iiinova — where your digital self keeps evolving

Sireesh Gururaja @siree.sh · 23/07/2026

40 minute headways in Pittsburgh are fine, apparently?

Google maps directions where the transfer instruction is "Walk, then wait for up to 1455 minutes"

Sireesh Gururaja @siree.sh · 05/07/2026

Great thread from a book that has lived rent-free in my head since it argued that computers are inherently and fundamentally conservative. I didn't even know it was out of print!!

"Yes, the computer did arrive "just in time." But in time for what? In tie to save—and save very nearly intact, indeed, to entrench and stabilize—social and political structures that otherwise might have been either radically renovated or allowed to totter under the demands that were sure to be made on them. The computer, them, was used to conserve America's social and political institutions. It buttressed them and immunized them, at least temporarily, against enormous pressures for change."

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Sireesh Gururaja @siree.sh · 05/07/2026

You may already have seen this, but also worth reading JR Pierce's 1969 piece "Whither Speech Recognition". The writing is similarly...pointed, and there's also some interesting foresight about the shape of what language models _did_ end up doing for speech recognition.

Quote from Pierce's "Whither Speech Recognition". It reads: "It would be too simple to say that work in speech recognition
is carried out simply because one can get money for it. That is a
necessary but not a sufficient condition. We are safe in asserting
that speech recognition is attractive to money. The attraction is
perhaps similar to the attraction of schemes for turning water
into gasoline, extracting gold from the sea, curing cancer, or going
to the moon. One doesn't attract thoughtlessly given dollars by
means of schemes for cutting the cost of soap by 10%. To sell
suckers, one uses deceit and offers glamor."

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Sireesh Gururaja @siree.sh · 02/06/2026

One of his first alligators, whom he donated to the Pittsburgh zoo almost 20 years ago, is named Otis, and the zoo gave him a $2500 tax writeoff and a lifetime zoo pass (which they have apparently stopped honoring) for him. This is Otis.

An American alligator named Otis at the Pittsburgh zoo

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Sireesh Gururaja @siree.sh · 28/07/2025

Coming soon (6pm!) to the #ACL poster session: how do experts work with collections of documents, and do LLMs do those things? tl;dr: only sometimes! While we have good tools for things like information extraction, the way that experts read documents goes deeper - come to our poster to learn more!

Screenshot of paper title "Beyond Text: Characterizing Domain Expert Needs in Document Research"

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Sireesh Gururaja @siree.sh · 15/05/2025

Yeah, I think I read this (and got burned) the same way :/ D&B also had additional spots that implied our original reading, like the dates page:

Screenshot of the full paper submission date for the datasets and benchmarks track. The date is described as "Datasets and Benchmarks - Full Paper Submission and Co-author Registration"

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Sireesh Gururaja @siree.sh · 02/05/2025

Hey, if it's good enough for the guy that founded the town...

Screenshot from the Wikipedia article "Name of Pittsburgh", describing how John Forbes (a Scotsman) may have pronounced Pittsburgh similar to Edinburgh.

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Sireesh Gururaja @siree.sh · 20/12/2024

Research going at the same pace, but!

Car salesman meme, with salesman advertising how many bolded numbers can fit into a table.

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Sireesh Gururaja @siree.sh · 17/12/2024

When I started on ARL project that funds my PhD, the thing we were supposed to build was a "MaterialsGPT". What is a MaterialsGPT? Where does that idea come from? I got to spend a lot of time thinking about that second question with @davidthewid.bsky.social and Lucy Suchman (!) working on this:

The abstract of a paper titled "Basic Research, Lethal Effects: Military AI Research Funding as Enlistment".

