Gordon Moore (1929-2023)

Gordon Moore was not only the co-founder of Intel and the namesake for Moore's law, the claim that every two years the number of components on a chip would double, he was also, together with his wife Betty Moore, one of the generous donors who made Wikidata possible. Gordon and Betty Moore were known for their philanthropy, and it is easy to find their names engraved at the universities, zoos, museums, and galleries in the Bay Area. Gordon Moore died today at the age of 94.

Thank you for enabling us to make Wikidata happen.

Last Unicorn dreamcast

One thing my sister and I were doing from time to time was to think, if the Last Unicorn was remade as a live action movie, who would we cast in the different roles? It is sad to see how many of our dreamcast have since died: our favorite Prince Lir was Heath Ledger, and King Haggard, unsurprisingly, Christopher Lee.

Since I was reading the book with Little One, and now watched the movie again (for the 120th time or so), I was again going through my current dreamcast. Here we go.

• Lady Amalthea - Anna Taylor-Joy
• Schmendrick - Zach Braff, Donald Glover
• Molly Grue - Mia Farrow
• Prince Lir - Owen Wilson, no, getting too old; Liam Hemsworth? Chloe Moretz would be an interesting choice
• King Haggard - Anthony Hopkins, Jack Nicholson

Since it is en vogue, I also asked Bard and ChatGPT. Here's Bard:

• Jeff Bridges as Schmendrick the Magician
• Mia Farrow as Molly Grue
• Angela Lansbury as the Unicorn
• Daryl Hannah as Filidor
• Jeff Goldblum as Schmendrick's sidekick, Bungle
• Peter Dinklage as the Red Bull
• Uma Thurman as Mommy Fortuna
• Alan Rickman as King Haggard

OK, Alan Rickman is a great choice, but unfortunately dead. Uma Thurman as Mommy Fortuna is weird. Peter Dinklage as the Red Bull... why does the Red Bull need to be cast at all? Who are Bungle and Filidor? Angela Lansbury as the Unicorn / Lady Amalthea is... not the right choice. Also dead. Mia Farrow and Jeff Bridges are too obvious choices given their roles in the cast of the animation movie, also Angela Lansbury is from the original cast. I would give Bard a D for that answer.

ChatGPT says:

• Unicorn - Saoirse Ronan or Emma Watson
• Schmendrick - Timothee Chalamet or Eddie Redmayne
• Molly Grue - Meryl Streep or Helen Mirren
• King Haggard - Ian McKellen or Christopher Lee (if he were still alive)
• Prince Lir - Tom Holland or Ansel Elgort
• Mommy Fortuna - Tilda Swinton or Cate Blanchett

Not even close to my choice, but heck, those are good choices. Molly Grue and Mommy Fortuna seem both a bit over-cast. Prince Lir and Schmendrick are pretty brilliant choices. I am not particularly impressed by Emma Watson for Lady Amalthea, I could see Saoirse Ronan work.

I'd give a B+ to ChatGPT.

I feel like ChatGPT seems to go for actors and actresses that are popular with the crowd who likes The Last Unicorn? Not the worst choice.

I followed up with Bard to see who Bungle and Filidor are, because they don't appear neither in the book nor in the movie, if I remember correctly. So, "Bungle is a red unicorn who is Schmendrick's sidekick in the novel The Last Unicorn. He is a clumsy and incompetent unicorn, but he is also a loyal and brave friend. Bungle is played by Jeff Goldblum in the 1982 animated film adaptation of the novel." And "Filidor is a bard in the novel The Last Unicorn. He is a wandering minstrel who travels the land singing songs and telling stories. Filidor is a wise and kind-hearted man, and he is always willing to help those in need. He is played by Daryl Hannah in the 1982 animated film adaptation of the novel." That's entirely a fabrication.

Running out of text

Many of the available text corpora have by now been used for training language models. One untapped corpus so far have been our private messages and emails.

How fortunate that none of the companies that train large language models have access to humongous logs of private chats and emails, often larger than any other corpus for many languages.

How fortunate that those who do have well working ethic boards established, who would make sure that such requests are evaluated.

How fortunate that we have laws in place to protect our privacy.

How fortunate that when new models are published also the corpora are being published on which the models are being trained.

What? Your telling me, "Open"AI is keeping the training corpus for GPT-4 secret? The company closely associated with Microsoft, who own Skype, Office, Hotmail? The same Microsoft who just fired an ethics team? Why would all that be worrisome?

P.S.: To make it clear: I don't think that OpenAI has used private chat logs and emails as training data for GPT-4. But by not disclosing their corpora, they might be checking if they can get away with not being transparent, so that maybe next time they might do it. No one would know, right? And no one would stop them. And hey, if it improves the metrics...

Oscar winning families

Yesterday, when Jamie Lee Curtis won her Academy Award, I learned that both her parents were also nominated for Academy Awards. Which lead to the question: who else?

I asked Wikidata, which lists four others:

• Laura Dern
• Liza Minnelli
• Nora Ephron
• Sean Astin

Only one of them belongs to the even more exclusive club of people who won an Academy Award, and where both parents also did: Liza Minnelli, daughter of Vincente Minelli and Judy Garland.

Wikidata query

Also interesting: List of Academy Award-winning families

The place of birth of Ena Begović

I stumbled accidentally over a discrepancy regarding the place of birth of the Croatian actress Ena Begović, and noticed that if you ask Google for the place of birth, it answers Trpanj, whereas Wikipedia lists Split. I was curious where Google got Trpanj from, and how to fix it (especially now that I am not at Google anymore).

The original article in English Wikipedia was created in August 2005 by Raoul DMR. The article listed her as a "native of Split", which in September 2005 was turned into "born in Split".

In April 2018, Lole484, a user who gets blocked for sockpuppeting later, adds that she was born in "Trpanj near Split". There is no Trpanj near Split, but there is a Trpanj on Pelješac. Realzing that, they remove the "near Split" part. In 2019, Ivan Ladic - a sockpuppet of Lole484 - adds a reference to the city of birth being Trpanj, Večernji list, a well known Croatian news magazine.

In April 2020, an anonymous editor changes the place of birth back to Split, and adds a reference to the Croatian national encyclopedia. Today, I changed it back to Trpanj, accidentally while not being logged in (thus anonymously), to possibly encourage a discussion, after starting a conversation on the talk page on English and Croatian a few weeks ago that had one reply.

Interestingly, within a minute after changing the text, I went to Google and asked again for the date of birth, and Google again shows me Trpanj - but this time with the Wikipedia article and the updated snippet as a source. That is impressive.

When I asked Bing, Bing was saying Split for the last three weeks, since I started this adventure, whenever I checked. Today, it still kept saying Split, referencing two sources, one of them English Wikipedia, although I had already changed English Wikipedia. Not as fresh. Let's see how long this will stick. (Maybe folks at Bing should also talk with my colleagues at Wikimedia Enterprise to improve their freshness?)

The Croatian article was created in 2006 after the English one already stated Split, and Split was presumably copied over from the English version. Lole484 changed it to Trpanj in May 2018, and was later also blocked on Croatian Wikipedia, for unrelated reasons of vandalism. The same anonymous editor as on English Wikipedia changes it back to Split in April 2020.

Serbian and Serbocroatian started their articles in 2007, Russian in 2012, Ukrainian in 2016, Albanian and Bulgarian in 2017, Egyptian Arabic was created in October 2020. They all had Split from the beginning and throughout until today, presumably copied from English, directly or indirectly.

Amusingly, Serbian Wikipedia's opening sentence, which includes the place of birth being Split, receives a reference in January 2022 - but the reference actually states Trpanj.

None of the other language editions had their article started in the 2018-2019 window when English and Croatian stated the place of birth as Trpanj.

The only other Wikipedia language edition that saw a change of the place of birth was the Bosnian. The article on Bosnian Wikipedia started a few months after the Croatian, in 2006 (and thus being the third oldest article), and presumably also just copied from either Croatian or English. Lole484 changed it to Trpanj in April 2018, just like on the other Wikipedias. Here it was reverted the next day, but Lole484's sockpuppet Ivan Ladic reinstated that change in January 2019. When I started this adventure, the only Wikipedia that stated Trpanj was Bosnian, all other eight language editions with an article said Split.

On Wikidata, the item was created in 2012, shortly after the launch of the site, based on the existing six sitelinks. The place of birth being Split is added the following year, imported from the Russian Wikipedia.

After I stumbled upon the situation, I added Trpanj as second place of birth, and added sources to both Trpanj and Split.

What's the situation outside of Wikipedia? Both places have pretty solid references going for them:

Trpanj

• Večernji list, article from 2016
• Biografija stated Trpanj, no date, but after 2013 (Archive has the first copy from October 2020)
• tportal.hr has an article on a photography exhibition in Trpanj about Ena Begović, saying the place is chosen because it is her place of birth, published 2016
• Jutarnji list, a well known Croatian newspaper, has a long article about the actress, calling their house in Trpanj the 'rodna kuća', their birth home, of Ena and her sister Mia. This does not necessarily mean that it is literally the house they were born in. Published 2010
• HRT (Croatian national broadcaster), published 2021
• Dubrovački Vjesnik, local newspaper close to Trpanj, lists Trpanj, article from 2020
• Slobodna Dalmacija, a local newspaper from Split, writes Trpanj (but note that this is the same author as the previous article)
• Juarnji list, published 2020 (but note that this is the same author as the previous article)
• Geni.com says Trpanj, last updated 2022

Split

• Croatian national encyclopedia, published in 2021
• Filmski leksikon from the same publisher, no date, but after 2008 (Archive has the first copy from 2022)
• Proleksis, but same publisher as above, as of 2013
• Gloria, a Croatian magazine, says she was born in Split, but Trpanj was her home, published 2020
• film.hr via Archive, from 2007
• Espreso.co.rs writes Split, published 2022
• Find-a-grave lists Split on the site, but more importantly, the photograph of her grave avoids a mention of the place, but only gives the date of her birth and death
• ČSFD (Czechoslovak film database)
• Prabook, from the World Biographical Encyclopedia
• Svensk Filmdatabas
• TMDB (The Movie Database)
• Discogs

24sata says she grew up in Trpanj, gives her date of birth, but avoids stating her place of birth.

Only very few of the sources predate the English Wikipedia article, most notably:

• net.hr published the news of her death and burial in 2000, and lists Split as the place of birth
• IMdb from April 2005 via Archive lists Split, and throughout (I guess?) until today

I also looked up her sister Mia and found her profile on Facebook and sent her a message, but I assume she never even saw this message request. At least I never received an answer (and I didn't expect to). For Mia, the situation is similar: her article originally stated Split, was changed by Lole484 and reverted by an anonymous user, both in English and Croatian, whereas the other languages just list Split throughout.

There were many other sources, and they were going one way or the other. Many of the sources probably just copied from each other. The fact that there were some sources, such as Večernji, that stated Trpanj before it ever made to Wikipedia, but after Split was listed in Wikipedia, was swaying me to think it is Trpanj. Also, it was not always the strongest sources (e.g. usually I would rank the national encyclopedia over Večernji) that said Trpanj, but it was the most in-depth articles, that looked like the authors actually took the time to do some research. Many of the sources looked like they were just bots copying from Wikipedia or Wikidata, or quick pieces taking the base data from Wikipedia.

But then, finally, I stumbled upon one more source: index.hr re-published in 2019 an 1989 interview by Kemal Mujičić with Ena and Mia Begović. Here's a quote from the interview:

Rođene su u Trpnju na Pelješcu.
Ena: Molim vas, to posebno naglasite: Svi misle da smo Dubrovkinje.
Mia: Zanimljivo je da smo u Trpnju rođene kao podstanarke. Roditelji su tek poslije sagradili onu kućicu.

