Jingwei, a bright digital humanities student working as a research assistant, has been playing with generative AI approaches from aiweirdness.com – Letting neural networks be weird. Janelle Shane has made neural networks funny by using the to generate things like New My Little Ponies. Jingwei scraped titles of digital humanities conferences from various conference sites and trained and generated new titles just waiting to be proposed as papers:
The Catalogue of the Cultural Heritage Parts
Automatic European Pathworks and Indexte Corpus and Mullisian Descriptions
Minimal Intellectual tools and Actorical Normiels: The Case study of the Digital Humanities Classics
Automatic European Periodical Mexico: The Case of the Digital Hour
TEIviv Industics – Representation dans le perfect textbook
Conceptions of the Digital Homer Centre
Preserving Critical Computational App thinking in DH Languages
DH Potential Works: US Work Film Translation Science
Translation Text Mining and GiS 2.0
DH Facilitating the RIATI of the Digital Scholar
Shape Comparing Data Creating and Scholarly Edition
DH Federation of the Digital Humanities: The Network in the Halleni building and Web Study of Digital Humanities in the Hid-Cloudy
The First Web Study of Build: A “Digitie-Game as the Moreliency of the Digital Humanities: The Case study of the Digital Hour: The Scale Text Story Minimalism: the Case of Public Australian Recognition Translation and Puradopase
The Computational Text of Contemporary Corpora
The Social Network of Linguosation in Data Washingtone
Designing formation of Data visualization
The Computational Text of Context: The Case of the World War and Athngr across Theory
The Film Translation Text Center: The Context of the Cultural Hermental Peripherents
The Social InfrastructurePPA: Artificial Data In a Digital Harl to Mexquise (1950-1936)
EMO Artificial Contributions of the Hauth Past Works of Warla Management Infriction
DAARRhK Platform for Data
Automatic Digital Harlocator and Scholar
Complex Networks of Computational Corpus
IMPArative Mining Trail with DH Portal
Pursour Auchese of the Social Flowchart of European Nation
Anatomy of an AI System – The Amazon Echo as an anatomical map of human labor, data and planetary resources. By Kate Crawford and Vladan Joler (2018)
Kate Crawford and Vladan Joler have created a powerful infographic and web site, Anatomy of an AI System. The dark illustration and site are an essay that starts with the Amazon Echo and then sketches out the global anatomy of this apparently simple AI appliance. They do this by looking at where the materials come from, where the labour comes from (and goes), and the underlying infrastructure.
Put simply: each small moment of convenience – be it answering a question, turning on a light, or playing a song – requires a vast planetary network, fueled by the extraction of non-renewable materials, labor, and data.
The essay/visualization is a powerful example of how we can learn by critically examining the technologies around us.
Just as the Greek chimera was a mythological animal that was part lion, goat, snake and monster, the Echo user is simultaneously a consumer, a resource, a worker, and a product.
For Facebook, Google, and Twitter the fight against fake news seems to be two-pronged: De-incentivize the targeted content and provide avenues to correct factual inaccuracies. These are both surface fixes, however, akin to putting caulk on the Grand Canyon.
And, despite grand hand waves, both approaches are reactive. They don’t aim at understanding how this problem became prevalent, or creating a method that attacks the systemic issue. Instead these advertising giants implement new mechanisms by which people can report one-off issues—and by which the platforms will be left playing cat-and-mouse games against fake news—all the while giving no real clear glimpse into their opaque ad platforms.
The problem is that these companies make too much money from ads and elections are a chance to get lots of ads, manipulative or not. For that matter, what political ad doesn’t try to manipulate viewers?
The slashdot story was actually about Mozilla’s Responsible Computer Science Challenge which will support initiatives to embedd ethics in computer science courses. Alas, the efficacy of ethics courses is questionable. Aristotle would say that if you don’t have the disposition to be ethical no amount of training would do any good. It just helps the unethical pretend to be ethical.
Waymo, the Google spin-off, is bringing autonomous taxis to Phoenix this fall. Other companies are developing shuttles and other types of pods that work, Self-driving pods are slow, boring, and weird-looking — and that’s a good thing. It seems to me that there hasn’t really been a discussion about what would benefit society. Companies will invest in where they see economic opportunity; but what should we as a society do with such technology? At the moment the technology seems to be used either in luxury cars to provide assistance to the driver or imagined to replace taxi and Uber drivers. What will happen to these drivers?
The death of a woman hit by a self-driving car highlights an unfolding technological crisis, as code piled on code creates ‘a universe no one fully understands’
The Guardian has a good essay by Andrew Smith about Franken-algorithms: the deadly consequences of unpredictable code. The essay starts with the obvious problems of biased algorithms like those documented by Cathy O’Neil in Weapons of Math Destruction. It then goes further to talk about cases where algorithms are learning on the fly or are so complex that their behaviour becomes unpredictable. An example is high-frequency trading algorithms that trade on the stock market. These algorithmic traders try to outwit each other and learn which leads to unpredictable “flash crashes” when they go rogue.
The problem, he (George Dyson) tells me, is that we’re building systems that are beyond our intellectual means to control. We believe that if a system is deterministic (acting according to fixed rules, this being the definition of an algorithm) it is predictable – and that what is predictable can be controlled. Both assumptions turn out to be wrong.
The good news is that, according to one of the experts consulted this could lead to “a golden age for philosophy” as we try to sort out the ethics of these autonomous systems.
Google CEO Sundar Pichai milked the woos from a clappy, home-turf developer crowd at its I/O conference in Mountain View this week with a demo of an in-the-works voice assistant feature that will e…
A number of venues, including TechCruch have discussed the recent Google demonstration of an intelligent agent Duplex who can make appointments. Many of the stories note how Duplex shows Google failing at ethical and creative AI design. The problem is that the agent didn’t (at least during the demo) identify as a robot. Instead it appeared to deceive the person it was talking to. As the TechCrunch article points out, there is really no good reason to deceive if the purpose is to make an appointment.
What I want to know is what are the ethics of dealing with a robot? Do we need to identify as human to the robot? Do we need to be polite and give them the courtesy that we would a fellow human? Would it be OK for me to hang up as I do on recorded telemarketing calls? Most of us have developed habits of courtesy when dealing with people, including strangers, that the telemarketers take advantage of in their scripts. Will the robots now take advantage of that? Or, to be more precise, will those that use the robots to save their time take advantage of us?
A second question is how Google considers the ethical implications of their research? It is easy to castigate them for this demonstration, but the demonstration tells us nothing about a line of research that has been going on for a while and what processes Google may have in place to check the ethics of what they do. As companies explore the possibilities for AI, how are they to check their ethics in the excitement of achievement?
We are also deeply concerned about the possible integration of Google’s data on people’s everyday lives with military surveillance data, and its combined application to targeted killing. Google has moved into military work without subjecting itself to public debate or deliberation, either domestically or internationally. While Google regularly decides the future of technology without democratic public engagement, its entry into military technologies casts the problems of private control of information infrastructure into high relief.
Google has announced some cool text projects. See Google AI experiment has you talking to books. One of them, Talk to Books, lets you ask questions or type statements and get answers that are passages from books. This strikes me as a useful research tool as it allows you to see some (book) references that might be useful for defining an issue. The project is somewhat similar to the Veliza tool that we built into Voyant. Veliza is given a particular text and then uses an Eliza-like algorithm to answer you with passages from the text. Needless to say, Talking to Books is far more sophisticated and is not based simply on word searches. Veliza, on the other hand can be reprogrammed and you can specify the text to converse with.