In about 3 weeks universities will be in session again. Many universities (like my own) want to pretend that things will be back to normal. The buildings and classrooms and quads will all be there and look the same. The routines of commuting to classes will be the same… 1/7
But WE will not be the same. We may still be suffering from mental fatigue. We may have developed new life routines and work habits that are suddenly incompatible with on-campus life. 2/7
2nd year students will be expected to act like 2nd year students even though they are navigating the new social norms of being away from home for the first time—something students normally learn in their first years. 3/7
We may be trying to do all this while still experiencing higher-than-normal levels of anxiety about getting sick. 4/7
We have to deal with the fact that our social norms don’t provide scripts for navigating the politics of determining how to react to people who do not share our same attitudes about vaccinations and masks. There will be social and emotional conflict. 5/7
The facade of normalcy is a trap. We will look around and see things looking normal and wonder why we don’t feel normal, if we are broken. We are broken. But also know that there will be a lot of people feeling the same way underneath. 6/7
We university faculty and course instructors will need to remember that we are not the same. Our students are not the same. Problems will arise that we did not seen pre-2019 and when they do our response should not be “be like you were in 2019”. 7/7

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More from @mark_riedl

17 Apr
For some insane reason, my team submitted 7 papers to the NAACL Workshop on Narrative Understanding.

Even more insane: all seven were accepted!
1. Fabula Entropy Indexing: Objective Measures of Story Coherence
@lcastricato @spencerfrazier @JonathanBalloch

A new way to OBJECTIVELY measure the coherence of story generation systems. Grounded in narratology and validated in controlled studies
2. Towards a Formal Model of Narratives
@lcastricato @recardona @DavidThue

A narratological theory that makes narratives mathematical. This is what makes the above evaluation method work. In other words, a practical theory.
Read 9 tweets
15 May 20
I’m finally ready to release my neural net based lyrics parody generation system…

Introducing: Weird A.I. Yankovic!

Runs on Google Colab:…
You can provide the rhyme scheme and syllables per line for an existing song, and it will write new lyrics to match.

In the true spirit of parody, here is a Michael Jackson song (“Beat It”) rewritten to be about food.

Then you can sing the song yourself to the horror of others
With a little bit of extra work (you provide a mp3 or mp4), the system will produce a karaoke video to make it easier to sing along
Read 10 tweets
21 Sep 19
Hollywood: It’s about an experimental AI that—

Me: that fails to converge until the programmer cleans up millions of lines of labeled data? Right! Very suspenseful! Never know if that’s going to work.

Hollywood: you’re fired
Hollywood: the AI has a robot body that—

Me: can’t pick up any objects unless they a placed in a very specific way on a table at just the right height. The struggle is real.

Hollywood: No—
Read 7 tweets
19 Feb 19
This is a problem for the DOD and has been for a long time…

But it is addressable by fixing our higher education system, which is not producing enough AI/ML engineers and skewed toward a small # of elite universities.
Under the belief that there are no secret algorithms, only secret engineering, the DOD mostly needs people that can build hardened AI/ML systems (let’s ignore the question of what these systems are for for the moment).
Right now there is high demand in industry for these skills. The demand is high because when it comes to AI and ML, our education system is skewed toward a small number of elite universities that have the resources to do research and thus train graduate students in these areas.
Read 7 tweets
17 Sep 18
I’m super geeked ➡️ this is video of @MatthewGuz playing a game generated by a ML algorithm trained on video of Super Mario Bros., Kirby, and Mega Man Everything is learned from scratch: level design and mechanics/rules.

What I like about this video is that the game is very different from any of the training examples.

The conceptual expansion algorithm is able to extrapolate beyond the training data, hypothesizing the existence of models that aren’t directly supported by the training data
.@MatthewGuz and I think the ability to extrapolate beyond training data is a very important quality for ML-based computational creativity.

We think conceptual expansion may be potentially important for ML generalization and domain transfer too.
Read 4 tweets

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