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What is the difference between reasoning and predicting what reasoning would look like?

One larger problem here is the value of a research paper is rarely the specific knowledge it adds but in the process of researching that adds to the collective knowledge+experience of those involved, especially training graduate students. AI papers shortcut this entirely. Academia has a lot to answer for this too by making papers the currency of success. AI generated papers are almost shortcut learning at a full system level.

Becoming a dad simultaneously made me more empathetic (seeing a little person from the beginning for all they are) but also more impatient (fewer hours in the day), but beyond that not much. Given the notoriety about some of the techniques referenced in this article [0] curious if others notice anything more consistent.

0: https://academic.oup.com/brain/article/140/8/e53/4032512

For R+RStudio users, any opinions on the switch? Assistant seems to be the big departure, trying to work out if worth losing keyboard shortcut muscle memory for

Beside the point but I really love the rainbow sparkles trailing the cursor on the netscape theme of this blog. Takes me back to a time when the internet was...fun

Someone has pointed out on X/Twitter that the "novel discovery" made by the AI system already has an entire review article written about the subject [0]

[0] https://x.com/wildtypehuman/status/1924858077326528991

While this is an excellently written piece and really insightful into the state of higher education funding, what seems to be missing from the debate is concrete ideas of what should be done differently (either in 2014 or today). A lot of US innovation success comes from deep pockets of private venture capital, which is just missing in the UK. So if you're a politician/bureaucrat with a (let's face it) relatively small budget and much politics to deal with, the best strategy to take is not obvious (at least to me).

It seems the take home is weight decay induces sparsity which helps learn the "true" representation rather than an overfit one. It's interesting the human brain has a comparable mechanism prevalent in development [1]. I would love to know from someone in the field if this was the inspiration for weight decay (or presumably just the more equivalent nn pruning [2]).

[1] https://en.wikipedia.org/wiki/Synaptic_pruning [2] https://en.wikipedia.org/wiki/Pruning_(artificial_neural_net...

Interestingly, on Monday a preprint [1] was posted calling into question a major Nature paper from 2020 that associated the microbiome with multiple cancer types [2] (though within each tissue sample, not the gut).

[1] https://www.biorxiv.org/content/10.1101/2023.07.28.550993v1 [2] https://www.nature.com/articles/s41586-020-2095-1

Does anyone have high level guidance on when (deep) RL is worth pursuing for optimization (e.g. optimizing algorithm design) rather than other approaches (e.g genetic)?

The divide between literature/arts and STEM feels related to a push in modern culture that every moment must be productive (or in some sense profitable), though I have a hard time unpicking whether things really did used to be “better” or this is just getting older and realizing how the world works.

Anecdotal, but as a pre-smartphone teenager I was a night owl which has gradually vanished despite the introduction of smartphones, so I think there's more at play

Likely going to be a wave of research/innovation "regularizing" LLM output to conform to some semblance of reality or at least existing knowledge (e.g. knowledge graph). Interesting to see how this can be done quickly enough...

Would love to know why Norway has so many lighthouses / "blinking beacons". Wikipedia lists many [0], but not as many as shown.

[0] https://en.wikipedia.org/wiki/List_of_lighthouses_in_Norway