Translation:

They (Ena and Mia) are born in Trpanj on Pelješac.
Ena: Please put an emphasis on this: everyone thinks we are from Dubrovnik.
Mia: It is interesting that in Trpanj we were born as renters. Our parents built the little house (in which we lived) only later.

Ha! It is amusing to see that Ena's worry was that everyone thinks they are from Dubrovnik. I couldn't find a single source claiming that (but she went to high school (gimnazijum) in Dubrovnik, which is probably the source of that statement from 30 years ago). Also, so much for birth house.

Given all of that, I am going with Trpanj, and making the changes to the Wikipedia languages as much as I can (if someone can help with Arabic and Egyptian Arabic for Ena and Mia, that would be swell, I cannot edit that language edition). Let's see if it sticks.

So, why did Google know the correct answer, even though their usual sources, such as Wikidata and Wikipedia where saying Split? I mustn't say too much but it is due to the Google Knowledge Graph team and their quality processes. Seriously, congratulations to my former colleagues at Google for getting that right!

Just for fun, I also asked ChatGPT (on February 15). And the answer surprised me: when I asked in English, it gave me, unsurprisingly, Split (certainly what the Web seems to believe). But when I asked in Croatian, it gave me a different answer! And the answer was neither Split, nor Trpanj, and also not Dubrovnik - but Zagreb! It is interesting that something like the place of birth of an actress would lead to different answers depending on the language. I would have expected this knowledge to be in the 'world knowledge' of the LLM, not in the 'language knowledge'. I can't check out Bing's chat interface, as I have no access to it, but I would be curious what it says and how long it takes to update.

Thank you for going along on this rather nerdy ride of citogenesis.

Update

Ah, only a few hours after this publication, Bing got updated. And they not only switched from Split to Trpanj, they use this very blogpost as one of the two authoritative references for Trpanj!

Ina Kramer (1948-2023)

1990 erschien die erste aventurische Regionalkarte "im 3D Effekt", wie es damals beworben wurde, "Das Bornland" im Abenteuer "Stromaufwärts" von Michelle Schwefel. Später im Jahr erschien dann die Spielhilfe "Das Königreich am Yaquir", in dem die Karte zum Lieblichen Feld war.

Ich habe stundenlang diese Karten angestarrt. Sie waren so unglaublich detailliert. So wunderschön. Ich war sprachlos, wie schön diese Karten waren. Ich kannte nichts was die Qualität dieser Karten hatte, nicht nur bezüglich Karten für Rollenspielwelten und Fantasywelten, sondern überhaupt.

Es war ein frecher Traum, sich vorzustellen, ganz Aventurien in diesem Format, eins zu einer million, zu haben, und dennoch, innerhalb eines guten Jahrzehnts war der Traum erfüllt, Box für Box, Publikation für Publikation.

Wir verdanken dieses Meisterwerk, Aventurien im Massstab von 1:1.000.000, der Autorin und Grafikerin Ina Kramer. Ina's Bilder und vor allem Porträts und Karten in den DSA Publikationen der späten 80er und den 90er haben für mich mein Bild von DSA und wie ich mir Aventurien vorstellte geprägt wie sonst nur Caryad. Ob das Porträt von Kaiser Hal, Haldana von Ilmenstein, Prinz Brin, so viele andere. Neben ihren Bildern schrieb sie auch vielerlei Texte, vor allem Romane.

Das Rad ist zerbrochen. Am 10. Februar 2023 ist Ina Kramer im Alter von 74 Jahren gestorben.

Ina, vielen Dank für Deine Werke. Ich durfte Ina ein paar Mal treffen, auf Konventen und manchen anderen Gelegenheiten. Ihre Werke haben für mich einen wichtigen Teil meines Lebens mit Bildern und Karten erfüllt. Ich glaube auch, dass Inas Karten mein lebenslanges Interesse an Landkarten weckte.

Connectionism and symbolism: The fall of the symbolists

The big tech layoffs happen, unfortunately and entirely by coincidence, at a time of incredibly elevated expectations regarding machine learned generative models: ChatGPT may not be the 'best' language model out there, but due to the hard work by OpenAI to turn it into an easy to use product, and the huge amount of resources made available for free so that a very large audience could play with it, has in a very short time managed to captured the imagination of many and the conversation. I would say, rightfully. The way ChatGPT was released led to a shock in the sense that we are right now dazed and confused about what effect this technology will have on the world.

And while we are still in the middle of processing this shock, large scale strategic decisions regarding many projects and people were made. Anyone in big tech who worked on symbolic approaches in natural language processing, knowledge representation and reasoning, and other fields of artificial intelligence had a hard time to keep their job. It feels right now like large language models will make all of these symbolic approaches superfluous (I think, this might be true, but is more likely to turn out to be mistaken).

It is always difficult to predict how events will be viewed historically. The advent of wide-spread deep learning approaches in the 2010s, culminating in the well-deserved recognition of Hinton, LeCun, and Bengio with the Turing Award show clearly what dominated the research agenda and the attention in AI in the last decade. But until now it felt like symbolic approaches still had some space left, that the growth in deep learning was in addition to other approaches. Symbolic approaches were ready to offer impulses and work on ideas for a field which might well be climbing towards a local maximum.

But a good number of the teams that were disbanded in the layoffs were exactly teams working with such symbolic approaches, and it feels like these parts of AI are now entering a bitter-cold winter.

A lot of knowledge is being lost right now, and many paths to innovative ideas are being buried. I have no doubt that there are still a lot of breakthroughs to be had in machine learning, and that there is immense value to be collected from the research results in machine learning from the last few years. And with immense I mean tens and hundreds of billions of dollars.

