It’s relatively easy for those involved in the entertainment industry in Asia to get caught up in geopolitical scuffles, with with social media accelerating and magnifying any faux pas.
From the Japan Times I learned about how some hololive vTubers or Virtual YouTubers g[o]t caught in the middle of a diplomatic spat. The vTuber Kiryu Coco, who is apparently a young (3,500 years young) dragon, showed a visualization that mentioned Taiwan as different from China and therefore ticked off Chinese fans which led to hololive releasing apologies. Young dragons don’t yet know about the One-China policy. To make matters worse the apologies/explanations published in different countries were different which was noticed and that needed further explanation. Such are the dangers of trying to appeal to both the Chinese, Japanese and US markets.
Not knowing much about vTubers I poked around the hololive site. An interesting aspect of the English site is the information in the FAQ about what you can send or not send your favorite talent. Here is their list of things hololive will not accept from fans:
– ALL second hand/used/opened up items that do NOT directly deliver from e-commerce sites such as Amazon (excluding fan letters and message cards)
– Luxury items (individual items which cost more than 30,000 yen)
– Living beings or raw items (including fresh flowers, except flower stands for specified venues and events)
– Items requiring refrigeration
– Handmade items (excluding fan letters and message cards)
– All sorts of stuffed toys, dolls, cushions (no exceptions)
– Currencies (cash, gift cards, coupons, tickets, etc.)
– Cosmetics, perfumes, soap, medicines, etc.
– Dangerous goods (explosives, knives/weapons, drugs, imitation swords, model guns, etc.)
– Clothes, underwear (Scarves, gloves, socks, and blankets are OK)
– Amulets, talismans, charms (items related to religion, politics, or ideological expressions)
– Large items (sizes where the talents would find it impossible to carry home alone)
– Pet supplies
– Items that may violate public order and moral
– Items that may violate laws and regulations
– Additional items (the authorities will perform final confirmation and judgment)
I feel this list is a distant relative of Borges’ taxonomy of animals taken from the fictional Celestial Emporium of Benevolent Knowledge which includes such self-referential animals as “those included in this classification” and “et cetera.”
On a serious note, it is impressive how much these live vTubers can bring in. By some estimates Coco made USD $140,000 in July. The mix of anime characters and live streaming of game playing (see above) and other fun seems to be popular. While this phenomena may look like one of those weird Japan things, I suspect we are going to see more virtual characters especially if face and body tracking tools become easy to use. How could I teach online as a virtual character?
Un manuale ampio ed esauriente che illustra tra teoria e prassi il tema dell’informatica umanistica per l’insegnamento e l’apprendimento universitario.
The publisher (Vita e Pensiero) kindly sent me a copy of Guido Milanese’s Filologia, letteratura, computer (Philology, Literature, Computer), an introduction to thinking about and thinking through the computer and texts. The book is designed to work as a text book that introduces students to the ideas and to key technologies, and then provides short guides to further ideas and readings.
The book focuses, as the title suggests, almost exclusively on digital filology or the computational study of texts. At the end Milanese has a short section on other media, but he is has chosen, rightly I think, to focus on set of technologies in depth rather than try a broad overview. In this he draws on an Italian tradition that goes back to Father Busa, but more importantly includes Tito Orlandi (who wrote the preface) and Numerico, Fiormonte, and Tomasi’s L’umanista digitale (this has been translated into English- see The digital humanist).
Milanese starts with the principle from Giambattista Vico that knowledge is made (verum ipsum factum.) Milanese believes that “reflection on the foundations identifies instruments and operations, and working with instruments and methods leads redefining the reflection on foundations.” (p. 9 – my rather free translation) This is virtuous circle in the digital humanities of theorizing and praxis where either one alone would be barren. Thus the book is not simply a list of tools and techniques one should know, but a series of reflections on humanistic knowledge and how that can be implemented in tools/techniques which in turn may challenge our ideas. This is what Stéfan Sinclair and I have been calling “thinking-through” where thinking through technology is both a way of learning about the thinking and about the technology.
