Curious if the authors are hanging around here and how they feel about the recent Leiden Statement. (Late in the article it’s clear that this is at least partially a publicity effort around a new AI math tool, if I’m reading it correctly.)
On a personal level I’m very excited about AI getting good at math, but I’m a consumer of math, not a creator. My job gets easier as AI gets better on this front, so I can’t fully empathize with mathematicians who feel threatened or are worried about the sanctity of the discipline.
Your comment prompted to look into this further, thank you. The funding situation in California is complicated and weird, but yes, CA does spend a lot of money on public education.
There are weird relics of the underfunded past, but you can’t blame the educational failures on lack of budget.
Well I live in a basic aid district; Bay Area but firmly middle class. We’re just above the cutoff for federally subsidized school lunches iirc, and the schools are chronically short on money.
Part of it is declining enrollment, part of it is Baumol’s cost disease (a living wage is pretty high here! Teachers get paid well on a national scale and very poorly on a local scale).
But yeah… education is simply not well-run in California. I find that pretty indisputable.
This is interesting news to me, thank you. I'm curious if the per-pupil spending includes locally raised funds; the reality in California is that the state level funding is poor, and districts that are above some threshold don't get enough funding to operate. So public schools function as local charities and inevitably have fundraising arms to make up the shortfall. This has been true since at least the 1980s when I was in school, and is definitely still true today.
The root cause of the collapse in math education in California is one bad researcher's work, combined with politics.
Briefly, a Stanford-affiliated "researcher" named Jo Boaler produced two deeply underpowered studies claiming to show that putting all students in the same grade-level math course led to better outcomes for everyone — even the kids that would've normally been tracked into advanced math. But she only tested results on grade-level math — of course the would-be advanced kids did better on "grade level" math if they've taken it recently. The loss is the advanced math they didn't take.
I fought with my son's middle school administration about this precise issue. It is the stated policy of CA's state level education department to de-emphasize advanced math and tracking, in favor of these deeply suspect ideas. I'm pretty progressive in general, but this is braindead stupid, alarming, and self-defeating. (If you care about equity, you NEED to have options in the public school for the underprivileged gifted kids! the rich kids have lots of options and will be fine.)
It's deeply depressing, but education has long been a weak spot for California; since Prop 13 in the 1970s, California has been 49th or 50th in per-pupil funding for public education (excluding college, I think). But to compound that with this wrongheaded, moronic, politically suspect and quantitatively incorrect policy is... infuriating.
This already happens — my district when I was in school, and my son's district now, both have / had "alternative" high schools that kids get transferred to when they're struggling. Kids who are dropping out inevitably get transferred as part of the process; the high school they were originally attending has stellar graduation rates. The alternative high school has miserable graduation rates, but no one really cares.
In his interview on the Hard Fork podcast, Palo Alto Networks’ CEO described the capability change from Opus to Mythos being more about availability; evidently it runs in a very compute-intensive, always-on mode. Unclear if the base model is significantly different, but Arora ascribed the difference mostly to that change.
In case anyone is wondering why this is important: the spherical harmonics are frequently most of the data in a Gaussian splat data set — as much as 80% of the data for good quality scenes.
The magic of Gaussian splats is their ability to render photorealistic outputs without material properties, explicit ray tracing, etc. They do this by synthesizing complex light transfer in the scene via these fuzzy blobs overlapping — but they need to be able to change color and transparency with view angle to recreate much of that light transport. Hence, relatively heavyweight data.
There are many approaches to reducing the data volume, and they get increasingly complex when you add a time component. Not even worth listing publications here because it's changing so quickly, just plan to look at the SIGGRAPH pre-prints in a couple of months. Exciting times!
It's worth signing up for Levine's free newsletter and listening to the podcast (though there is substantial overlap in the content, it's still fun to hear what you've already read).
His take on this, crypto, all things Elon Musk, and the current 'predictions market' are funny and insightful.
Because it acquires an asset worth roughly that much, it’s neutral. GME is (probably!) not doing a huge at-the-market offering, they’re creating the shares and immediately giving them to eBay shareholders.
In practice the price paid for the company being acquired is usually a bit higher than the market value (so the shareholders take the deal), and the market usually punishes the acquirer a bit and the resulting entity’s stock will fall a bit. (This is most definitely not investing advice.)
This is not atypical; smaller company “buys” the larger company with debt on the larger company’s books. The blended shareholder mix is mostly the larger company; management comes from the smaller company.
The one I was most familiar with was the Discovery “acquisition” of Warner Brothers. Though apparently that’s a little complicated because AT&T was divesting itself of Warner.
To be fair, that’s the recommended way to put out an alkali metal fire. At least according to my grandfather who helped write safety regulations for nuclear subs whose reactors were cooled by liquid sodium.
Not really something I’d want to try out in practice, seems like a fire in a nuclear reactor under the ocean, where the source of the fire explodes on contact with water, is a less-than-ideal situation.
Missed in the previous discussion: methanol is irrelevant. Grain based ferments have essentially zero methanol.(And methanol risk is a function of its concentration relative to ethanol — the treatment for methanol poisoning is… ethanol!) even fruit based fermentations with significantly higher pectin concentrations only produce trace methanol, and it’s not all that well concentrated in a distillation due to azeotropes (which also says that throwing out the heads doesn’t help that much).
Methanol poisoning stories in the news almost exclusively result from people trying to sell denatured or industrial alcohol. The biggest risk in home distilling is fire.
This is a good, succinct unpacking of the metaphysical stakes. Nonetheless I am curious for the world where striking a match results in a shower of seagulls.
The article talks about how they are controlling for the variation across time, and they’re reporting a new signal. So even if everyone was working Saturdays before, everyone is more working Saturdays now. (Edited typos.)
That's an awfully crude test, that derives its power from its appeal to emotion.
Which is not to say it's prima facie wrong, but to point out that its correctness should be separated from its emotional impact, which is very tough for most humans to do.
On a personal level I’m very excited about AI getting good at math, but I’m a consumer of math, not a creator. My job gets easier as AI gets better on this front, so I can’t fully empathize with mathematicians who feel threatened or are worried about the sanctity of the discipline.