Writing with the machine

“…it’s like writing with a deranged but very well-read parrot on your shoulder.”

Robin Sloan, author of Mr. Penumbra’s 24-Hour Bookstore, has been doing some interesting work with recursive neural nets in order to generate text. See Writing with the machine. He trained a machine on science fiction and then hooked it into a text editor so it can complete sentences. The New York Times has a nice story on Sloan’s experiments, Computer Stories: A.I. Is Beginning to Assist Novelists.

One wonders what it would be like if you trained it on your own writing. Would it help you be yourself or discourage you from rereading your prose?

 

Making AI accountable easier said than done, says U of A expert

The Folio has a story on the ethics of AI that quotes me with the title, Making AI accountable easier said than done, says U of A expert.

One of issues that interests me the most now is the history of this discussion. We tend to treat the ethics of AI as a new issue, but people have been thinking about how automation would affect people for some time. There have been textbooks for teaching Computer Ethics like that of Deborah G. Johnson since the 1980s. As part of research we did on how computer were presented in the news we found articles in the 1960s about how automation might put people out of work. They weren’t thinking of AI then, but the ethical and social effects that concerned people back then were similar. What few people discussed, however, was how automation affected different groups differently. Michele Landsberg wrote a prescient article on “Will Computer Replace the Working Girl?” in 1964 for the women’s section of The Globe and Mail that argued that is was women in the typing pools that were being put out of work. Likewise I suspect that some groups be more affected by AI than others and that we need to prepare for that.

Addressing the issue of how universities might prepare for the disruption of artificial intelligence is a good book, Robot-Proof: Higher Education in the Age of Artificial Intelligence by Joseph Aoun (MIT Press, 2017).

Instead of educating college students for jobs that are about to disappear under the rising tide of technology, twenty-first-century universities should liberate them from outdated career models and give them ownership of their own futures. They should equip them with the literacies and skills they need to thrive in this new economy defined by technology, as well as continue providing them with access to the learning they need to face the challenges of life in a diverse, global environment.

Letting neural networks be weird

Halloween Costume Names Generated by a Weird AI

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 Infrastructure  PPA: 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

  • The Stefanopology: The Digital Humanities

Anatomy of an AI System

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.

Big Tech’s Half-Hearted Response To Fake News And Election Hacking

Despite big hand waves, Facebook, Google, and Twitter aren’t doing enough to stop misinformation.

From slashdot I found a story about : Big Tech’s Half-Hearted Response To Fake News And Election Hacking. This Fast Company story talks about ways that social media companies are trying to prevent the misuse of their platforms as we head into the US midterms.

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.

Self-driving pods are slow, boring, and weird-looking — and that’s a good thing

Driverless pods, retirement communities, and grocery delivery

Autonomous vehicles are here! That’s the message from a panel on AI and Transportation I listened to at the International Symposium on Applications of Artificial Intelligence held here at the University of Alberta.

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 thingIt 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?

AI Weirdness

I just came across a neat site called AI Weirdness. The site describes all sorts of “weird” experiments in learning neural networks. Some examples:

The site has a nice FAQ that describes her tools and how to learn how to do it.

Franken-algorithms: the deadly consequences of unpredictable code

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.

Writing with the machine

“…it’s like writing with a deranged but very well-read parrot on your shoulder.”

Robin Sloan of Mr. Penumbra’s 24-hour Bookstore fame, has been talking about Writing with the machine. He was inspired by presentations like Adrej Karpathy’s blog post on The Unreasonable Effectiveness of Recurrent Neural Networks and Bowman et al’s Generating Sentences from a Continuous Space to try developing a neural net that could generate text. He used as a training corpus a collection of early science-fiction from the Internet Archive and created different text generation tools like the short video of that which you can see above and hear explained in this Eyeo video.

One of the points he emphasizes is that he didn’t do this just for the fun of seeing strange phrases generated, but wants to use it seriously as a writing aide.

I can’t help wondering if this could be used philosophically. Could we generate philosophical or ethical phrases in response to questions?

Re-Imagining Education In An Automating World conference at George Brown

On May 25th I had a chance to attend a gem of a conference organized the Philosophy of Education (POE) committee at George Brown. They organized a conference with different modalities from conversations to formal talks to group work. The topic was Re-Imagining Education in An Automating World (see my conference notes here) and this conference is a seed for a larger one next year.

I gave a talk on Digital Citizenship at the end of the day where I tried to convince people that:

  • Data analytics are now a matter of citizenship (we all need to understand how we are being manipulated).
  • We therefore need to teach data literacy in the arts and humanities, so that
  • Students are prepared to contribute to and critique the ways analytics are used deployed.
  • This can be done by integrating data and analytical components in any course using field-appropriate data.