picometer

The state is not going to drown. The polity of urban New Orleans is the liberal thorn in its side, and that's the area at risk.

The code block after "Welcome" is the code sample. Very literate.

I’m a violinist (amateur but play regularly). When I have an important note, which is held for a while and needs vibrato, I frequently decide to shift my left hand position so that my middle finger is responsible, rather than the index finger. It feels stronger, easier to nail the intonation (pitch) with precision, and freer to perform the desired type of vibrato. (String players do vibrato by wiggling the left hand finger, which affects the pitch and overtones / oscillation modes of the string.) In fact, I tend to avoid using the index finger on notes that require vibrato.

That preference might be explained here, by the precision/strength combination. I tried holding a hammer as described in the author’s hammer exercise, and there’s similarity, though it requires much more weight-holding. The left hand doesn’t hold the weight of the violin (consider a cello or a guitar with shoulder strap), but a little grip strength is required to securely hold down the string, especially with vibrato.

Overall, fascinating article. I feel quite motivated to read more on hand anatomy and biomechanics.

Agreed. I recall being taught in college physics labs: there is no such thing as “human error”. Instead, think about the causes and mechanisms of each source of error, which helps both quantifying and mitigating them.

Same energy here. “Be more careful” is extraordinarily hand-wavy for a profession that calls itself engineering.

Count me in as interested!

I’m glad I saw your comment!! I’ve experienced this exact phenomenon when playing Minecraft while listening to an audiobook or podcast. Returning to that area will immediately remind me of the topic or narrative that I heard. Presumably it’s related to the “memory palace” technique, but otherwise, I can’t make heads or tails of it. It’s immediate, as if the location is a hash key mapping to the information. Or as if they’re stored in literally the same place, and fetching one implies fetching the other.

Similarly to you and the article’s author, this doesn’t happen with whatever thoughts I may think while at a location. But in that situation the brain is engaged in generating those thoughts, and not with the task of learning new information. So I don’t find it surprising that it works differently.

I haven’t thought about it in relation to “consciousness” yet. Will have to chew on this article a bit.

The museum was also a generous community space; I remember attending a Seattle Indies game jam and other events before the pandemic. It was very special to be surrounded by reminders of early-computing exploratory spirit.

*If* such a thing could be done under typical corporate incentives/behavior, then I suspect the “high impact” part would need to be scrapped. Because when something is important to a corporation, it turns its eyes that thing (so to speak), which disrupts the other properties.

Or, “high impact” could be spread over the long term. So, unknown-payoff R&D. It would need to be an “invest and ignore” strategy and require a lot of institutional trust.

Question for ML/AI people: is it still common to use the term “overfitting” for these cases, where a model is overtrained on one thing, to the detriment of another? Or is that term only used for literal curve fitting?

I do think it’s an interesting line of inquiry… but not robust enough.

E.g. this paper would be much more interesting if it measured the threshold at which the LLM starts to become good at X, and linked that threshold to the number and character of training examples of X. Then, maybe, we can begin to think about comparing the LLM to a human.

Alas, it requires access to the training data to do that study, and it requires a vast amount of compute to do it robustly.

Yes, but in a major journal like PNAS? (Proceedings of the National Academy of Sciences)

Good point - I saw the FLAN anomaly and this didn’t occur to me!

A good follow up question would be: why didn’t the other models do better on the 2nd-order question? Especially BLOOM and davinci-003, which were middling on the 1st-order question.

I agree on your overall criticism of the experimental protocol, though.