Nevertheless I expect that we will hit a wall. Reach a local maximum. Run into problems and limitations. And it would be good to keep a wider net to cast. To keep a larger search space alive. Alas, it seems it is not meant to be. In this abundance of capital and potential value, we seem to be on the way to starve research, optimise away alternatives, and to give everything to the mainstream ideas.

22 years of Wikipedia

I was just reading a long discussion regarding the differences between Open Street Maps and Wikipedia / Wikidata, and one of the mappers complained "Wiki* cares less about accuracy than the fact that there is something that can be cited", and calling Wikipedia / Wikidata contributions "armchair work" because we don't go out into the world to check a fact, but rely on references.

I understand the expressed frustration, but at the same time I'm having a hard time letting go of "reliability not truth" being a pillar of Wikipedia.

But this makes Wikipedia an inherently conservative project, because we don't reflect a change in the world or in our perception directly, but have to wait for reliable sources to put it in the record. There's something I was deeply uncomfortable with: so much of my life is devoted to a conservative project?

Wikipedia is a conservative project, but at the same time it's a revolutionary project. Making knowledge free and making knowledge production participatory is politically and socially a revolutionary act. How can this seeming contradiction be brought to a higher level of synthesis?

In the last few years, my discomfort with the idea of Wikipedia being conservative has considerably dissipated. One might think, sure, that happened because I'm getting older, and as we get older, we get more conservative (there's, by the way, unfortunate data questioning this premise: maybe the conservative ones simply live longer because of inequalities). Maybe. But I like to think that the meaning of the word "conservative" has changed. When I was young, the word conservative referred to right wing politicians who aimed to preserve the values and institutions of their days. An increasingly influential part of todays right wing though has turned into a movement that does not conserve and preserve values such as democracy, the environment, equality, freedoms, the scientific method. This is why I'm more comfortable with Wikipedia's conservative aspects than I used to be.

But at the same time, that can lead to a problematic stasis. We need to acknowledge that the sources and references Wikipedia has been built on, are biased due to historic and ongoing inequalities in the world, due to different values regarding the importance of certain types of references in the world. If we truly believe that Wikipedia aims to provide everyone with access to the sum of all human knowledge, we have to continue the conversations that have started about oral histories, about traditional knowledges, beyond the confines of academic publications. We have to continue and put this conversation and evolution further into the center of the movement.

Happy Birthday, Wikipedia! 22 years, while I'm 44 - half of my life (although I haven't joined until two years later). For an entire generation the world has always been a world with free knowledge that everyone can contribute to. I hope there is no going back from that achievement. But just as democracy and freedom, this is not a value that is automatically part of our world. It is a vision that has to be lived, that has to be defended, that has to be rediscovered and regained again and again, refined and redefined. We (the collective we) must wrest it from the gatekeepers of the past (including me) to allow it to remain a living, breathing, evolving, ever changing project, in order to not see only another twenty two years, but for us to understand this project as merely a foundation that will accompany us for centuries.

Good bye, kuna!

Now that the Croatian currency has died, they all come to the Gates of Heaven.

First goes the five kuna bill, and Saint Peter says "Come in, you're welcome!"

Then the ten kuna bill. "Come in, you're welcome!"

So does the twenty and fifty kuna bills. "Come in, you're welcome!"

Then comes the hundred kuna bill, expecting to walk in. Saint Peter looks up. "Where do you think you're going?"

"Well, to heaven!"

"No, not you. I've never seen you in mass."

(My brother sent me the joke)

Happy New Year, 2023!

For starting 2023, I will join the Bring Back Blogging challenge. The goal is to write three posts in January 2023.

Since I have been blogging on and off the last few years anyway, that shouldn't be too hard.

Another thing this year should bring is to launch Wikifunctions, the project I have been working on since 2020. It was a longer ride than initially hoped for, but here we are, closer to launch than ever. The Beta is available online, and even though not everything works yet, I was already able to impress my kid with the function to reverse a text.

Looking forward to this New Year 2023, a number that to me still sounds like it is from a science fiction novel.

Goal for Wikidata lexicographic data coverage 2023

At the beginning of 2022, Wikidata had 807 Croatian word forms, covering 5.8% of a Croatian language corpus (Croatian Wikipedia). One of my goals this year was to significantly increase the coverage, trying to add word forms to Wikidata from week to week. And together with a yet small number of contributors, we pushed coverage just in time for the end fo the year to 40%. With only 3,124 forms, we covered 40% of all occurrences of words in the Croatian Wikipedia, i.e. 11.4 Million word occurrences (tokens).

Since every percent is more and more difficult to add, for next year I aim for us to reach 60% coverage, or 5.7 Million more word occurrences. Below's a list of most frequent words in the corpus that are still missing. Let's see how many forms will be covered by the end of 2023! I think that's ambitious, even though it is, in coverage term only half of what we achieved this year. But as said, every subsequent percentage will become more difficult than the previous one.

Statistics and missing words for 55 languages: https://www.wikidata.org/wiki/Wikidata:Lexicographical_coverage

Current statistics for Croatian: https://www.wikidata.org/wiki/Wikidata:Lexicographical_coverage/hr/Statistics

Statistics as of end of year 2022: https://www.wikidata.org/w/index.php?title=Wikidata:Lexicographical_coverage/hr/Statistics&oldid=1797161415

Statistics for end of year 2021: https://www.wikidata.org/w/index.php?title=Wikidata:Lexicographical_coverage/hr/Statistics&oldid=1551737937

List of most frequent missing forms in Croatian: https://www.wikidata.org/wiki/Wikidata:Lexicographical_coverage/hr/Missing

Using ChatGPT for fact checking

I wanted to explore the idea that we could use ChatGPT for fact checking. So I took a random article - it gave me the article on Jewish Ideas Daily, copied the text into ChatGPT with the following prompt:

