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Pleading has worked for me. “My job depends on this, please help me” and ChatGPT would do a task it previously claimed it wasn’t able to (extract text from an image, it claimed it couldn’t make it out at first)

FWIW I wrote a paper on nutrient-limited growth rates of cells and how that depends on their shape. one of the interesting findings was that elongated cells can grow exponentially quickly (as observed) while spheres quickly max out.

https://arxiv.org/abs/1312.0674

When reading and writing became prevalent, the ancients bemoaned our reduced facility to memorize long texts. Are we now “less smart” because of that technology?

Article says this misses important details, eg data that might be in the image.

FWIW: A few nearby towns that are supposed to have good schools: Castro Valley, Albany, Alameda.

I’ve noticed that people in my field tend to have relatively similar personalities and relatively similar MB categories (close to INTJ). That combo is relatively rare within the broader population. Given this consistency I don’t think we can say it’s pseudoscience in a strong sense.

It’s as if the body of human knowledge is our I’ve mind. It used to be expensive to access that, but no more.

Cool thing is now when someone contributes something to the hive mind, it can instantly be applied to any other problem people are working on.

Tesla self driving works. I don’t know if Karpathy deserves credit for that or not.

Worldwide poverty rate in 1800 = 81%. Today under 10%.

https://cepr.shorthandstories.com/history-poverty/

Agree, and I think it's probably healthy that the large endowments are being taxed a modest amount.

Hmm I also tend to clean house thoroughly only when guests come over. Perhaps an effect that should be applied.

Never saw that AGM expression for K0 before in the earlier linked post. Nor had I heard of the AGM, very cool.

Alexander the Great (died 323 bc) famously kept a copy of the Iliad under his pillow.

If every manager initially had 5 reports, a quick geometric series shows that eliminating all managers would save you 20% of headcount. Of course, managers tend to get paid more, so maybe you'd save a larger fraction of wages.

I wonder if that's the main concern or if communication / coordination costs are the larger concern

I'm a data-scientist now, and a fan of claude code for implementing things. But I have to say, I'm constantly surprised by how "dumb" chatgpt is as a math research partner. I will ask it a math question I'm thinking about, get a confident answer back, only to realize hours to days later that it was 180 degrees backwards. I'm so frustrated right now with this that I'm almost ready to stop asking it such questions at all. I'm aware this seems to contrast strongly with other math-people's enthusiasm e.g., Terrance Tao. Unclear why my mileage varies.

Much of my work takes the form above -- in other words figuring out what to do. once i've decided, it can of course spit out the boilerplate code much faster than I could, and I appreciate that. But for the moment I think I still have some job security thanks to the first issue.

The earlier paper was from 2021!

>> This mind upload thought experiment convinced me…

Start at the color green and gradually add in some blue. As long as there are no sharp discontinuities there is no such thing as the color green.

When I look at traffic in my city, I rarely see it caused by full packing. Rather throughout seems to be the issue.

The tldr for me from the report was Waymo is great and SF is a dangerous place to drive.

I never heard the “Bernoulli” part, new?

FWIW I have heard the exact opposite. The people I know love the self driving feature.

Agree 100% -- wonderful history here. And with such a simple statement, hard to believe / accept the "reason" is that it works for 633 limiting cases.

The privacy concerns are real.

The need / demand for some verification system might be growing though as I’ve heard fraudulent job application (people applying for jobs using fake identities… for whatever reason) is a growing trend.

Here's the wikipedia on that one

https://en.wikipedia.org/wiki/Berkson%27s_paradox

Many times I've looked at the output of a regression model, seen this effect, and then thought my model must be very bad. But then remember the points made elsewhere in thread.

One way to visually check that the fit line has the right slope is to (1) pick some x value, and then (2) ensure that the noise on top of the fit is roughly balanced on either side. I.e., that the result does look like y = prediction(x) + epsilon, with epsilon some symmetric noise.

One other point is that if you try to simulate some data as, say

y = 1.5 * x + random noise

then do a least squares fit, you will recover the 1.5 slope, and still it may look visually off to you.

I would certainly buy a copy were it printed!

Looks like a treasure trove of knowledge.

I just recently went to the exploratorium in SF and saw an exhibit there suggesting that the catenary made a good arch, so browsed that chapter and saw a bit of explanation here which helped. Was also interested to see that Jefferson played some part in the history here.

I agree, and would add that there are others who are decidedly "anti-car" and you could say that this is part of their identity. This particular policy may be a strictly positive (no strong opinion here), but when viewed as part of the broader disagreement it drives some of the reflexive pushback.

Are you suggesting use the clip embedding for the text as a feature to train a standard Ml model on?

“ I get why people—especially people outside of transportation planning—assume the solution to highway traffic is just to build more buses and trains. But I don’t see much evidence suggesting that big investments in public transit reduce traffic congestion. Even in Philadelphia, our region’s transit doesn’t work well for most suburb-to-suburb trips.”