 PM, <ai-request(a)lists.wikimedia.org> wrote: > Send AI mailing list submissions to > ai(a)lists.wikimedia.org > > To subscribe or unsubscribe via the World Wide Web, visit > https://lists.wikimedia.org/mailman/listinfo/ai > or, via email, send a message with subject or body 'help' to > ai-request(a)lists.wikimedia.org > > You can reach the person managing the list at > ai-owner(a)lists.wikimedia.org > > When replying, please edit your Subject line so it is more specific > than "Re: Contents of AI digest..." > > > Today's Topics: > > 1. personal AI (Toby Negrin) > 2. Re: personal AI (Aaron Halfaker) > 3. Re: personal AI (Toby Negrin) > > > ---------------------------------------------------------------------- > > Message: 1 > Date: Thu, 9 Feb 2017 12:25:19 -0800 > From: Toby Negrin <tnegrin(a)wikimedia.org> > To: Application of Artificial Intelligence and other advanced > computing strategies to Wikimedia Projects <ai(a)lists.wikimedia.org > > > Subject: [AI] personal AI > Message-ID: > <CAAjh0ExupEFz32Ham_fmv=sPW9EHDfQGSFc+-d91FSGD1p9p7g@mail. > gmail.com> > Content-Type: text/plain; charset="utf-8" > > Hey ORES team -- we need 1,000,000,000 new models :) > > https://qz.com/906751/tech-companies-are-building-tiny- > personal-ais-to-keep-your-messages-private/ > > -Toby >

1 0

personal AI
by Toby Negrin 09 Feb '17

09 Feb '17

Hey ORES team -- we need 1,000,000,000 new models :) https://qz.com/906751/tech-companies-are-building-tiny-personal-ais-to-keep… -Toby

2 2

Fwd: [Wikidata] WMDE looking for a data analyst
by Aaron Halfaker 07 Feb '17

07 Feb '17

Just saw this fly by. Thought someone on this list might be interested. ---------- Forwarded message ---------- From: Léa Lacroix <lea.lacroix(a)wikimedia.de> Date: Tue, Feb 7, 2017 at 2:45 AM Subject: [Wikidata] WMDE looking for a data analyst To: "Discussion list for the Wikidata project." < wikidata(a)lists.wikimedia.org> Hello all, Our development team is looking for a data analyst, in freelance, remotely, to work mostly on Wikidata. The person will: - Work closely with product managers and UX researchers to maintain and improve detailed on-going analysis of the department’s products, their usage patterns and performance. - Write database queries and supporting code to analyze usage volume, user behaviour and performance data to identify opportunities and areas for improvement. - Collaborate with other analysts in the department to maintain our department’s dashboards, ensuring they are up-to-date, accurate, fair and focussed on representations of our product efficiency. - Support product managers through rapidly surfacing positive and adverse data trends, and complete ad hoc analysis support as needed. - Communicate clearly and responsively your findings to a range of departmental, organisational, volunteer and public stakeholders in order to inform and educate them. If you want to know more and apply: https://software.wikimedia.de/ jobs/data-analyst See also our other job offers: https://software.wikimedia.de/jobs -- Léa Lacroix Project Manager Community Communication for Wikidata Wikimedia Deutschland e.V. Tempelhofer Ufer 23-24 10963 Berlin www.wikimedia.de Wikimedia Deutschland - Gesellschaft zur Förderung Freien Wissens e. V. Eingetragen im Vereinsregister des Amtsgerichts Berlin-Charlottenburg unter der Nummer 23855 Nz. Als gemeinnützig anerkannt durch das Finanzamt für Körperschaften I Berlin, Steuernummer 27/029/42207. _______________________________________________ Wikidata mailing list Wikidata(a)lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/wikidata

1 0

← Newer
1
...
15
16
17
18
19
20
21
...
27
Older →

Jump to page:

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

AI