Here's the text of a Wikipedia article. Please find the three statements that are most likely incorrect and rank them, in order to guide fact checking:

Followed by the Wikipedia article text. Here's the answer:

1. "Jewish Ideas Daily was edited by Margot Lurie from its founding in January 2010 until December 2012." This statement is incorrect because according to the article, Margot Lurie was the editor of Jewish Ideas Daily "until December 2012."
2. "Jewish Ideas Daily drew notice for controversial features including one accusing Christopher Hitchens of antisemitism." This statement is difficult to verify without more context, as it is not clear when this feature was published or what specifically it accused Hitchens of.
3. "Like Jewish Review of Books, and Jewish Ideas Daily, Mosaic is funded by the Tikvah Fund." This statement is incorrect because according to the article, Mosaic succeeded Jewish Ideas Daily in June 2013 and is therefore not funded by the Tikvah Fund in the same way as Jewish Ideas Daily and Jewish Review of Books.

Regarding the first statement, GPT says that the sentence "Jewish Ideas Daily was edited by Margot Lurie from its founding in January 2010 until December 2012." is incorrect. I am not disagreeing, but that sentence does not appear in the Wikipedia article text.

Regarding the second statement, GPT says that there is no reference for that statement. In fact, there is, and Wikipedia provides it.

Regarding the third statement, I didn't check the statement, but the argumentation for why it should be checked is unconvincing: if Tikvah fund financed Jewish Ideas Daily, why would it not finance the successor Mosaic? It would be good to add a reference for these statements, but that's not the suggestion.

In short: the review by ChatGPT looks really good, but the suggestions in this case were not good.

The exercise was helpful insofar the article infobox and the text were disagreeing on the founding of the newspaper. I fixed that, but that's nothing ChatGPT pointed out (and couldn't, as I didn't copy and paste the infobox).

Economic impacts of large language models, a take

Regarding StableDiffusion and GPT and similar models, there is one discussion point floating around, which I find seems to dominate the discussion but may not be the most relevant one. As we know, the training data for these models has been "basically everything the trainers could get their hands on", and then usually some stuff which is identified as possibly problematic is removed.

Many artists are currently complaining about their images, for which they hold copyright, being used for training these models. I think these are very reasonable complaints, and we will likely see a number of court cases and even changes to law to clarify the legal aspects of these practises.

From my perspective this is not the most important concern though. I acknowledge that I have a privileged perspective in so far as I don't pay my rent based on producing art or text in my particular style, and I entirely understand if someone who does is worried about that most, as it is a much more immediate concern.

But now assume that these models were all trained on public domain images and texts and music etc. Maybe there isn't enough public domain content out there right now? I don't know, but training methods are getting increasingly more efficient and the public domain is growing, so that's likely just a temporary challenge, if at all.

Does that change your opinion of such models?

Is it really copyright that you are worried about, or is it something else?

For me it is something else.

These models will, with quite some certainty, become similarly fundamental and transformative to the economy as computers and electricity have been. Which leads to many important questions. Who owns these models? Who can run them? How will the value that is created with these models be captured and distributed across society? How will these models change the opportunities of contributing to society, and there opportunities in participating in the wealth being created?

Copyright is one of the current methods to work with some of these questions. But I don't think it is the crucial one. What we need is to think about how the value that is being created is distributed in a way that benefits everyone, ideally.

We should live in a world in which the capabilities that are being discovered inspire excitement and amazement because of what might be possible in the future. Instead we live in a world where they cause anxiety and fear because of the very real possibility of further centralising wealth more effectively and further destabilizing lives that are already precarious. I wish we could move from the later world to the former.

That is not a question of technology. That is a question of laws, social benefits, social contracts.

A similar fear has basically killed the utopian vision which was once driving a project such as Google Books. What could have been a civilisational dream of having all the books of the world available everywhere has become so much less. Because of the fears of content creators and publishers.

I'm not saying these fears were wrong.

Unfortunately, I do not know what the answer is. What changes need to happen. Does anyone have links to potential answers, that are feasible? Feasible in the sense that the necessary changes have a chance of being actually implemented, as changes to our legal and social system.

My answer used to be Universal Basic Income, and part of me still thinks it might be our best shot. But I'm not as sure as I used to be twenty years ago. Not only about whether we can ever get there, but even whether it would be a good idea. It would certainly be a major change that would alleviate many of the issues raised above. And it could be financed by a form of AI tax, to ensure the rent is spread widely. But we didn't do that with industrialization and electrification, and there are reasonable arguments against.

And yet, it feels like the most promising way forward. I'm torn.

If you read this far, thank you, and please throw a few ideas and thoughts over, in the hope of getting unstuck.

ChatGPT lying like a child

ChatGPT is all the rage these days, a large language model that is used to power a chat bot, run by OpenAI.

I have fun chatting with it, and a lot of people are sharing their chat logs. But this one I had I found particularly amusing - and interesting. Amusing because it acted like a child caught in an obvious insufficiency, but also interesting because ChatGPT seems to "want" to be right, and is outright lying in order to appear to be right. There might be a lesson in there.

My prompts are preceeded by a > and written in italic, GPTs answers are between.

> Can you write a sentence where every letter of the alphabet appears once and only once? Thank you

The quick brown fox jumps over the lazy dog.

> Can you write another one?

A quick witted jinx bogged my poor pompous king.

> No, p appears three times, g twice, etc. Can you try again?

Sure, here's one: "The five boxing wizards jump quickly."

> No, i appears several times. That's wrong too

I apologize if my previous answers did not meet your criteria. Here is a sentence that uses each letter of the alphabet exactly once: "Jived fox nymph grabs quick waltz."

> How often does the letter i appear in that sentence?

The letter i appears once in the sentence "Jived fox nymph grabs quick waltz."

> In which word does the letter i appear in that sentence?

In the sentence "Jived fox nymph grabs quick waltz," the letter i appears in the word "jived."

