Tags:  visualization ,  altair ,  bokeh

Last week’s speaker in the Penny Stamps Speaker Series was Refik Anadol, one of the most successful artists working primarily with AI, data, and visualization. Over a decade or so, Anadol has produced work in collaboration with organizations and venues like the LA Philharmonic, Gaudi’s Casa Batlló, the Exosphere in Las Vegas, and the Museum of Modern Art in New York.

The lecture he gave was basically a review of his body of work, and that of his art and design studio, which is comprised of about 20 people. The earlier projects he showed mostly took the form of massive scale light projections on the sides of buildings, which could give the impression of a totally different texture or form. More recent work tended to use data driven visualizations, abstracting some sort of data into visual patterns displayed on a wall, building, or screen. Many of these also made use of what appeared to be procedural animations to create the impression of three dimensionality and motion.

Tags:  machine-learning ,  artificial-intelligence ,  archives ,  visualization ,  memory ,  art ,  refik-anadol ,  zach-leiberman

There is a duality at the heart of large language models. On the one hand, they are essentially a backwards-looking invention, a “cultural technology”, in the words of Alison Gopnik—algorithms which index and remix a large slice of human culture (though one that is typically heavily biased towards the recent past). On the other hand, they can often seem to be producing something entirely new, and can thereby leave many people with the impression of having a personality or even “sentience” (whatever that means exactly); in the most extreme cases, some people have apparently convinced themselves that such models are a step on the path towards some sort of successor species to humanity, a new regime of algorithmic children that will survive our own human catastrophes. Complicating matters here is the fact that the emergence of and widespread attention to these systems largely overlapped with the Covid-19 pandemic, a time in which we have all had additional reason to reflect on life, death, loss, and creation.

Tags:  machine-learning ,  large-language-models ,  archives ,  books ,  fiction ,  memory ,  mortality ,  digital-preservation

Tags:  large-language-models ,  chatgpt ,  reproducibility ,  evaluation ,  open-science

Tags:  visualization ,  altair ,  bokeh

It was recently reported by inews.co.uk that Twitter is going to start charging academic researchers and institution $42,000/month if they want to maintain their current level of expansive access to data, and – more significantly – require that they delete all Twitter data from their archives if they do not. I’d heard rumors that this might be happening a few weeks ago, but the iNews article is the first independent reporting that I’ve seen about it.

Tags:  twitter ,  archives ,  sociotechnical-systems ,  reproducibility ,  change ,  instability

ChatGPT has understandably garnered a huge amount of attention from all corners of academia, from philosophy to economics. One of the more quixotic examples I’ve encountered recently is Robert W. McGee, and his many papers on this topic, such as “Is Chat Gpt Biased Against Conservatives? An Empirical Study”.

A professor of accounting at Fayetteville University, McGee’s biography reads as something like a Marvel Cinematic Universe version of a nerdy academic supervillain. In addition to having published 59 non-fiction books, McGee apparently holds 23 academic degrees, including 13 doctorates, as well as being a world champion in various martial arts, such as Taekwondo and Tai Chi.

Tags:  large-language-models ,  chatgpt ,  sociotechnical-systems ,  measurement ,  evaluation

Last week, a number of US outlets reported on data leaks at Samsung, in which three employees (in separate incidents) apparently entered confidential company information into ChatGPT. According to reports, in one case, an employee tried using ChatGPT to help debug code, another to optimize code, and a third to have it produce a summary of meeting notes.

Tags:  machine-learning ,  large-language-models ,  chatgpt ,  data ,  privacy ,  security

I’ve written about this before, but it’s worth remembering that almost nothing in sociotechnical systems is guaranteed to remain stable for very long. We’ve recently had two great examples of this, with the first being the changes to Twitter, and the second being ChatGPT (and, by extension, the new Bing).

In the first case, a platform which had long seemed relatively static, (especially compared to all the rest), rather suddenly changed hands, which led to major changes in what it delivered. On some level, many of the technical changes to the actual functionality of the site were relatively minor. Much bigger, however, was the impact of many people abandoning the platform for alternatives like Mastodon. Although it seems to me like people have gradually been filtering back to Twitter, most people seem to have the sense that the experience has changed. Obviously more dramatic infrastructural changes, like prioritizing tweets from paying users, could produce even more dramatic shifts. Regardless, it’s a good reminder that what we think of as “Twitter” is the product of a combination of people and software, either or both of which can shift dramatically in a short period of time.

Tags:  large-language-models ,  twitter ,  sociotechnical-systems ,  chatgpt ,  instability ,  archives

I expect that almost anyone reading this will have heard of ChatGPT by now. Released about a month ago, ChatGPT is a system developed by OpenAI which provides text responses to text input. Although details are scarce, under the hood ChatGPT is basically a large language model, trained with some additional tricks (see Yoav Goldberg’s write up for a good summary). In other words, it is a model which maps from the text input (treated as a sequence of tokens), to a distribution over possible next tokens, and generates text by making repeated calls to this function, and sampling tokens from the predicted distributions.

Tags:  machine-learning ,  large-language-models ,  chatgpt ,  open-science ,  evaluation