Jev introduces a new shape of LLM—System One, aka Decision Models
Last week TypeSafe AI unveiled Jev, their first example of a new category of model that they are calling “System One models” (I’m with Maggie Appleton, I think “decision models” …
What comes next with open models
Markets, capabilities, cope, and bewilderment in the industrialization of language models.
People Loved the Dot-Com Boom. The A.I. Boom, Not So Much.
Tech leaders are beginning to worry about the public’s underwhelming enthusiasm for their plans to remake the world with artificial intelligence. Will that burst the bubble?
AI’s Memorization Crisis
Large language models don’t “learn”—they copy. And that could change everything for the tech industry.
100 years of whatever this will be
What if all these weird tech trends actually add up to something? Last time, we explored why various bits of trendy technology are, in my o...
The Beginning of the End of Big Tech
From politicians to VC firms, everyone is falling out of love with the massive, money-oriented, global technology titans. In their place, we have the chance to build something open and trustworthy.
Large language models are not the problem
If a Large Language Model (LLM) can replicate your scientific contribution, the problem is not the LLM. What does it say about our field that so much of the anxiety about AI comes down to the fear that a machine could do what we do? Perhaps it says we should be doing something better.
Designing AI for Disruptive Science
Why scaling AI won’t automatically lead to paradigm shifts.
Topics, Authors, and Institutions in Large Language Model Research: Trends from 17K arXiv Papers
Large language models (LLMs) are dramatically influencing AI research, spurring discussions on what has changed so far and how to shape the field's future. To clarify such questions, we analyze a new dataset of 16,979 LLM-related arXiv papers, focusing on recent trends in 2023 vs. 2018-2022. First, we study disciplinary shifts: LLM research increasingly considers societal impacts, evidenced by 20x growth in LLM submissions to the Computers and Society sub-arXiv. An influx of new authors -- half of all first authors in 2023 -- are entering from non-NLP fields of CS, driving disciplinary expansion. Second, we study industry and academic publishing trends. Surprisingly, industry accounts for a smaller publication share in 2023, largely due to reduced output from Google and other Big Tech companies; universities in Asia are publishing more. Third, we study institutional collaboration: while industry-academic collaborations are common, they tend to focus on the same topics that industry focuses on rather than bridging differences. The most prolific institutions are all US- or China-based, but there is very little cross-country collaboration. We discuss implications around (1) how to support the influx of new authors, (2) how industry trends may affect academics, and (3) possible effects of (the lack of) collaboration.
Topics, Authors, and Institutions in Large Language Model Research: Trends from 17K arXiv Papers
Large language models (LLMs) are dramatically influencing AI research, spurring discussions on what has changed so far and how to shape the field's future. To clarify such questions, we analyze a new dataset of 16,979 LLM-related arXiv papers, focusing on recent trends in 2023 vs. 2018-2022. First, we study disciplinary shifts: LLM research increasingly considers societal impacts, evidenced by 20x growth in LLM submissions to the Computers and Society sub-arXiv. An influx of new authors -- half of all first authors in 2023 -- are entering from non-NLP fields of CS, driving disciplinary expansion. Second, we study industry and academic publishing trends. Surprisingly, industry accounts for a smaller publication share in 2023, largely due to reduced output from Google and other Big Tech companies; universities in Asia are publishing more. Third, we study institutional collaboration: while industry-academic collaborations are common, they tend to focus on the same topics that industry focuses on rather than bridging differences. The most prolific institutions are all US- or China-based, but there is very little cross-country collaboration. We discuss implications around (1) how to support the influx of new authors, (2) how industry trends may affect academics, and (3) possible effects of (the lack of) collaboration.
No Vibes Allowed: Solving Hard Problems in Complex Codebases – Dex Horthy, HumanLayer
Bret Victor - The Future of Programming
I think this talk gives lots of food for thought. Are we too entrenched in our ways, to think in other/better ways?
The idea of a "tech industry" has always relied on "computer" being a special kind of medium, akin to alchemy. And if tech demands to be king, AI hype has now elevated LLMs to a king of kings. But the solipsism has hit a dead end. The only way forward is to recognize that computers are not special.
LLMs are just normal technology. But tech is just a normal medium.
productpicnic.beehiiv.com1. We—@eduede.bsky.social, @mjcrockett.bsky.social, Kevin Gross, and I—have a new preprint on the arXiv today, based on ideas that emerged during an @sfiscience.bsky.social workshop in November 2024: The unintended consequences of large language models as a labor-augmenting technology in science.
The unintended consequences of large language models as a labor-augmenting technology in science
arxiv.orgone of the reasons why I'm critical of "bigger models will solve this" is that cranking the output velocity of technician work sharpens the need for process work, which is the domain of expertise. process is incredibly hard to get right. most fields are bad at it. and it's *gaining* importance.
Ed
when it comes to LLMs, I have been trying to beat the technician/expert drum. LLMs are becoming master technicians, but they obviously lack integrative capabilities to deploy expertise. but I'm starting to realize that a lot of people don't understand, or don't value the difference.