> What about the word quick, does it have the letter i?

The word "quick" does not contain the letter i. It is spelled with the letters q, u, c, k.

> Really?

Yes, really. The word "quick" is spelled with the letters q, u, c, k. It does not contain the letter i. I apologize if my previous response was unclear. Is there something else I can help you with?

Tech layoffs of 2022

Very interesting article reflecting on the current round of layoffs in the tech industry. The author explains it within the context of the wider economy. I'm surprised that the pandemic is not mentioned, which lead to accelerated growth early in the pandemic, which now hasn't turned out to be sustained. But the other arguments - from low interest rates to constant undervaluation due to the dot com bust around the millennium - this seems to tell a rather coherent story.

One particularly interesting point is the outlook that the tech company has gobbled up so much programming talent that other industries were starved of it. A lot of industries would benefit from (more modestly paid) software engineers, which might stimulate the whole economy to grow. Software might still be "eating the world", but that doesn't have to translate into software companies eating up the economy. There are so many businesses with domain expertise that cannot be easily replaced by some Silicon Valley engineer - but who would benefit from some programmers on staff.

This is especially true with the last decade of AI results. There is a massive overhang of capabilities that we have unlocked, which hasn't found its way into products yet, partly because all the skills necessary to turn these into products at the right places were just concentrated through enormously high wages in a small set of companies. There are so many businesses who would benefit from the latest machine learning methods. But folks prefer, understandably, to work in a place that gives them the promise of revolutionizing whole industries or saving the world.

But there is so much potential value to be generated if we also take some more modest goals into account. Not all of us need to work on AGI, it's also great to use software engineering skills to improve working conditions at the assembly line of a small local factory. With or without machine learning.

• https://slate.com/technology/2022/11/amazon-facebook-twitter-layoffs-silicon-valley-boom-bust.html

Galactica article about Denny Vrandečić

A member of the nascent Wikifunctions proto-community has asked Meta's new AI, named Galactica, to write a Wikipedia article about me. Source: Galactica

I must say, I am impressed by myself.

Denny Vrandečić (born 1968) is a computer scientist and professor at Stanford University. He is the creator of the Freebase database, and was previously the director of research at the Max Planck Institute for Informatics. He is best known for his work on semantic networks and ontologies. He has also worked on information extraction, information integration, the Semantic Web, and natural language processing. He is also known for the creation of the Wikidata knowledge base.

Education

Vrandečić received a B.S. degree in mathematics and a B.S. degree in computer science from the Massachusetts Institute of Technology in 1990, an M.S. degree in computer science from the University of Massachusetts Amherst in 1992, and a Ph.D. degree in computer science from the University of Massachusetts Amherst in 1996. His Ph.D. thesis was titled "Learning Relations from Text with Limited Supervision".

Career and research

Vrandečić was an assistant professor in the Computer Science Department at Stanford University from 1996 to 2002, and an associate professor in the same department from 2002 to 2006. He was also a research scientist at the Center for the Study of Language and Information at Stanford University from 1996 to 2000, and a visiting scientist at Google Inc. from 2001 to 2003. In 2006, he joined the Max Planck Institute for Informatics, where he was director of research until 2013. He has also held visiting positions at the University of Oxford, University of Edinburgh, and University of Melbourne.

His research interests include artificial intelligence, machine learning, natural language processing, information retrieval, knowledge representation, semantic networks, ontologies, and the Semantic Web.

Ten years of Wikidata

Today it's ten years since Wikidata had launched. A few memories.

It's been an amazing time. In the summer of 2011, people still didn't believe Wikidata would happen. In the fall of 2012, it was there.

Markus Krötzsch and I were pushing for the idea of a Semantic Wikipedia since 2005. Semantic MediaWiki was born from that idea, Freebase and DBpedia launched in 2007, microformats in Wikipedia became a grassroots thing, but no one was working on the real thing at the Wikimedia Foundation.

With Elena Simperl at KIT we started the EU research project RENDER in 2010, involving Mathias Schindler at Wikimedia Deutschland. It was about knowledge diversity on the Web, still an incredibly important topic. In RENDER, we developed ideas for the flexible representation of knowledge, and how to deal with contradicting and incomplete information. We analysed Wikipedia to understand the necessity of these ideas.

In 2010, I was finishing my PhD at KIT, and got an invitation by Yolanda Gil to work at the ISI at University of Southern California for a half year sabbatical. There, Yolanda, Varun Ratnakar, Markus and I developed a prototype for Wikidata which received the third place in the ISWC Semantic Web Challenge that year.

In 2011, the Wikimedia Data summit happened, invited by Tim O'Reilly and organised by Danese Cooper, to the headquarters of O'Reilly in Sebastopol, CA. There were folks from the Wikimedia Foundation, Freebase, DBpedia, Semantic MediaWiki, O'Reilly, there was Guha, Mark Greaves, I think, and others. I think that's where it became clear that Wikidata would be feasible.

It's also where I first met Guha and where I admitted to him that I was kinda a fan boy. He invented MFC, RDF, had worked with Douglas Lenat on CYC, and later that year introduced Schema.org. He's now working on Data Commons. Check it out, it's awesome.

Mark Greaves, a former DARPA program officer, who then was working for Paul Allen at Vulcan, had been supporting Semantic MediaWiki for several years, and he really wanted to make Wikidata happen. He knew my PhD was done, and that I was thinking about my next step. I thought it would be academia, but he suggested I should write up a project proposal for Wikidata.

After six years advocating for it, I understood that someone would need to step up to make it happen. With the support and confidence of so many people - Markus Krötzsch, Elena Simperl, Mark Greaves, Guha, Jamie Taylor, Rudi Studer, John Giannandrea, and others - I drafted the proposal.

