Professionally, AI, science, AI4Science, Safety4AI. Also human ecology and indonesian death metal remixes.
See danmackinlay.name for more words about background and my now page for bonus stuff.
Dan MacKinlay
Karma: 267
I like this idea aesthetically. I foresee some challenges in making “staking” something that won’t trigger alarms in the existing research bureaucracies that host many of our potential authors. If you have clever ideas for how to handle that I would be curious to hear.
This publication bias story in ML is a whole can of worms which I would love to open at some point. tl;dr it is a problem, but the field has semi-accidentally mitigated many of the worse excesses of it. There is an IMO massively under-regarded work on this— Moritz Hardt’s Machine Learning Benchmarks, which I will write a LW review of some day if I have time.
Yes, I’m excited to see what we can learn David’s experience, especially given the incentive designer’s insight that he brings to this. We also, collectively, have some experience with the ILIAD conferences which was a precursor experimenting with alternative compensation mechanisms. See Proceedings of ILIAD: Lessons and Progress for some analysis of that project.
We are trying to do both, in that we are attempting to be a bridge between LW and wider scientific communities. Where do you feel our tone might be excluding domain scientists?
An Alignment Journal: Coming Soon
@megasilverfist there are quite a few of us based in Melbourne. HMU.
We’re not free at the Melbourne AI Safety Hub, but we are all terribly charming.
Tom Everitt did his PhD in Australia too. (As did I, FWIW.)
If contains one true parameter ,
Having trouble parsing this. Does this mean that one element of the parameter vector is “true”?
The deep history of intelligence
“Opponent shaping” as a model for manipulation and cooperation
Interesting! Ingenious choice of “color learning” to solve the problem of plotting the learned representations elegantly.
This puts me in mind of the “disentangled representation learning” literature (review e.g. here). I’ve thought about disentangled learning mostly in terms of the Variational Auto-Encoder and GANs, but I think there is work there that applies to any architecture with a bottleneck, so your bottleneck MLP might find some interesting extensions there,
I wonder: what is the generalisation of your regularisation approach to architectures without a bottleneck? I think you gesture at it when musing on how to generalise to transformers. If the latent/regularised content space needs to “share” with lots of concepts, how do we get “nice mappings” there?
I’m enjoying envisaging this as an alternative explanation for the classic Lizardman’s Constant, which is a smidge larger than 3% but then, in cheap talk markets you have less on the line, so…
Ideally you would wish to calibrate your EV calcs against the benefit of a UAE AISI, though, no, not the expected budget? We could estimate the value of such an institute being more than the running cost (or, indeed, less) depending on the relative leverage of such an institute.
We do not yet plan to support replications of empirical work. Organisationally, there is a desire to keep opening scope tight and theoretical to avoid having diffuse messaging at start up
Personally, I would make a case that replications are not as important in the ML/AI research as in the sciences of the physical (although this depends somewhat on what we mean by “replications”)
That said, I think that there is a strong argument for replications generally, and maybe in this field too, and if the Editorial Board agreed with that, then that is what we would do. I am beholden at this point to mention the connection to the UnJournal work that David has mentioned elsewhere in these comments.