In the context of unprecedented U.S. Department of Defense (DoD) budgets, this paper examines the recent history of DoD funding for academic research in algorithmically based warfighting. We draw from a corpus of DoD grant solicitations from 2007 to 2023, focusing on those addressed to researchers in the field of artificial intelligence (AI). Considering the implications of DoD funding for academic research, the paper proceeds through three analytic sections. In the first, we offer a critical examination of the distinction between basic and applied research, showing how funding calls framed as basic research nonetheless enlist researchers in a war fighting agenda. In the second, we offer a diachronic analysis of the corpus, showing how a 'one small problem' caveat, in which affirmation of progress in military technologies is qualified by acknowledgement of outstanding problems, becomes justification for additional investments in research. We close with an analysis of DoD aspirations based on a subset of Defense Advanced Research Projects Agency (DARPA) grant solicitations for the use of AI in battlefield applications. Taken together, we argue that grant solicitations work as a vehicle for the mutual enlistment of DoD funding agencies and the academic AI research community in setting research agendas. The trope of basic research in this context offers shelter from significant moral questions that military applications of one's research would raise, by obscuring the connections that implicate researchers in U.S. militarism.

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Sireesh Gururaja @siree.sh · 08/12/2024

Fully dislocated my shoulder going down some icy steps, so there will be no winter fishing for me this year :/ now gazing longingly at pictures of the last time I was out

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Sireesh Gururaja @siree.sh · 12/10/2023

These years have also raised existential concerns about the incentives that drive the community, peer review, research under limited compute budgets, and even the place of a *CL community.

Thought clouds depicting questions we've heard about the field: how big a deal is GPT-4, really? Were things always this fast-paced? Why is everything PyTorch/Huggingface?

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Sireesh Gururaja @siree.sh · 12/10/2023

What about LLMs? The last few years have intensified these trends: the community has grown immensely. As models grow better and NLP becomes more public-facing, failures in benchmarking become evident. Centralization on individual models has grown.

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Sireesh Gururaja @siree.sh · 12/10/2023

Neural NLP increased the sharing of toolkits or library code across labs and even across subfields, with libraries like PyTorch and Tensorflow. Pretraining extended this to the sharing of models, too, with Hugging Face being the biggest example.

Line graph showing mentions of software libraries in *CL papers. Libraries show cyclical use, with a "successor" library rising past the previous dominant library shortly after its peak. We see this pattern ith Theano, Tensorflow, Pytorch, and Hugging Face particularly.

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Sireesh Gururaja @siree.sh · 12/10/2023

The rise of statistical NLP in the early 2000s was another such cycle. But major methodological shifts come with major cultural shifts as well. Statistical NLP introduced a culture laser-focused on benchmarks and saw the end of a small, “high trust” research community.

A chart showing the number of unique researchers publishing in *CL venues. The number has increased from 715 in 1980, to 17,829 in 2022.

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Sireesh Gururaja @siree.sh · 12/10/2023

Participants describe cycles of research: a breakthrough, a flurry of work exploiting the new method, then a slower wave of work exploring extensions or limitations. This pattern is not new– for instance, we heard about this with SVMs, RNNs, and BERT!

An image depicting typical attitudes during the two phases of research.

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Sireesh Gururaja @siree.sh · 12/10/2023

These years have also raised existential concerns about the incentives that drive the community, peer review, research under limited compute budgets, and even the place of a *CL community.

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Sireesh Gururaja @siree.sh · 12/10/2023

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Sireesh Gururaja @siree.sh · 12/10/2023

We conducted long-form interviews with established NLP researchers, which reveal larger trends and forces that have been shaping the NLP research community since the 1980s.

A timeline of developments in natural language processing, below a chart showing citations of popular papers and mentions of common methods.

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Sireesh Gururaja @siree.sh · 12/10/2023

We all know that “recently large language models have”, “large language models are”, and “large language models can.” But *why* LLMs? How did we get here? (where is “here”?) What forces are shaping NLP, and how recent are they, actually? To appear at EMNLP 2023: arxiv.org/abs/2310.07715

Screenshot of paper title: "To Build Our Future, We Must Know Our Past: Contextualizing Paradigm Shifts in Natural Language Processing"

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