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.
On Twitter someone posted a link to a 1944 OSS Simple Sabotage Field Manual. This includes simple, but brilliant advice on how to sabotage organizations or conferences.
This sounds a lot like what we all do when we academics normally do as a matter of principle. I particularly like the advice to “Make ‘speeches.'” I imagine many will see themselves in their less cooperative moments in this list of actions or their committee meetings.
The OSS (Office of Strategic Services) was the US office that turned into the CIA.
Documenting the Now develops tools and builds community practices that support the ethical collection, use, and preservation of social media content.
I’ve been talking with the folks at MassMine (I’m on their Advisory Board) about tools that can gather information off the web and I was pointed to the Documenting the Now project that is based at the University of Maryland and the University of Virginia with support from Mellon. DocNow have developed tools and services around documenting the “now” using social media. DocNow itself is an “appraisal” tool for twitter archiving. They then have a great catalog of twitter archives they and others have gathered which looks like it would be great for teaching.
MassMine is at present a command-line tool that can gather different types of social media. They are building a web interface version that will make it easier to use and they are planning to connect it to Voyant so you can analyze results in Voyant. I’m looking forward to something easier to use than Python libraries.
Speaking of which, I found a TAGS (Twitter Archiving Google Sheet) which is a plug-in for Google Sheets that can scrape smaller amounts of Twitter. Another accessible tool is Octoparse that is designed to scrape different database driven web sites. It is commercial, but has a 14 day trial.
One of the impressive features of Documenting the Now project is that they are thinking about the ethics of scraping. They have a Social Labels set for people to indicate how data should be handled.
Human data encodes human biases by default. Being aware of this is a good start, and the conversation around how to handle it is ongoing. At Google, we are actively researching unintended bias analysis and mitigation strategies because we are committed to making products that work well for everyone. In this post, we’ll examine a few text embedding models, suggest some tools for evaluating certain forms of bias, and discuss how these issues matter when building applications.
On the Google Developvers Blog there is an interesting post on Text Embedding Models Contain Bias. Here’s Why That Matters. The post talks about a technique for using Word Embedding Association Tests (WEAT) to see compare different text embedding algorithms. The idea is to see whether groups of words like gendered words associate with positive or negative words. In the image above you can see the sentiment bias for female and male names for different techniques.
While Google is working on WEAT to try to detect and deal with bias, in our case this technique could be used to identify forms of bias in corpora.
I knew the end of Agile was coming when we started using hockey sticks.
From Slashdot I found my way to a good essay on The End of Agile by Kurt Cagle in Forbes.
The Agile Manifesto, like most such screeds, started out as a really good idea. The core principle was simple – you didn’t really need large groups of people working on software projects to get them done. If anything, beyond a certain point extra people just added to the communication impedance and slowed a project down. Many open source projects that did really cool things were done by small development teams of between a couple and twelve people, with the ideal size being about seven.
Cagle points out that certain types of enterprise projects don’t lend themselves to agile development. In a follow up article he provides links to rebuttals and supporting articles including one on Agile and Toxic Masculinity (it turns out there are a lot of sporting/speed talk in agile.) He proposes the Studio model as an alternative and this model is based on how creative works like movies and games get made. There is an emphasis on creative direction and vision.
I wonder how this critique of agile could be adapted to critique agile-inspired management techniques?
What can we learn from the discourse around text tools? More than might be expected. The development of text analysis tools has been a feature of computing in the humanities since IBM supported Father Busa’s production of the Index Thomisticus (Tasman 1957). Despite the importance of tools in the digital humanities (DH), few have looked at the discourse around tool development to understand how the research agenda changed over the years. Recognizing the need for such an investigation a corpus of articles from the entire run of Computers and the Humanities (CHum) was analyzed using both distant and close reading techniques. By analyzing this corpus using traditional category assignments alongside topic modelling and statistical analysis we are able to gain insight into how the digital humanities shaped itself and grew as a discipline in what can be considered its “middle years,” from when the field professionalized (through the development of journals like CHum) to when it changed its name to “digital humanities.” The initial results (Simpson et al. 2013a; Simpson et al. 2013b), are at once informative and surprising, showing evidence of the maturation of the discipline and hinting at moments of change in editorial policy and the rise of the Internet as a new forum for delivering tools and information about them.
This is a story from early in the technological revolution, when the application was out searching for the hardware, from a time before the Internet, a time before the PC, before the chip, before the mainframe. From a time even before programming itself. (Winter 1999, 3)
Father Busa is rightly honoured as one of the first humanists to use computing for a humanities research task. He is considered the founder of humanities computing for his innovative application of information technology and for the considerable influence of his project and methods, not to mention his generosity to others. He did not only work out how use the information technology of the late 1940s and 1950s, but he pioneered a relationship with IBM around language engineering and with their support generously shared his knowledge widely. Ironically, while we have all heard his name and the origin story of his research into presence in Aquinas, we know relatively little about what actually occupied his time – the planning and implementation of what was for its time one of the major research computing projects, the Index Thomsticus.
Glenn Greenwald (see the embedded video) questions the value of this sort of mass surveillance. He suggests that mass surveillance impedes the ability to find terrorists attacks. The problem is not getting more information, but connecting the dots of what one has. In fact the slides that you can get to from these stories both show that CSE is struggling with too much information and analytical challenges.
Stéfan Sinclair and I just finished a workshop on My Very Own Voyant. The workshop focused on how to run VoyantServer on your local machine. This allows you to run Voyant locally. There are all sorts of reasons to run locally: