> "it is important to note that Creative Commons licenses allow for free reproduction and reuse, so AI programs like ChatGPT might copy text from a Wikipedia article or an image from Wikimedia Commons. However, it is not clear yet whether massively copying content from these sources may result in a violation of the Creative Commons license if attribution is not granted. Overall, it is more likely than not if current precedent holds that training systems on copyrighted data will be covered by fair use in the United States, but there is significant uncertainty at time of writing."
The new Wikimedia Enterprise APIs facilitate attribution. For example, the "api.enterprise.wikimedia.com/v2/structured-contents/{name}" response [2] includes an "editor" object in a "version" object. So the Wikipedia editor who most recently edited the article seems quite feasible to attribute. ML apps could incorporate such attribution in their offering, and help satisfy the "BY" clause in the underlying CC-BY-SA 4.0 license for Wikipedia content.
> I wouldn't be surprised if some Wikipedia editors balk at their volunteer work being actively marketed and reformatted for ease of LLM training
As someone who avidly edited Wikipedia for 6-8 years, I am happy to see my volunteer work used for LLM training. I also agree some other editors likely aren't.
> if you want to have really structured and semi-reliable information you will probably have to rely, at some point, on something like Wikipedia meta-information (DBpedia).
Wikidata is also worth considering for that task. It is:
* Directly linked from Wikipedia [1]
* The data source for many infoboxes [2]
* Seeded with data from Wikipedia
* More active and integrated in community
* Larger in total number of concepts
Wikidata also has initiatives in lexicographic data [3] and images [4, 5].
On the subject of Cyc: the CycL "generalization" (#$genls) predicate inspired Wikidata's "subclass of" property [6], which now links together Wikidata's tree of knowledge.
> * it is hard to imagine this not costing 10s of thousands of dollars and being out of the reach of most high school and college students.*
I would be surprised if Gage and his parents have spent more than $10,000 of their personal money on this hobby. Again, cost near that range is certainly significant, but not monetarily immense for a middle class kid with a consuming hobby and supportive parents over the course of 7+ years.
Gage has been frugal in his choice of college, and gets funding from GoFundMe campaigns. He also seems to have had side jobs. Simply choosing to attend a community college and then an in-state public university as Gage has done -- rather than a private university for 4 years -- is probably enough to defray a huge portion of his hobby's cost.
I suspect Gage is also frugal in his means of travel and lodging. A sibling comment mentions the possibility of photographing candidates that come within a day or two trip of home. I imagine that accounts for most of Gage's photography.
Consider this note from [2]: "Skidmore is a 19-year-old student at Glendale Community College in Phoenix and a freelance graphic designer. A Ron Paul supporter, he began photographing politicians when he was living in Terre Haute, Indiana, attending events held by Rand Paul during his successful 2010 Senate run in Kentucky." The drive from Terre Haute, IN to Lexington, KY is about 4 hours. That's completely doable in a day trip. I've driven 4 hours each way in day trips for similar free culture pursuits. It costs about $80 for gas and food.
> "between states to more than 40 speaking engagements."
Travel among multiple US states to attend 40 speaking engagements over the course of 7 years is not necessarily a major financial burden, even for someone Gage's age.
> "I traveled to nearly every part of the country to cover his political events"
"Part" can be pretty general. One could have covered events in Arizona, Iowa, New Hampshire and, say, Virginia and say one has traveled to nearly every part of the country -- the American West, Midwest, Northeast and South.
> "Skidmore is hot on the campaign trail again, toggling his time between New Hampshire, Iowa, and Arizona"
I think it's much more likely that Gage has been to New Hampshire and Iowa each once or twice in the 2016 campaign season, rather than flying out every weekend or so like a high-level political operative or corporate executive from his Arizona State University dorm room.
Gage's work is probably not prohibitively expensive in terms of money. A few plane tickets to Iowa and New Hampshire every four years, lodging in each for maybe a few days. He also attends Comic-Con every year. He has a good DSLR and lens kit, but that's a one time cost. If he had a paid summer internship in accounting, a side job during the academic year, and supportive parents this all seems doable for a middle class college student attending a public university with in-state tuition.
The vast reach of his photography is likely a sufficient incentive for Gage to invest all that time and significant-but-not-immense money. I hope Gage continues his excellent work.
New York will feature a talk about Wikidata, how to query it with SPARQL, and how we are integrating it with Wikipedia and pushing forward the Semantic Web. Other NYC talks include things like "Git-flow approach to collaborative editing", "Copyright and plot summaries", and "Automated prevention of spam, vandalism and abuse". We will be linking up with San Francisco and likely some other cities for a global teleconference at 4:00 - 5:00 PM ET (21:00 UTC).
> "it is important to note that Creative Commons licenses allow for free reproduction and reuse, so AI programs like ChatGPT might copy text from a Wikipedia article or an image from Wikimedia Commons. However, it is not clear yet whether massively copying content from these sources may result in a violation of the Creative Commons license if attribution is not granted. Overall, it is more likely than not if current precedent holds that training systems on copyrighted data will be covered by fair use in the United States, but there is significant uncertainty at time of writing."
The new Wikimedia Enterprise APIs facilitate attribution. For example, the "api.enterprise.wikimedia.com/v2/structured-contents/{name}" response [2] includes an "editor" object in a "version" object. So the Wikipedia editor who most recently edited the article seems quite feasible to attribute. ML apps could incorporate such attribution in their offering, and help satisfy the "BY" clause in the underlying CC-BY-SA 4.0 license for Wikipedia content.
---
1. https://meta.wikimedia.org/wiki/Wikilegal/Copyright_Analysis...
2. https://enterprise.wikimedia.com/docs/on-demand/#article-str...