An interesting example of this move from theory to praxis is in chapter 7 on “The Markup of Text.” (“La codifica del testo”) He moves from a discussion of adding metadata to the datafied raw text to Minsky’s idea of frames of knowledge as a way of understanding XML. I had never thought of Minsky’s ideas about articial intelligence contributing to the thinking behind XML, and perhaps Milanese is the first to do so, but it sort of works. The idea, as I understand it, goes something like this – human knowing, which Minsky wants to model for AI, brings frames of knowledge to any situation. If you enter a room that looks like a kitchen you have a frame of knowledge about how kitchens work that lets you infer things like “there must be a fridge somewhere which will have a snack for me.” Frames are Minsky’s way of trying to overcome the poverty of AI models based on collections of logical statements. It is a way of thinking about and actually representing the contextual or common sense knowledge that we bring to any situation such that we know a lot more than what is strictly in sight.
Frame systems are made up of frames and connections to other frames. The room frame connects hierarchically to the kitchen-as-a-type-of-room frame which connects to the fridge frame which then connects to the snack frame. The idea then is to find a way to represent frames of knowledge and their connections such that they can be used by AI systems. This is where Milanese slides over to XML as a hierarchical way of adding metadata to a text that enriches it with a frame of knowledge. I assume the frame (or Platonic form?) would be the DTD or Schema which then lets you do some limited forms of reasoning about an instance of an encoded text. The markup explicitly tells the computer something about the parts of the text like this (<author>Guido Milanese</author>) is the author.
The interesting thing is to refect on this application of Minsky’s theory. To begin, I wonder if it is historically true that the designers of XML (or its parent SGML) were thinking of Minsky’s frames. I doubt it, as SGML is descended from GML that predates Minsky’s 1974 Memo on “A Framework for Representing Knowledge.” That said, what I think Milanese is doing is using Minsky’s frames as a way of explaining what we do when modelling a phenomena like a text (and our knowledge of it.) Modelling is making explicit a particular frame of knowledge about a text. I know that certain blocks are paragraphs so I tag them as such. I also model in the sense of create a paradigmatic version of what my perspective on the text is. This would be the DTD or Schema which defines the parts and their potential relationships. Validating a marked up text would be a way of testing the instance against the model.
This nicely connects back to Vico’s knowing is making. We make digital knowledge not by objectively representing the world in digital form, but by creating frames or models for what can be digital known and then apply those frames to instances. It is a bit like object-oriented programming. You create classes that frame what can be represented about a type of object.
There is an attractive correspondence between the idea of knowledge as a hierarchy of frames and an XML representation of a text as a hierarchy of elements. There is a limit, however, to the move. Minsky was developing a theory of knowing such that knowledge could be artificially represented on a computer that could then do knowing (in the sense of complete AI tasks like image recognition.) Markup and marking up strike me as more limited activities of structuring. A paragraph tag doesn’t actually convey to the computer all that we know about paragraphs. It is just a label in a hierarchy of labels to which styles and processes can be attached. Perhaps the human modeller is thinking about texts in all their complexity, but they have to learn not to confuse what they know with what they can model for the computer. Perhaps a human reader of the XML can bring the frames of knowledge to reconstitute some of what the tagger meant, but the computer can’t.
Another way of thinking about this would be Searle’s Chinese room paradox. The XML is the bits of paper handed under the door in Chinese for the interpreter in the room. An appropriate use of XML will provoke the right operations to get something out (like a legible text on the screen) but won’t mean anything. Tagging a string with <paragraph> doesn’t make it a real paragraph in the fullness of what is known of paragraphs. It makes it a string of characters with associated metadata that may or may not be used by the computer.
Perhaps these limitations of computing is exactly what Milanese wants us to think about in modelling. Frames in the sense of picture frames are a device for limiting the view. For Minsky you can have many frames with which to make sense of any phenomena – each one is a different perspective that bears knowledge, sometimes contradictory. When modelling a text for the computer you have to decide what you want to represent and how to do it so that users can see the text through your frame. You aren’t helping the computer understand the text so much as representing your interpretation for other humans to use and, if they read the XML, re-interpret. This is making a knowing.