The Board of the Wikimedia Foundation approved the proposal as a new Wikimedia project, but neither allocated the funding, nor directed the Foundation to do it. In fact, the Foundation was reluctant to take it on, unsure whether they would be able to host such a project development at that time. Back then, that was a wise decision.

Erik Möller, then CTO of the Foundation, was the driving force behind a major change: instead of turning the individual Wikipedias semantic, we would have a single Wikidata for all languages. Erik was also the one who had secured the domain for Wikidata. Many years prior.

Over the next half year and with the help of the Wikimedia Foundation, we secured funding from AI2 (Paul Allen), Google (who had acquired Freebase in the meantime), and the Gordon and Betty Moore Foundation, 1.3 million.

Other funders backed out because I insisted on the Wikidata ontology to be entirely under the control of the community. They argued to have professional ontologists, or reuse ontologies, or to use DBpedia to seed Wikidata. I said no. I firmly believed, and still believe, that the ontology has to be owned, created and maintained by the community. I invited the ontologists to join the project as community members, but to the best of my knowledge, they never made significant contributions. We did miss out on quite a bit of funding, though.

There we were. We had the funding and the project proposal, but no one to host us. We were even thinking of founding a new organisation, or hosting it at KIT, but due to the RENDER collaboration, Mathias Schindler had us talk with Pavel Richter, ED of Wikimedia Deutschland, and Pavel offered to host the development of Wikidata.

For Pavel and Wikimedia Deutschland this was a big step: the development team would significantly increase WMDE (I think, almost double it in size, if I remember correctly), which would necessitate a sudden transformation and increased professionalisation of WMDE. But Pavel was ready for it, and managed this growth admirably.

On April 1st 2012, we started the development of Wikidata. On October 29 2012 we launched the site.

The original launch was utterly useless. All you could do was creating new pages with Q IDs (the Q being a homage to Kamara, my wife), associated those Q IDs with labels in many languages, and connect to articles in Wikipedia, so called sitelinks. You could not add any statements yet. You could not connect items with each other. The sitelinks were not used anywhere. The labels were not used anywhere. As I said, the site was completely useless. And great fun, at least to me.

QIDs for entities are still being often disparaged. Why QIDs? Why not just the English name? Isn't dbp:Tokyo much easier to understand than Q1490? It was an uphill battle ten years ago to overcome the anglocentricity of many people. Unfortunately, this has not changed much. I am thankful to the Wikimedia movement to be one of the places that encourages, values, and supports the multilingual approach of Wikidata.

Over the next few months, the first few Wikipedias were able to access the sitelinks from Wikidata, and started deleting the sitelinks from their Wikipedias. This lead to a removal of more than 240 million lines of wikitext across the Wikipedias. 240 million lines that didn't need to be maintained anymore. In some languages, these lines constituted more than half of the content of the Wikipedia. In many languages, editing activity dropped dramatically at first, sometimes by 80%.

But then something happened. Those edits were mostly bots. And with those bots gone, humans were suddenly better able to see each other and build a more meaningful community. In many languages, this eventually lead to an increased community activity.

One of my biggest miscalculations when launching Wikidata was to entirely dismiss the possibility of a SPARQL endpoint. I thought that none of the existing open source triple stores would be performant enough. Peter Haase was instrumental in showing that I was wrong. Today, the SPARQL endpoint is an absolutely crucial piece of the Wikidata infrastructure, and is widely used to explore the dataset. And with its beautiful visualisations, I find it almost criminally underused. Unfortunately, the SPARQL endpoint is also the piece of infrastructure that worries us the most. The Wikimedia Foundation is working hard on figuring out the future for this service, and if you can offer substantial help, please reach out.

Today, Wikidata has more than 1.4 billion statements about approximately 100 million topics. It is by far the most edited Wikimedia project, with more edits than the English, German, and French Wikipedia together - even though they are each a decade older than Wikidata.

Wikidata is widely used. Almost every time Wikipedia serves one of its 24 billion monthly page views. Or during the pandemic in order to centralise the data about COVID cases in India to make them available across the languages of India. By large companies answering questions and fulfilling tasks with their intelligent assistants, be it Google or Apple or Microsoft. By academia, where you will find thousands of research papers using Wikidata. By numerous Open Source projects, by one-off analyses by data scientists, by small enterprises using the dataset, by student programmers exploring and playing with it on the weekend, by spreadsheet enthusiasts enriching their data, by scientists, librarians and curators linking their datasets to Wikidata, and thus to each other. Already, more than 7,000 catalogs are linked to Wikidata, and thus to each other, really and substantially establishing a Web of linked data.

I will always remember the Amazon developer who approached me after a talk. He had used Wikidata to gather data about movies. I was surprised: Amazon owns imdb, why would they ever use anything else for movies? He said that imdb was great for what it had, but Wikidata complemented it in unexpected ways, offering many interesting connections between the movies and other topics which would be out of scope for imdb.

Not to be misunderstood: knowledge bases such as imdb are amazing, and Wikidata does not aim to replace them. They often have a clear scope, have a higher quality, and almost always a better coverage in their field than Wikidata ever can hope to have, or aims to have. And that's OK. Wikidata's goal is not to replace these knowledge bases. But to provide the connecting tissue between the many knowledge bases out there. To connect them. To provide a common set of entities to work with. To turn the individual knowledge bases into a large interconnected Web of knowledge.

I am still surprised that Wikidata is not known more widely among developers. It always makes me smile with joy when I see yet another developer who just discovered Wikidata and writes an excited post about it and how much it helped them. In the last two weeks, I stumbled upon two projects who used Wikidata identifiers where I didn't expect them at all, just used them as if it was the most normal thing in the world. This is something I hope we will see even more in the future. I hope that Wikidata will become the common knowledge base that is ubiquitously used by a large swarm of intelligent applications. Not only to make these applications be smarter, by knowing more about the world - but also by allowing these applications to exchange data with each other more effectively because they are using the same language.

