From Teaching in a Digital Age by A. W. Bates I came across this 1954 video of Skinner explaining his Teaching Machine inspired by behaviourism. The machine runs a paper script, but it isn’t that different from the computer based drill training today. You get a question, you write your answer, and you get feedback.
Wired Magazine has a nice essay on Hey, Computer Scientists! Stop Hating on the Humanities. The essay by a computer scientist argues that CS students need to study the ethical and social implications of what they build. It can’t be left to others because then it will be too late. Further, CS students should be scared a little:
Professors need to scare their students, to make them feel they’ve been given the skills not just to get rich but to wreck lives; they need to humble them, to make them realize that however good they might be at math, there’s still so much they don’t know.
This year I kept notes about the Digital Humanities 2017 conference at McGill. See DH 2017 Conference Report. My conference report also covers the New Scholars Symposium that took place before.
The NSS is supported by CHCI and centerNet. KIAS provided administrative support and the ACH provided coffee and snacks on the day of. We were lucky to have so many groups supporting the NSS which in turn supports new scholars to come to the conference and to articulate their issues in an unconference format.
DH 2017 itself was a rich feast of ideas. There was too much going on to summarize in a paragraph, but here are two highlights:
We had an opening keynote in French from Marin Dacos. He talked about the “Unexpected Reader” that one gets when publications are open.
We had a great closing keynote by Elizabeth Guffey on “The Upside-Down Politics of Access in the Digital Age” that asked about access for disabled people in the digital realm.
The participants of the New Scholars Symposium identified the following as topics to watch and think about:
Alice and Bob is a web site and paper by Quinn DuPont and Alana Cattapan that nicely tells the history of the famous virtual couple used to explain cryptology.
While Alice, Bob, and their extended family were originally used to explain how public key cryptography works, they have since become widely used across other science and engineering domains. Their influence continues to grow outside of academia as well: Alice and Bob are now a part of geek lore, and subject to narratives and visual depictions that combine pedagogy with in-jokes, often reflecting of the sexist and heteronormative environments in which they were born and continue to be used. More than just the world’s most famous cryptographic couple, Alice and Bob have become an archetype of digital exchange, and a lens through which to view broader digital culture.
The web site provides a timeline going back to 1978. The history is then explained more fully in the full paper (PDF). They end by talking about the gendered history of cryptography. They mention other examples where images of women serve as standard test images like the image of Lena from Playboy.
The design of the site nicely shows how a paper can be remediated as an interactive web site. It isn’t that fancy, but you can navigate the timeline and follow links to get a sense of this “couple”.
Finally, this is a cautionary tale. The collection and storage of metadata from any individual in our society should be of concern to all of us. While it is possible to discern patterns from several sources, it is also far too easy to construct a false narrative, particularly one that ﬁts an already held point of view. As analysts, we fall prey to our cognitive biases. Interactive ﬁlter and display of metadata from a large corpus of communications add another tool to an already powerful analytic arsenal. As with any other powerful tool it needs to be used with caution.
Their cautionary tale touches on the value of metadata. After the Snowden revelations government officials like Dianne Feinstein have tried to reassure us that mining metadata shouldn’t be a concern because it isn’t content. Research like this shows what can be inferred from metadata.
I’ve been playing with DataCamp‘s Python lessons and they are quite good. Python is taught in the context of data analysis rather than the turtle drawing of How to Think Like a Computer Scientist. They have a nice mix of video tutorials and then exercises where you get a tripartite screen (see above.) You have an explanation and instructions on the left, a short script to fill in on the upper-right and interactive python shell where you can try stuff below.
Each of our lectures will explore one specific facet of bullshit. For each week, a set of required readings are assigned. For some weeks, supplementary readings are also provided for those who wish to delve deeper.
On Twitter I came across this terrific syllabus: Calling Bullshit: Syllabus.The syllabus is learned, full of useful links, clear and funny. I wish I could write a syllabus like this. For example, here are some of the learning objectives:
Recognize said bullshit whenever and wherever you encounter it.
Figure out for yourself precisely why a particular bit of bullshit is bullshit.
What could be more important an objective in the humanities?
AI is not going to take over the world the way the sci-fi stories have it.
The effect will be on tasks as AI takes over tasks that people are paid to do, putting them out of work.
How then will we deal with the unemployed? (This is a question people asked in the 1960s when the first wave computerization threatened massive unemployment.)
One solution is “Keynesian policies of increased government spending” paid for taxing the companies made wealthy by AI. This spending would pay for “service jobs of love” where people act as the “human interface” to all sorts of services.
Those in the jobs that can’t be automated and that make lots of money might also scale back on their time at work so as to provide more jobs of this sort.