Milanese, G. (2020). Filologia, Letteratura, Computer: Idee e strumenti per l’informatica umanistica. Milan, Vita e Pensiero.
Minsky, M. (1974, June). A Framework for Representing Knowledge. MIT-AI Laboratory Memo 306. MIT.
Searle, J. R. (1980). “Minds, Brains and Programs.” Behavioral and Brain Sciences. 3:3. 417-457.
International Day for Universal Access to Information focused on the right to information in times of crisis and on the advantages of having constitutional, statutory and/or policy guarantees for public access to information to save lives, build trust and help the formulation of sustainable policies through and beyond the COVID-19 crisis. Speakers talked about how vital access to accurate information is in these pandemic times and the role artificial intelligence could play as we prepare for future crises. Tied to this was a discussion of the important role for international policy initiatives and shared regulation in ensuring that smaller countries, especially in the Global South, benefit from developments in AI. The worry is that some countries won’t have the digital literacy or cadre of experts to critically guide the introduction of AI.
I want situate the kinds of programming typically practiced in digital humanities research and teaching in relation to practices more familiar to book historians and bibliographers, such as the work of compositors and printers working with moveable type.
Ryan Cordell sent me a link to a talk on Programmable Type: the Craft of Printing, the Craft of Code. The talk looks at the “modes of thought and labor” of composing movable type and programming. He is careful to warns us about the simplistic story that has movable type and the computer as two information technologies that caused revolutions in how we think about knowledge. What is particularly interesting is how he weaves hands-on work into his course Technologies of Text. He asks students to not just read about printing, but to try doing it. Likewise for programming in R. There is a knowing that comes from doing something and attending to the labor of that doing. Replicating the making of texts gives students (and researchers) a sense of the materiality and contexts of media. It is a way of doing media archaeology.
In the essay, Cordell writes about the example of the visual poem “A Dude” and its many iterations composed with different type. I had blogged about “A Dude”, but hadn’t thought about how the poem would have been a way for the compositor to show of their craft much like a twitterbot might be a way for a programmer to show off theirs.
Cordell frames this discussion by considering the controversy around whether digital humanists should need to be able to code. He raises an interesting challenge – whether learning the craft of programming (or letterpress printing) might make it harder to view the craft critically. In committing time and labour to learning a craft does one get implicated or corrupted by the craft? Doesn’t one want end up valuing the craft simply because it is something one can now do, and to critique it would be to critique oneself.
“A dude”, 1886. Published in the poetry section of the January issue of The Undergraduate, Middlebury’s newspaper.
From Pinterest I came across this great tumblr called Text Mode gathers “A collection of text graphics and related works, stretching back thousands of years.” Note the image above of a visual poem about “A Dude” from 1886. Included are all sorts of examples from typewriter art to animations to historical emoticons.
While earlier computational approaches focused on narrow and inflexible grammar and syntax, these new Transformer models offer us novel insights into the way language and literature work.
The Journal of Cultural Analytics has a nice article that asks Can GPT-3 Pass a Writer’s Turing Test? They didn’t actually get access to GPT-3, but did test GPT-2 extensively in different projects and they assessed the output of GPT-3 reproduced in an essay on Philosophers On GPT-3. At the end they marked and commented on a number of the published short essays GPT-3 produced in response to the philosophers. They reflect on how would decide if GPT-3 were as good as an undergraduate writer.
What they never mention is Richard Powers’ novel Galatea 2.2 (Harper Perennial, 1996). In the novel an AI scientist and the narrator set out to see if they can create an AI that could pass a Masters English Literature exam. The novel is very smart and has a tragic ending.
Like other ridesharing companies, it made a big bet on an automated future that has failed to materialise, says Aaron Benanav, a researcher at Humboldt University
Aaron Benanav has an important opinion piece in The Guardian about Why Uber’s business model is doomed. Benanav argues that Uber and Lyft’s business model is to capture market share and then ditch the drivers they have employed for self-driving cars as they become reliable. In other words they are first disrupting the human taxi services so as to capitalize on driverless technology when it comes. Their current business is losing money as they feast on venture capital to get market share and if they can’t make the switch to driverless it is likely they go bankrupt.
This raises the question of whether we will see driverless technology good enough to oust the human drivers? I suspect that we will see it for certain geo-fenced zones where Uber and Lyft can pressure local governments to discipline the streets so as to be safe for driverless. In countries with chaotic and hard to accurately map streets (think medieval Italian towns) it may never work well enough.
All of this raises the deeper ethical issue of how driverless vehicles in particular and AI in general are being imagined and implemented. While there may be nothing unethical about driverless cars per se, there IS something unethical about a company deliberately bypassing government regulations, sucking up capital, driving out the small human taxi businesses, all in order to monopolize a market that they can then profit on by firing the drivers that got them there for driverless cars. Why is this the way AI is being commercialized rather than trying to create better public transit systems or better systems for helping people with disabilities? Who do we hold responsible for the decisions or lack of decisions that sees driverless AI technology implemented in a particularly brutal and illegal fashion. (See Benanav on the illegality of what Uber and Lyft are doing by forcing drivers to be self-employed contractors despite rulings to the contrary.)
It is this deeper set of issues around the imagination, implementation, and commercialization of AI that needs to be addressed. I imagine most developers won’t intentionally create unethical AIs, but many will create cool technologies that are commercialized by someone else in brutal and disruptive ways. Those commercializing and their financial backers (which are often all of us and our pension plans) will also feel no moral responsibility because we are just benefiting from (mostly) legal innovative businesses. Corporate social responsibility is a myth. At most corporate ethics is conceived of as a mix of public relations and legal constraints. Everything else is just fair game and the inevitable disruptions in the marketplace. Those who suffer are losers.
This then raises the issue of the ethics of anticipation. What is missing is imagination, anticipation and planning. If the corporate sector is rewarded for finding ways to use new technologies to game the system, then who is rewarded for planning for the disruption and, at a minimum, lessening the impact on the rest of us? Governments have planning units like city planning units, but in every city I’ve lived in these units are bypassed by real money from developers unless there is that rare thing – a citizen’s revolt. Look at our cities and their spread – despite all sorts of research and a history of spread, there is still very little discipline or planning to constrain the developers. In an age when government is seen as essentially untrustworthy planning departments start from a deficit of trust. Companies, entrepreneurs, innovation and yes, even disruption, are blessed with innocence as if, like children, they just do their thing and can’t be expected to anticipate the consequences or have to pick up after their play. We therefore wait for some disaster to remind everyone of the importance of planning and systems of resilience.
Now … how can teach this form of deeper ethics without sliding into political thought?
Edgenuity involves short answers graded by an algorithm, and students have already cracked it
The Verge has a story on how students are figuring out how to game automatic marking systems like Edgenuity. The story is titled, These students figured out their tests were graded by AI — and the easy way to cheat. The story describes a keyword salad approach where you just enter a list of words that the grader may be looking for. The grader doesn’t know whether what your wrote is legible or nonsense, it just looks for the right words. The students in turn get good as skimming the study materials for the keywords needed (or find lists shared by other students online.)
Perhaps we could build a tool called Edgenorance which you could feed the study materials to and it would generate the keyword list automatically. It could watch the lectures for you, do the speech recognition, then extract the relevant keywords based on the text of the question.
None of this should be surprising. Companies have been promoting algorithms that were probably word based for a while. The algorithm works if it is not understood and thus not gamed. Perhaps we will get AIs that can genuinely understand a short paragraph answer and assess it, but that will be close to an artificial general intelligence and such an AGI will change everything.
AI Dungeon, an infinitely generated text adventure powered by deep learning.
Robert told me about AI Dungeon, a text adventure system that uses GPT-2, a language model from OpenAI that got a lot of attention when it was “released” in 2019. OpenAI felt it was too good to release openly as it could be misused. Instead they released a toy version. Now they have GPT-3, about which I wrote before.
AI Dungeon allows you to choose the type of world you want to play in (fantasy, zombies …). It then generates an infinite game by basically generating responses to your input. I assume there is some memory as it repeats my name and the basic setting.