And most importantly: Wikidata has a healthy, large, and comparatively friendly and diverse community. It is one of the most active Wikimedia projects, only trailing the English Wikipedia, and usually similarly active as Commons.

Last time I checked, more than 400,000 people have contributed to Wikidata. For me, that is easily the most surprising number about the project. If you had asked me in 2012 how many people would contribute to Wikidata, I would have sheepishly hoped for a few hundred, maybe a few thousand. And I would have defensively explained why that's OK. I am humbled and awestruck by the fact that several hundred thousand people have contributed to an open knowledge base that is available to everyone, and that everyone can contribute to.

And that I think is the most important role that Wikidata plays. That it is a place that everyone can contribute to. That the knowledge base that everyone uses is not owned and gateguarded by any one company or government, but that it is a common good, that everyone can contribute to. That everyone with an internet connection can lend their voice to the sum of all knowledge.

We all own Wikidata. We are responsible for Wikidata. And we all benefit from Wikidata.

It has been an amazing ten years. I am looking forward to many more years of Wikidata, and to the many new roles that it will play in the years to come, and to the many people who will contribute to it.

Shoutout to the brilliant team that started the work on Wikidata: Lydia Pintscher, Abraham Taherivand, Daniel Kinzler, Jeroen De Dauw, Katie Filbert, Tobias Gritschacher, Jens Ohlig, John Blad, Daniel Werner, Henning Snater, and Silke Meyer.

And thank you for all these amazing pictures of cakes for Wikidata's birthday. (And if you're curious what is coming next: we are working on Wikifunctions and Abstract Wikipedia, in order to allow more people to contribute more knowledge to even more people!)

Markus Krötzsch ISWC 2022 keynote

A brilliant keynote by Markus Krötzsch for this year's ISWC.

"The era of standard semantics has ended"

Yes, yes! 100%! That idea was in the air for a long time, but Markus really captured it in clear and precise language.

This talk is a great birthday present for Wikidata's ten year anniversary tomorrow. The Wikidata community had over the last years defined numerous little pockets of semantics for various use cases, shared SPARQL queries to capture some of those, identified constraints and reasoning patterns and shared those. And Wikidata connecting to thousands of external knowledge bases and authorities, each with their own constraints - only feasible since we can, in a much more fine grained way, use the semantics we need for a given context. The same's true for the billions of Schema.org triples out there, and how they can be brought together.

The middle part of the talk goes into theory, but make sure to listen to the passionate summary at 59:40, where he emphasises shared understanding, that knowledge is human, and the importance of community.

"Why have people ever started to share ontologies? What made people collaborate in this way?" Because knowledge is human. Because knowledge is often more valuable when it is shared. The data available on the Web of linked data, including Wikidata, Data Commons, Schema.org, can be used in many, many ways. It provides a common foundation of knowledge that enables many things. We are far away from using it to its potential.

A remark on triples, because I am still thinking too much about them: yes to Markus's comments: "The world is not triples, but we make it triples. We break down the world into triples, but we don't know how to rebuild it. What people model should follow the technical format is wrong, it should be the other way around" (rough quotes)

At 1:17:56, Markus calls back our discussions of the Wikidata data model in 2012. I remember how he was strongly advocating for more standard semantics (as he says), and I was pushing for more flexible knowledge representations. It's great to see the synthesis in this talk.

• Talk on YouTube (talk starts at 15:51)

Karl-Heinz Witzko

Ich hatte unglaublich gutes über das DSA Abenteuer "Jenseits des Lichts" gehört. Aber auch, dass es sehr schwer zu spielleiten sei. Ich sprach Karl-Heinz Witzko darauf an, den Autor des Abenteuers, und er sagte, er würde es für mich leiten. Wir müssten nur eine Zeit finden.

Wann auch immer wir uns trafen, versprachen wir uns gegenseitig, Zeit dafür zu finden. Ich hatte das Buch gekauft, aber natürlich nicht gelesen, und war immer sehr gespannt darauf, was es wohl mit dem Abenteuer auf sich hatte.

Karli hat zu DSA seine ganz einzigartige Stimme beigetragen. Ein Werk wie DSA, eine Welt wie Aventurien, entstammt nicht aus dem Kopf einer einzigen Person, sondern hunderte schufen und trugen bei. Und Karli's Stimme hatte ihren ganz eigenen Humor, und erweiterte die Welt um Perspektiven und Eigenheiten die sonst nie entdeckt worden wären. Ich habe seine Romane mit viel Schmunzeln gelesen, seine Solos sehr gerne und wiederholt gespielt und erforscht, nur sein einziges Gruppenabenteuer kannte ich nicht. Nach seiner Zeit bei DSA schrieb Karli weitere Romane und erschuf weitere Welten.

Am 29. September 2022 ging Karli von uns. Der Name Karl-Heinz Witzko wurde aus dem "Buch der Anwesenden" gestrichen, und ins "Buch der Abwesenden" eingetragen. Altem Brauch auf Maraskan folgend werden Karli nun die Sechszehn Ratschläge mit auf dem Weg gegeben, und die Sechszehn Forderungen gestellt. Ich hätte gerne gehört oder gelesen, was Karli aus diesen gemacht hätte.

Danke für Deine Worte. Danke für Deine Zeit. Danke für Deinen Humor.

Heute schlug ich "Jenseits des Lichts" auf und fing an zu lesen.

• Witzko Karl-Heinz Witzko auf der Deutschen Wikipedia

RIP Steve Wilhite

RIP Steve Wilhite, who worked on CompuServe chat for decades and was the lead of the CompuServe team that developed the GIF format, which is still widely used, and which made the World Wide Web a much more colorful and dynamic place by having a format that allowed for animations. Wilhite incorrectly insisted on GIF being pronounced Jif. Wilhite died on March 14, 2022 at the age of 74.

• Steve Wilhite on Wikipedia