David Scott Krueger

I'm more active on Twitter than LW/AF these days: https://twitter.com/DavidSKrueger

Bio from https://www.davidscottkrueger.com/:
I am an Assistant Professor at the University of Cambridge and a member of Cambridge's Computational and Biological Learning lab (CBL). My research group focuses on Deep Learning, AI Alignment, and AI safety. I’m broadly interested in work (including in areas outside of Machine Learning, e.g. AI governance) that could reduce the risk of human extinction (“x-risk”) resulting from out-of-control AI systems. Particular interests include:

  • Reward modeling and reward gaming
  • Aligning foundation models
  • Understanding learning and generalization in deep learning and foundation models, especially via “empirical theory” approaches
  • Preventing the development and deployment of socially harmful AI systems
  • Elaborating and evaluating speculative concerns about more advanced future AI systems

Wiki Contributions


I'm not necessarily saying people are subconsciously trying to create a moat.  

I'm saying they are acting in a way that creates a moat, and that enables them to avoid competition, and that more competition would create more motivation for them to write things up for academic audiences (or even just write more clearly for non-academic audiences).

Q: "Why is that not enough?"
A: Because they are not being funded to produce the right kinds of outputs.

My point is not specific to machine learning. I'm not as familiar with other academic communities, but I think most of the time it would probably be worth engaging with them if there is somewhere where your work could fit.

My point (see footnote) is that motivations are complex.  I do not believe "the real motivations" is a very useful concept here.  

The question becomes why "don't they judge those costs to be worth it"?  Is there motivated reasoning involved?  Almost certainly yes; there always is.

  1. A lot of work just isn't made publicly available
  2. When it is, it's often in the form of ~100 page google docs
  3. Academics have a number of good reasons to ignore things that don't meet academic standards or rigor and presentation

Yeah this was super unclear to me; I think it's worth updating the OP.

FYI: my understanding is that "data poisoning" refers to deliberately the training data of somebody else's model which I understand is not what you are describing.

Load More